discriminating and mapping soil variability with ... · the university of adelaide discriminating...

THE UNIVERSITY OF ADELAIDE

Discriminating and mapping soil variability with hyperspectral reflectance data

David Summers

B. Ag. Sci. (Hons), The University of Adelaide

B.A. Flinders University of South Australia

Thesis presented for the degree of Doctorate of Philosophy

Faculty of Sciences, School of Earth and Environmental Sciences

July 2009

i

Abstract

The classification and mapping of soils and soil variability is important for a variety of

environmental and agricultural applications. Advances in precision agriculture, better

understanding of environmental processes and improvements in mathematical models used

to predict and understand landscape phenomena all require detailed information about soils

at increasingly finer scales. The goal of this thesis was to address this need for fine scale

soil information by developing new mapping methodologies from hyperspectral remote

sensing and reflectance spectroscopy. The spatially continuous and rich spectral

information of hyperspectral data provides a powerful diagnostic tool for mapping and

monitoring the earth’s surface materials. Similarly, reflectance spectroscopy allows for

rapid and cost effective measurement of materials based on their spectral response. These

two technologies offer the potential to record information about soils and provide fine

scale or continuous surface information for natural resource management.

The research aimed to explore the extent to which variation in surface horizon soils could

be discriminated and mapped with hyperspectral reflectance data. The study examined the

prediction of soil properties and classes with spectroscopic measurements, the mapping of

surface soils through interpolation from sample sites and the analysis of hyperspectral

imagery. The influence of vegetative cover and soil type on the identification of soil class

and quantification of soil exposure was investigated using simulated imagery. Each of the

research components focused on the soil properties and range of variation typically

encountered in southern Australian agricultural regions.

Reflectance spectroscopy was used to discriminate select field soil survey classes and to

predict and quantify various laboratory derived soil properties. For both of these analyses

visible near-infrared reflectance spectra (350 – 2500 nm) were collected with an ASD

FieldSpec Pro using a hand held probe. The spectral separability of the commonly used

field survey classes texture, carbonate and Munsell colour (separated into hue, value and

chroma) was assessed using penalised discriminant analysis. Only Munsell chroma was

adequately discriminated; while other classes showed some separability, it was limited and

not sufficient for soil classification. Failure to adequately classify the soil property classes

Abstract ii

was attributed to the subjective nature of the field survey methods, as well as co-variance

between soil properties.

Quantitative prediction of laboratory-measured soil properties (clay, organic carbon, iron

oxide and carbonate) from reflectance spectra was conducted using partial least squares

regression. Clay and carbonate contents were the best predicted, although predictions of

iron oxide and organic carbon were also acceptable. The utility of reflectance spectroscopy

to provide inputs for soil mapping was assessed by comparison of kriged surfaces of soil

properties. This comparison indicated that the methodology captured the same variability

in the landscape over the same range in values for each of the soil properties.

Prediction of soil exposure and type through vegetation cover was assessed with two types

of simulated imagery which were created using spectra of soil, photosynthetic and non-

photosynthetic vegetation. Both simulated images had the same, known combinations of

soil and vegetation but the relative mixes were created differently. Soil and vegetation

cover fractions were retrieved from the images through linear spectral unmixing and

compared with the measured fractions. Soils were accurately identified and classified in

both image types. However, not all soil spectra were isolated from mixed pixels equally or

successfully to provide accurate abundance fractions: some spectral mixes of soil and

vegetation were incorrectly classified as different soils, highlighting potential sources of

error in unmixing procedures.

The mapping of surface soils was assessed using image derived soil endmembers and

HyMap hyperspectral image data. Endmembers were isolated from the imagery using a

pixel purity process before being used in a partial unmixing routine. Field estimates of soil

exposure and laboratory analysis of soil samples were correlated with unmixing

abundances and used to characterise areas mapped by the different soil endmembers. Only

a moderate correlation between the field and image derived soil exposure was found.

Furthermore, soil properties for the different endmembers showed little difference between

classes and the mean of all samples. However, more than 70% of the areas mapped by the

four endmembers were unique, indicating that they were spatially distinct. These results

imply that the spectral response of soils captured by the hyperspectral imagery is more

strongly influenced by land management and soil properties other than those determined

through laboratory analysis.

Abstract iii

Reflectance spectroscopy of surface samples offers the potential to quickly and reliably

predict soil properties. Results indicate that it can be applied successfully to local

geographic areas and interpolated with geostatistics to create maps. The mapping of soils

with hyperspectral data presents problems that stem both from issues of plant material

obscuring the soil surface and high variability in soil reflectance due to management and

landscape processes. The unmixing of soils and vegetation (photosynthetic and non-

photosynthetic) from simulated imagery was successful but showed the potential for mixed

pixels to be confused for non-target soils. Similarly, landscape and management process

are subject to high variability and are not necessarily related to soil properties relevant to

agricultural and environmental applications. To fully utilise remote sensing for mapping

soils in a natural environment further research is required.

iv

Declaration

This work contains no material which has been accepted for the award of any other degree

or diploma in any university or other tertiary institution to David Summers and, to the best

of my knowledge and belief, contains no material previously published or written by

another person except where due reference has been made in the text.

I give consent to this copy of my thesis when deposited in the University Library, being

made available for loan and photocopying, subject to the provisions of the Copyright Act

1968.

The author acknowledges that copyright of published works contained within this thesis (as

listed below) resides with the copyright holders(s) of those works.

I also give permission for the digital version of my thesis to be made available on the web,

via the University’s digital research repository, the Library catalogue, the Australasian

Digital Theses Program (ADTP) and also through web search engines unless permission

has been granted by the University to restrict access for a period of time.

David Summers

July 2009

v

Publications arising from this thesis

Refereed publications

Summers, D., Lewis, M., Ostendorf, B. and Chittleborough, D. J. 2005 Spectral discrimination of soil properties, In SSC 2005 Spatial Intelligence, Innovation and Praxis:

The National Biennial Conference of the Spatial Sciences Institute, Melbourne, Australia, September, 2005.

Summers, D., Lewis, M., Ostendorf, B. and Chittleborough, D. J. 2009 Mapping soil variability with hyperspectral image data, In SSC 2009 Spatial diversity: The Biennial

International Conference of the Surveying and Spatial Sciences Institute, Adelaide, Australia, September-October, 2009.

Summers, D., Lewis, M., Ostendorf, B. and Chittleborough, D. J. (In Press) Visible near-infrared reflectance spectroscopy as a predictive indicator of soil properties, Ecological

Indicators.

Summers, D., Lewis, M., Ostendorf, B. and Chittleborough, D. J. (In Review) Unmixing of soil types and estimation of soil exposure with simulated hyperspectral imagery, International Journal of Remote Sensing.

Non-refereed publications

Summers, D., Lewis, M., Ostendorf, B. and Chittleborough, D. J. 2006 Spectral determination of soil properties under vegetation, In 13th Australasian Remote Sensing and

Photogrammetry Conference, Canberra, Australia, 18-22 October.

Summers, D., Lewis, M., Ostendorf, B. and Chittleborough, D. J. 2007 Identification of soil properties under vegetation using hyperspectral imagery, In EcoSummit 2007, Beijing, China, 22-27 May 2007.

Publications vi

Proportion of contribution by author

This is a declaration of the extent of each author’s contributions to the refereed papers

arising from this thesis. The extent of each of author’s contribution is quantified for

conceptualisation, realisation and documentation. Each author gives permission for the

paper containing their contribution to be included in this thesis.

Percent contribution and permission to include paper in this thesis:

Summers, D., Lewis, M., Ostendorf, B. and Chittleborough, D. J. 2005 Spectral discrimination of soil properties, In SSC 2005 Spatial Intelligence, Innovation and Praxis:

The National Biennial Conference of the Spatial Sciences Institute, Melbourne, Australia, September, 2005.

Conceptualisation Realisation Documentation Signature

Summers, D. 80% 90% 85% ______________

Lewis, M. 10% 5% 10% ______________

Ostendorf, B. 5% 2.5% 2.5% ______________

Chittleborough, D. 5% 2.5% 2.5% ______________

Summers, D., Lewis, M., Ostendorf, B. and Chittleborough, D. J. 2009 Mapping soil variability with hyperspectral image data, In SSC 2009 Spatial diversity: The Biennial

International Conference of the Surveying and Spatial Sciences Institute, Adelaide, Australia, September-October, 2009.


Summers, D. 80% 90% 85% ______________

Lewis, M. 10% 5% 10% ______________

Ostendorf, B. 5% 2.5% 2.5% ______________


Publications vii

Summers, D., Lewis, M., Ostendorf, B. and Chittleborough, D. J. (In Press) Visible near-infrared reflectance spectroscopy as a predictive indicator of soil properties, Ecological

Indicators.


Summers, D. 80% 90% 85% ______________

Lewis, M. 10% 5% 10% ______________

Ostendorf, B. 5% 2.5% 2.5% ______________




Summers, D. 80% 90% 85% ______________

Lewis, M. 10% 5% 10% ______________

Ostendorf, B. 5% 2.5% 2.5% ______________


viii

Acknowledgements

I need to thank all of my family and friends for their help and patience over the years.

Thanks to my parents John Summers and Deborah McCulloch for all of their support,

encouragement and advice. And thanks to Andi Sebastian, similarly for support,

encouragement and advice but also her general enthusiasm for everything. Thanks also to

Ella Sebastian for always being my little sister.

Friends like Kerry Ireland, Mathew Rice, Simon Krieg, Jo-Anne Krieg, Chris Iley, Simone

Iley, Amanda Whitford, Jacob Habner, Amy Roberts, Matthew Slade, Claire Sherman and

Steve Safralidis, were all invaluable in one way or another to getting through.

I would also like to thank Kirsty Baldock who is my wonderful partner in all things and

has supported me in this journey with encouragement, patience and good humour.

Thanks go to all of my supervisors Megan Lewis, Bertram Ostendorf, David

Chittleborough and David Maschmedt who provided advice and direction in what was

sometimes a tortuous path. Special thanks go to Megan, who was always available for

advice and prompt with responses, providing intelligent and insightful feedback, and able

to see the whole picture and the detail with seeming ease.

The students, researches and professional staff of the Spatial Information Group and Soil

and Land Systems who over the years have provided advice, assistance, friendship,

humour and distraction. In no particular order they are; Ramesh Raja Segaran, Greg Lyle,

Neville Crossman, Kenneth Clarke, Patrick O’Connor, Tonja Wright, Paul Bierman,

Melissa Fraser, Kate Langdon, Sjaan Davey, Mohsen Forouzangohar, Sean Mahoney,

Dorothy Turner, David Mitchell, Claire Trelibs, Allana Grech, Reza Jafari, David Gerner,

David Hart, Rowena Morris, Serhiy Marchuk, Anna Dutkiewicz, Victoria Marshall,

Davina White, Adam Kilpatrick, Troy Willats, Tom Ellis, Collin Rivers, Debbie Miller,

Cameron Grant, Ron Smernik. Extra special thanks goes to those who joined me for

morning tea nearly everyday and the occasional game of hacky sack.

I would like to thank Sean Mahoney, Anna Dutkiewicz and Amanda Whitford for their

help in the field and with collecting and recording samples. Kerry Ireland and Amanda

Whitford also provided invaluable help in the laboratory taking spectroscopic

measurements and being generally very good friends.

Acknowledgements ix

Thanks to the people who provided technical advice in all its many forms; Debbie Miller,

Colin Rivers and Alla Marchuk from the University of Adelaide; and Richard Merry and

Les Janik, formally of CSIRO but now just hanging around and generally knowledgeable.

This research was conducted with joint funding from the Cooperative Research Centre for

Future Farm Industries (CRC FFI) and The University of Adelaide. Funding from the CRC

FFI was part the project ‘Development and application of high resolution spatial diagnostic

tools to aid in deployment of perennial systems at a catchment scale’. Funding from The

University of Adelaide was as part of a Faculty of Sciences Divisional Scholarship. Thanks

to the Cooperative Research Centre for Future Farm Industries and to The University of

Adelaide for their financial support and training and the excellent community they created.

Special thanks to Daryll Richardson for all his help in many forms, and to all of the

students of the CRC who provided friendship and advice over the years.

x

Table of Contents

Abstract ............................................................................................................................ i

Declaration ..................................................................................................................... iv

Publications arising from this thesis ............................................................................... v

Acknowledgements .......................................................................................................viii

Table of Contents ............................................................................................................ x

List of Figures............................................................................................................... xvi

List of Tables ................................................................................................................ xix

Chapter 1 ......................................................................................................................... 1

Understanding Soil Variability ....................................................................................... 1

1.1 Introduction ....................................................................................................... 1

1.2 Scope ................................................................................................................. 4

1.3 Thesis Structure ................................................................................................. 6

1.4 References ......................................................................................................... 7

Chapter 2 ......................................................................................................................... 9

Identifying and Evaluating Remote Sensing Techniques and Methodologies for

Mapping Soils .................................................................................................................. 9

1.1 Introduction ....................................................................................................... 9

1.2 Scope of Review ................................................................................................ 9

1.3 Soil Formation and Mapping............................................................................ 10

1.3.1 Soil Formation ......................................................................................... 10

1.3.2 Soil Mapping in Australia......................................................................... 10

Table of contents xi

1.3.3 Traditional Soil Mapping Methodology.................................................... 11

1.4 Improving Soil Mapping .................................................................................. 12

1.4.1 Pedotransfer functions.............................................................................. 12

1.4.2 Geostatistical analysis .............................................................................. 13

1.4.3 Continuous Classification......................................................................... 14

1.4.4 Digital Elevation Models and Topographic Indices................................... 14

1.5 Remote Sensing and Reflectance Spectroscopy................................................ 16

1.5.1 Spectral Characteristics of Soils ............................................................... 17

1.5.2 Soil Reflectance Spectra........................................................................... 18

1.5.3 Limitations of Optical Remote Sensing for Soil Mapping......................... 26

1.5.4 Vegetation Discrimination and Mapping .................................................. 26

1.6 Summary ......................................................................................................... 28

1.7 References ....................................................................................................... 29

Chapter 3 ....................................................................................................................... 34

Spectral Discrimination of Soil Properties ................................................................... 34

3.1 Introduction ..................................................................................................... 34

3.1.1 Spectral Variation in Soils ........................................................................ 35

3.2 Methods........................................................................................................... 36

3.2.1 Sample collection ..................................................................................... 36

3.2.2 Physical sample analysis .......................................................................... 36

3.2.3 Reflectance spectra collection................................................................... 37

Table of contents xii

3.2.4 Statistical analysis .................................................................................... 38

3.3 Results and Discussion..................................................................................... 39

3.4 Conclusion....................................................................................................... 47

3.5 References ....................................................................................................... 47

Chapter 4 ....................................................................................................................... 49

Visible near-infrared reflectance spectroscopy as a predictive indicator of soil

properties....................................................................................................................... 49

4.1 Introduction ..................................................................................................... 49

4.1.1 Spectral Reflectance Variation in Soils..................................................... 51

4.2 Methods........................................................................................................... 53

4.2.1 Study site and sample collection............................................................... 53

4.2.2 Laboratory soil analysis............................................................................ 55

4.2.3 Reflectance spectra................................................................................... 55

4.2.4 Statistical analysis .................................................................................... 55

4.2.5 Spatial Prediction ..................................................................................... 57

4.3 Results and Discussion..................................................................................... 57

4.3.1 Soil Properties.......................................................................................... 57

4.3.2 Soil Spectral Characteristics ..................................................................... 58

4.3.3 Prediction of Soil Properties ..................................................................... 60

4.3.4 Mapping of Predicted Soil Properties ....................................................... 64

4.4 Conclusion....................................................................................................... 65

4.5 References ....................................................................................................... 67

Table of contents xiii

Chapter 5 ....................................................................................................................... 70

Unmixing of soil types and estimation of soil exposure with simulated hyperspectral

imagery .......................................................................................................................... 70

5.1 Introduction ..................................................................................................... 70

5.2 Materials and methods ..................................................................................... 73

5.2.1 Soil and vegetation samples...................................................................... 73

5.2.2 Collection of spectra and image creation .................................................. 74

5.2.3 Spectral unmixing .................................................................................... 77

5.3 Results ............................................................................................................. 78

5.3.1 Spectral characteristics ............................................................................. 78

5.3.2 Mixes of spectra ....................................................................................... 80

5.3.3 Unmixing ................................................................................................. 82

5.4 Discussion ....................................................................................................... 87

5.4.1 Unmixing ................................................................................................. 87

5.4.2 Discrimination of soils ............................................................................. 87

5.4.3 Discrimination of soil and vegetation ....................................................... 88

5.4.4 Unmixing errors ....................................................................................... 89

5.4.5 Virtual versus laboratory images .............................................................. 90

5.5 Conclusions ..................................................................................................... 91

5.6 References ....................................................................................................... 92

Table of contents xiv

Chapter 6 ....................................................................................................................... 95

Mapping soil variability with hyperspectral image data ............................................. 95

6.1 Introduction ..................................................................................................... 95

6.2 Methodology.................................................................................................... 97

6.2.1 Study site characterisation and sample collection...................................... 97

6.2.2 Laboratory soil analysis............................................................................ 98

6.2.3 Image acquisition and pre-processing ....................................................... 98

6.2.4 Endmember selection and partial unmixing ............................................ 100

6.2.5 Validation .............................................................................................. 100

6.3 Results and Discussion................................................................................... 101

6.3.1 Endmembers .......................................................................................... 101

6.3.2 Soil mapping .......................................................................................... 103

6.3.3 Validation .............................................................................................. 104

6.4 Conclusion..................................................................................................... 109

6.5 References ..................................................................................................... 110

Chapter 7 ..................................................................................................................... 113

Discussion and Conclusion.......................................................................................... 113

7.1 Introduction ................................................................................................... 113

7.2 Summary of specific contributions to knowledge ........................................... 114

7.2.1 Spectral discrimination of soil properties (Chapter 3) ............................. 114

7.2.2 Visible near-infrared reflectance spectroscopy as a predictive indicator of

soil properties (Chapter 4)...................................................................................... 115

Table of contents xv

7.2.3 Unmixing of soil types and estimation of soil exposure with simulated

hyperspectral imagery (Chapter 5) ......................................................................... 116

7.2.4 Mapping soil variability with hyperspectral image data (Chapter 6)........ 117

7.2.5 Overall assessment of thesis topic........................................................... 117

7.3 General discussion: wider significance and limitations................................... 118

7.3.1 Spectral discrimination of soil properties (Chapter 3) ............................. 118

7.3.2 Visible near-infrared reflectance spectroscopy as a predictive indicator of

soil properties (Chapter 4)...................................................................................... 118


hyperspectral imagery (Chapter 5) ......................................................................... 119

7.3.4 Mapping soil variability with hyperspectral image data (Chapter 6)........ 119

7.4 Recommendations for future research ............................................................ 120

7.5 Conclusion..................................................................................................... 121

7.6 References ..................................................................................................... 121

xvi

List of Figures

Figure 2.1: Representative reflectance spectra of soils collected in the U.S.A. and Brazil.

Curves a-e explained in text below (Stoner and Baumgardner 1981)................................ 18

Figure 2.2: Example of reflectance spectra Kaolinite minerals showing absorption bands (~

2200 µm) characteristic of clay minerals (Clark 1999)..................................................... 23

Figure 2.3: Showing spectral features of hematite, green grass and dry grass (Fraser 1991).

........................................................................................................................................ 25

Figure 3.1: Jamestown study site, 200km north of Adelaide............................................. 37

Figure 3.2: Mean reflectance spectra for soil properties measured: field texture, soil

carbonate and soil colour; hue, value and chroma ............................................................ 40

Figure 3.3: Plots of spectral discrimination of field texture and field soil carbonate

measurements showing first and second discriminant variables ....................................... 43

Figure 3.4: Plots of spectral discrimination of components of Munsell soil colour; hue,

value and chroma, showing first and second discriminant variables ................................. 44

Figure 3.5: Discriminant loading plots for field texture, soil carbonate, hue, value and

chroma indicate regions of the spectra most significant in the discrimination analysis. V1

and V2 indicate the first and second discriminant variable respectively. .......................... 46

Figure 4.1: Jamestown study site, 200 km north of Adelaide, South Australia. Polygons

show Common Soils from the Land and Soil Spatial Data for southern South Australia

(Soil and Land Program 2007), soil sample sites marked with black dots. The legend

describes the soil Order from the Australia Soil Classification (in bold) (Isbell 2002) as

well as the soil description from the Land and Soil Spatial Data for southern South

Australia.......................................................................................................................... 54

Figure 4.2: Mean spectra of quartiles for percent clay...................................................... 58

Figure 4.3: Mean spectra of quartiles for soil organic carbon. .......................................... 59

Figure 4.4: Mean spectra of quartiles for carbonate concentration.................................... 60

List of figures xvii

Figure 4.5: Mean spectra of quartiles for iron oxide content............................................. 60

Figure 4.6: Spectral loading weight graph for the prediction of clay content. ................... 62

Figure 4.7: Spectral loading weight graph for the prediction of soil organic carbon content.

........................................................................................................................................ 62

Figure 4.8: Spectral loading weight graph for the prediction of carbonate content............ 63

Figure 4.9: Spectral loading weight graph for the prediction of iron oxide content. .......... 63

Figure 4.10: Spatial distribution of measured and predicted soil properties following

Kriging............................................................................................................................ 64

Figure 5.1: (a) Demonstrates the configuration of the ASD high intensity reflectance probe

held in a clamp over the tray containing soil and leaves. (b) Demonstrates the incremental

movement of probe field of view over plant and soil interface. The solid lines indicate soil

where pure soil and vegetation spectra were collected. The dashed lines indicate the 10%

increments as the probe was moved. Not to scale............................................................. 76

Figure 5.2: The ‘laboratory image’ created from the measured spectra. Soil type is listed at

the bottom, vegetation cover type at the top and percent soil exposure on the left. ........... 77

Figure 5.3: Pure soil spectra (endmembers) from soils used in this experiment. ............... 79

Figure 5.4: Pure vegetation spectra (endmembers) from soils used in this experiment...... 80

Figure 5.5: Spectra collected from actual mixes of Sodic Clay and photosynthetic

Eucalyptus vegetation. Mixes of 0%, 20%, 40%, 60%, 80% and 100% soil are shown for

clarity. ............................................................................................................................. 81

Figure 5.6: Spectra collected from actual mixes of Sodic Clay and the non-photosynthetic

field pea. Mixes of 0%, 20%, 40%, 60%, 80% and 100% soil are shown for clarity......... 81

Figure 5.7: RMSE from ‘virtual’ (a: Eucalyptus, b: orange, c: pea straw) and ‘laboratory’

(d: Eucalyptus, e: orange, f: pea straw) images for each of the vegetation cover types. .... 82

List of figures xviii

Figure 5.8: Unmixing with the Sodic Clay endmember of the ‘virtual’ (a: Eucalyptus, b:

orange, c: pea straw) and ‘laboratory’ (d: Eucalyptus, e: orange, f: pea straw) images for

each of the vegetation cover types. .................................................................................. 83

Figure 5.9: Unmixing with the Silty Loam endmember of the ‘virtual’ (a: Eucalyptus, b:



Figure 5.10: Unmixing with the Loam endmember of the ‘virtual’ (a: Eucalyptus, b:



Figure 5.11: Unmixing with the Clay Loam endmember of the ‘virtual’ (a: Eucalyptus, b:



Figure 6.1: HyMap image strip in true colour (bands 660.4, 557.9 and 468.9 nm)

superimposed on Landsat 7 panchromatic band of Jamestown-Belalie district. Ranges are

marked with arrows. ........................................................................................................ 99

Figure 6.2: Soil endmembers (EM 1, EM 2, EM 3 and EM 4) used in the Matched Filtering

analysis. Actual reflectance spectra are on the left and continuum removed spectra are on

the right......................................................................................................................... 102

Figure 6.3: Partial unmixing results of the four soil endmembers (EM 1, EM 2, EM 3 and

EM 4) isolated from the image. Bright areas indicate a high match with endmembers and

dark areas indicate a poor match.................................................................................... 104

Figure 6.4: Soil maps produced by the application of thresholds to partial unmixing

outputs. ......................................................................................................................... 105

xix

List of Tables

Table 3.1: Error matrices for five discriminant analysis made in this study. Texture: SCL =

Sandy Clay Loam, CL = Clay Loam, LMC = Light Medium Clay, MC = Medium Clay.

CO3: N = Nil, S = Slight, M = Moderate, H = High. ........................................................ 42

Table 3.2: First (V1), second (V2) and third (V3) variates from the analysis and attribution

error derived from the error matrices. .............................................................................. 45

Table 4.1: Summary of laboratory results from chemical and physical analysis. .............. 57

Table 4.2: Sample numbers, residual predictive deviation (RPD), root mean square error

(RMSE) and R2 results for data sets................................................................................. 61

Table 5.1: Laboratory measured soil properties of four soils used in the study. ................ 74

Table 6.1: Coefficient of determination (r2) for the relationship between field estimated soil

exposure and image derived soil exposure for each endmember..................................... 106

Table 6.2: Average soil laboratory results for the total soil samples and the sites

corresponding to each soil endmember. ......................................................................... 107

1

Chapter 1

Understanding Soil Variability

1.1 Introduction

In recent decades there has been a greater awareness of the need to better understand soil

variability. The impact of land degradation and the falling price of many agricultural

commodities have placed increasing pressure on policy makers and land managers to

improve management around Australia and the world (John et al. 2005, Kingwell and

Pannell 2005). Increasingly producers are attempting to improve productivity to maintain

profit margins while arable land becomes degraded from processes such as erosion, salinity

and acidity (Passioura 2002, John et al. 2005, Rengasamy 2006).

In order to remain profitable, farmers are turning to new technologies, such as precision

agriculture, to more efficiently manage assets and allocate resources (Passioura 2002). The

aim of precision agriculture is to refine management decision making through improved

understanding of spatial variables such as yield and soil properties (Bongiovanni and

Lowenberg-Deboer 2004, McBratney et al. 2005). While the current understanding of soil

variability is very advanced at a regional scale (≥ 1:50 000) there is much scope to improve

our understanding at finer scales. As an input for precision agriculture, accurate and

detailed information about soil variability at a farm scale is required. With farm scale soil

maps, farmers will be better able to relate yield variability to changing soil properties.

Improved understand of soil variability is also useful to help mitigate land degradation. It

is difficult and complex allocating resources to manage land degradation problems such as

dryland salinity, soil loss, soil acidity, water quality and biodiversity loss. A great deal of

information is required to understand the processes taking place. This includes

groundwater recharge, surface water flows, river salinity, water nutrient loadings, the

impact of loss of biodiversity and the cost of implementing land management change

(Beverly et al. 2003). Adding further to this complexity is the temporal and spatial

discontinuity between the implementation of management strategies and observed

outcomes. Impacts of these strategies often manifest themselves many kilometres from the

Chapter 1: Introduction 2

implementation site and are often only seen after years or even decades. One way to

mitigate the difficulty in linking the cause and effect is to estimate the likely outcomes of

landscape intervention from mathematical models of landscape or biological processes.

Models that would benefit from improved soil maps are those related to plant growth and

soil processes such as hydrology. Plant growth models estimate the suitability and growth

of plants under different conditions. They require inputs relating to the climate (e.g. rainfall

and temperature), soil (e.g. texture, depth, slope and available water holding capacity) and

the plants themselves. Information about the plant largely relates to how they interact with

the environment, for example, the pH range a plant can tolerate or the degree of aeration/

water-logging preferred by the plant (Hackett and Harris 1990). By contrast, soil hydrology

models involve the estimation of the movement of water through the soil. Thus they are

most concerned with the soil texture and structure as it affects water movement due to

saturated and unsaturated flow (Hatton 1998, Beverly and Croton 2001).

Early models were developed for use with point data, but with improving information

technology they are been being applied to regions and landscapes. As a result spatially

distributed input data on a range of variables including soil is required. Spatial data on soils

originally came from existing regional soil maps. While a unique and informative resource

for regional land managers and planners, the regional soil maps, with scales of 1:250 000

to 1:50 000, have substantial limitations when applied to finer local and farm scales of less

than 1:20 000 (Maschmedt 2000). The broad scale regional maps do not have the detail

required to portray within-paddock variability and thus inform decision making at the farm

level, or provide a suitable input for fine scale catchment modelling. To effectively map

these local land changes at a usable scale is difficult and expensive by conventional soil

mapping methodologies. Furthermore, the polygon-based unit representation of discrete

boundaries delineating homogeneous areas is not a realistic representation of the

continuous variability found in soils. While variation within a polygon may be

acknowledged by descriptors or attributes assigned to the mapping unit, that variation is

not spatially located.

A further limitation of traditional soil mapping is the reliance on laboratory analysis of

samples to quantify soil properties. These methods are generally time consuming and

expensive, requiring many consecutive steps and often involve toxic and corrosive

reagents. What is more, soils are not homogenous and mechanisms and interactions within


the soil matrix are difficult to understand. Conventional laboratory techniques do not

account for this complexity but instead rely on physical and chemical relationships

between limited components to explain observed interactions (Viscarra Rossel et al. 2006).

Consequently new methods such as mass spectroscopy, X-Ray diffraction, nuclear

magnetic resonance, and visible-near infrared and mid infrared spectroscopy are being

used to analyse soil composition. These methods are typically rapid and repeatable,

reducing the need for extractions and allowing for the analysis of the solid soil matrix

(Janik et al. 1998).

One approach widely employed to avoid expensive and time consuming laboratory

analysis is the use of soil field survey protocols (McDonald and Isbell 1990). Soil field

survey aims to derive as much information about soil as possible from a series of simple

protocols applicable in the field without the need for laboratory or ongoing analysis. These

methodologies are applied extensively in Australia around high value and irrigated

agriculture to determine soil properties and maximise irrigation efficiency. However,

despite the speed and relative affordability of soil field survey, it does suffer from

substantial limitations. Soil field survey is prohibitively expensive for all but the most

intensive land uses; broad acre agriculture does not generally provide sufficient returns to

make such expenditure affordable. Furthermore, most of the techniques used in soil field

survey are subjective, requiring extensive training to achieve acceptable accuracy.

Visible-near infrared and mid infrared spectroscopy (from here summarised as reflectance

spectroscopy) are particularly appealing because they are quick, require almost no sample

preparation and are relatively inexpensive. Most impressively some researchers claim to

achieve more accurate results with reflectance spectroscopy than with traditional

laboratory methods (Viscarra Rossel et al. 2006). Thus reflectance spectroscopy has the

potential to improve the speed and perhaps the accuracy of soil sampling. Such advances in

soil analysis could substantially improve the density of sampling and improve the spatial

resolution of mapping without prohibitive increases in cost. However, such techniques still

generally lend themselves to polygon-based unit representation, albeit facilitating

improved spatial resolution.

Remote sensing from satellites or aircraft is a technology that relies on similar principles as

reflectance spectroscopy, measuring light reflected from materials, but it provides these

measurements over a spatially continuous area in the form of images. Source materials of


the spectral response can be identified and the relative abundance of materials can be

mapped. Opposed to traditional mapping and monitoring methods that rely on point data

sources from which to project the properties of whole landscapes, remote sensing provides

information over the whole landscape with a ground resolution down to 2 or 3 metres.

Studies have shown this form of earth observation to be useful in mapping and monitoring

many surface features from geology and minerals, vegetation and ecology to soils and soil

properties. Soil mapping with remote sensing has been carried out largely in the northern

hemisphere (Drake et al. 1999). Generally these studies have examined one or two

particular soil properties (Galvao et al. 2001, Chabrillat et al. 2002) although there are

some exceptions to this where many soil properties have been examined simultaneously

(Ben-Dor et al. 2002). Techniques used in these studies to extract thematic information

from the imagery include spectral matching, mixture modelling (Drake et al. 1999), band

ratios (Ryan and Lewis 2000) and multivariate statistical classification (Palacios-Orueta

and Ustin 1996). Additionally, both in Australia and overseas there have been studies

aimed at mapping the expression of degradation such as salinity, mapping salt affected

soils and the indicator vegetation types (Sharma and Bhargava 1988, Hick and Russell

1990, Dutkiewicz et al. 2003). The most significant addition that remote sensing brings to

these applications is the spatial continuity of the data as opposed to the interpolation of

point data of traditional mapping and monitoring.

1.2 Scope

The research presented in this thesis addresses the need for improved information on soil

variation at scales appropriate for precision agriculture and landscape process modelling. It

assesses the potential for prediction of soil properties with visible-near infrared reflectance

spectroscopy and examines some of the limitations to quantification of soil properties

under plant cover. Spectroscopic analysis of soil samples is used to inform regional

mapping of surface soils with hyperspectral imagery. The research comprises four

components addressing these areas.

The first study aimed to discriminate samples into soil field survey classes from spectral

response curves measured under laboratory conditions. Field survey analysis is a common

method used for characterising soil samples to map soil properties. Texture and colour are

measured in almost all instances and carbonate is measured in most southern Australian


environments where it is prevalent in soil profiles. Despite protocols for these methods,

there is still some subjectivity and variation in measurement and although faster than lab

analyses can still be time consuming. Spectral analysis of these properties could provide a

new objective, rapid technique to assist soil survey. Furthermore, establishing a

methodology that can predict field survey classes would provide continuity between

spectroscopy techniques and traditional field survey.

A second laboratory study aimed to predict quantitative soil properties in order to

overcome the subjectivity inherent in the soil field survey. Rapid and relatively

inexpensive determination of soil properties through reflectance spectroscopy could

improve the resolution of existing maps and provide important inputs for modeling and

precision agriculture. The soil properties predicted from spectral response curves were clay

content, organic carbon content, iron oxide content and carbonate content. These were

chosen due to their requirement as inputs for current hydrological models and because of

their general importance in determining agricultural fertility.

The influence of photosynthetic and non-photosynthetic plant material on the detection and

quantification of soil types was examined in a third study. A pilot study into image analysis

over South Australia’s northern agricultural districts found direct sensing of soil properties

difficult due to crop residue obscuring the earth’s surface. As a result of that finding this

subsequent study aimed to determine realistic thresholds for the spectral determination of

soil type and soil exposure using imagery simulated from laboratory measured reflectance.

The simulated imagery allowed for specifically quantified abundance ratios of different

soil and cover materials. The land surface in agricultural districts in southern Australia is

typically obscured from imaging sensors by photosynthetic vegetation or crop residue. For

image remote sensing of soils to inform future soil mapping programs, the spectral

interaction of soil and plant cover must be understood.

The final study used airborne hyperspectral imagery in an attempt to map surface soils in a

broad acre agricultural district. This study aimed to examine the ability of hyperspectral

remote sensing to map soil variability and discriminate different soil types. Partial spectral

unmixing and image derived endmembers were used to minimise a priori knowledge and

examine the possibility of using hyperspectral imagery to inform subsequent soil sampling

and survey.


All of these studies were carried out using imagery and soils from two agricultural districts

of South Australia; Monarto, 50 km east of Adelaide and Jamestown, 200 km north of

Adelaide.

1.3 Thesis Structure

The thesis is structured with 8 chapters. This introductory chapter (Chapter 1) provides a

brief overview of the motivation behind the research and outlines the unifying research

theme. Chapter 2 provides a detailed review of remote sensing, reflectance spectroscopy

and soil mapping literature. The review examines the literature that was available until the

beginning of the research phase of the study. Specific knowledge gaps relating to each

component of the research are addressed in subsequent research chapters (Chapters 3-6)

and each of these chapters contains more recent literature relevant to their specific

objectives. Chapter 3 examines the discrimination of field survey soil classes using

laboratory collected reflectance spectra. This chapter has been peer reviewed and accepted

for publication in the proceedings of SSC 2005 Spatial Intelligence, Innovation and Praxis

Conference (Summers et al. 2005). Chapter 4 focuses on the prediction of quantitative soil

properties from laboratory-collected reflectance data. This Chapter has been peer reviewed

and accepted for publication in Ecological Indicators (Summers et al. In Press). Chapter 5

evaluates the spectral unmixing of soil and vegetation (photosynthetic and non-

photosynthetic) using simulated laboratory imagery. This chapter is currently in review for

publication in the International Journal of Remote Sensing (Summers et al. In Review).

Chapter 6 examines the unmixing and mapping of soils using HyMap hyperspectral

imagery in South Australia’s northern agricultural districts. This chapter has also been peer

reviewed and accepted for publication in the proceedings of SSC 2009 Spatial Diversity

(Summers et al. 2009). Therefore, the thesis is presented with Chapter 3, Chapter 4,

Chapter 5 and Chapter 6 as standalone articles for publication. Although they have been

reformatted to match the rest of the thesis, the content is unchanged from the submitted

articles. This style of presentation necessarily results in some areas of repetition,

particularly in the introductions, methods and reference lists. The discussion (Chapter 7)

provides an overview of the research findings under the unifying thesis topic and outlines

the acquired knowledge. Furthermore, it will discuss the limitation and significance of the

findings and outline to future research that arises from these findings.


1.4 References

Ben-Dor, E., Patkin, K., Banin, A. and Karnieli, A. 2002 Mapping of several soil properties using DAIS-7915 hyperspectral scanner data - a case study over clayey soils in Israel, International Journal of Remote

Sensing, 23, 1043-1062.

Beverly, C., Avery, A., Ridley, A. and Littleboy, M. 2003 Linking farm management with catchment response in modelling framework, In 11th Australian Agronomy Conference, Geelong,

Beverly, C. and Croton, J. T. 2001 Formulation and application of the unsaturated/saturated catchment models SUSCAT and WEC-C, Hydrological Processes, 15.

Bongiovanni, R. and Lowenberg-Deboer, J. 2004 Precision agriculture and sustainability, Precision

Agriculture, 5, 359-387.

Chabrillat, S., Goetz, A. F. H., Krosley, L. and Olsen, H. W. 2002 Use of hyperspectral images in the identification and mapping of expansive clay soils and the role of spatial resolution, Remote Sensing of

Environment, 82, 431-445.

Drake, N. A., Mackin, S. and Settle, J. J. 1999 Mapping vegetation, soils, and geology in semiarid shrublands using spectral matching and mixture modeling of SWIR AVIRIS imagery, Remote Sensing of Environment, 68, 12-25.

Dutkiewicz, A., Lewis, M. and Ostendorf, B. 2003 Evaluation of hyperspectral imagery for mapping the symptoms of dryland salinity, In Spatial Sciences Coalition 2003, Canberra,

Galvao, L. S., Pizarro, M. A. and Epiphanio, J. C. N. 2001 Variations in reflectance of tropical soils: Spectral-chemical composition relationships from AVIRIS data, Remote Sensing of Environment, 75, 245-255.

Hackett, C. and Harris, G. 1990 PLANTGRO: A software package for the prediction of plant growth, Griffith University, Melbourne.

Hatton, T. 1998 Catchment scale recharge modeling, In The basics of recharge and discharge (Ed, L. Zhang) CSIRO Publishing, Melbourne.

Hick, P. T. and Russell, W. G. R. 1990 Some spectral considerations for remote sensing of soil salinity, Australian Journal of Remote Sensing, 28, 417-431.

Janik, L. J., Merry, R. H. and Skjemstad, J. O. 1998 Can mid infrared diffuse reflectance analysis replace soil extractions?, Australian Journal of Experimental Agriculture, 38, 681-696.

John, M., Pannell, D. and Kingwell, R. 2005 Climate change and the economics of farm management in the face of land degradation: Dryland salinity in western Australia, Canadian Journal of Agricultural Economics, 53, 443-459.

Kingwell, R. and Pannell, D. 2005 Economic trends and drivers affecting the Wheatbelt of western Australia to 2030, Australian Journal of Agricultural Research, 56, 553-561.

Maschmedt, D. 2000 Assessing agricultural land: Agricultural land classification standards used in South Australia's land resource mapping program, Primary Industries and Resources South Australia, Adelaide,

McBratney, A., Whelan, B. M., Ancev, T. and Bouma, J. 2005 Future directions of precision agriculture, Precision Agriculture, 6, 7-23.

McDonald, R. C. and Isbell, R. F. 1990 Soil profile, In Australian soil and land survey: Field handbook (Eds, R. C. McDonald, R. F. Isbell, J. G. Speight, J. Walker and M. S. Hopkins) Inkata Press, Melbourne.

Palacios-Orueta, A. and Ustin, S. L. 1996 Multivariate statistical classification of soil spectra, Remote

Sensing of Environment, 57, 108-118.

Passioura, J. B. 2002 Environmental biology and crop improvement, Functional Plant Biology, 29, 537-546.

Rengasamy, P. 2006 World salinization with emphasis on Australia, Journal of Experimental Botany, 57, 1017-1023.

Ryan, S. and Lewis, M. 2000 Discrimination and mapping soils using HyMap hyperspectral imagery, Barossa valley, S.A., In 10th Australasian Remote Sensing and Photogrammetry Conference, Adelaide,


Sharma, R. C. and Bhargava, G. P. 1988 Landsat imagery for mapping saline soils and wet lands in north-west India, International Journal of Remote Sensing, 9, 39-44.

Summers, D., Lewis, M., Ostendorf, B. and Chittleborough, D. J. 2005 Spectral discrimination of soil properties, In SSC 2005 Spatial Intelligence, Innovation and Praxis: The National Biennial Conference of the Spatial Sciences Institute, Melbourne, Australia, September, 2005.

Summers, D., Lewis, M., Ostendorf, B. and Chittleborough, D. J. 2009 Mapping soil variability with hyperspectral image data, In SSC 2009 Spatial diversity: The Biennial International Conference of the Surveying and Spatial Sciences Institute, Adelaide, Australia, September-October, 2009.

Summers, D., Lewis, M., Ostendorf, B. and Chittleborough, D. J. In Press Visible near-infrared reflectance spectroscopy as a predictive indicator of soil properties, Ecological Indicators.

Summers, D., Lewis, M., Ostendorf, B. and Chittleborough, D. J. In Review Unmixing of soil types and estimation of soil exposure with simulated hyperspectral imagery, International Journal of Remote Sensing.

Viscarra Rossel, R. A., Walvoort, D. J. J., McBratney, A. B., Janik, L. J. and Skjemstad, J. O. 2006 Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties, Geoderma, 131, 59-75.

9

Chapter 2

Identifying and Evaluating Remote Sensing Techniques

and Methodologies for Mapping Soils

2.1 Introduction

This project investigates contemporary remote sensing and reflectance spectroscopy

technologies and how they can be used to effectively map soils at a resolution that provides

useful property-scale land management tools. The project was established to investigate the

possibilities provided by these new technologies to overcome some of the expense and

limitations of conventional soil mapping techniques.

2.2 Scope of Review

This review briefly examines the theory behind soil formation and current regional scale (≥

1:50 000) soil mapping methods to provide an understanding of what is available, the

benefits arising from current methodologies and current databases available, but also to

detail the relative shortcomings. The review details some of the methodologies used more

broadly in research and general soil mapping such as pedometrics and geostatistics and

explains how these fit into the broader context of understanding soil process and mapping

procedures. It also examines digital terrain data and highlights previous studies where the

different methods have been incorporated. The review then examines the use of remote

sensing and reflectance spectroscopy and discusses how these technologies have been used

for land monitoring and soil attribute mapping.

Chapter 2: Literature review 10

2.3 Soil Formation and Mapping

1.1.1 Soil Formation

Soil formation is generally attributed to five soil forming factors. These are parent

material, climate, topography, biological processes and time (Jenny 1941). These factors

all combine to effect soil composition.

The interaction of these factors was expressed by Jenny (1941) in the following equation:

S = ƒ (Cl, o, r, p, t)

Where S is soil, Cl is climate, o is organisms (including humans), r is topography, p is

parent material and t is time. This equation defines a relationship between landform

processes and soil formation and their resulting properties.

Since its inception this equation has been considered a qualitative approach to

understanding soil formation and many surveyors have used it as such. These surveyors

use it as part of their expert knowledge in understanding the factors that are important in

producing soil pattern (McBratney et al. 2003). Other researches have taken quantitative

approaches to the equation by trying to formalise it. These approaches generally involve

analysis where all but one function is kept constant and as such quantitative climofunctions

and topofunctions have been developed; however their use in soil mapping is limited

(McBratney et al. 2003). Nonetheless, these factors play an important role in soil

variability and as such should be acknowledged.

1.1.2 Soil Mapping in Australia

A number of major soil mapping programs have taken place in Australia. Many of these

were undertaken by the Commonwealth Scientific and Industrial Research Organisation

(CSIRO) while others were conducted by individual states. Early national maps include the

Atlas of Australian Soils prepared at 1:3,000,000 scale (Northcote et al. 1968). Although

some local scale mapping is dated as far back as the 1920s, modern techniques were not

applied until the 1940s (Taylor 1970). However, these surveys were generally broad scale

and driven by local catchment and rural area planning strategies. Still today the extent of

systematic soil mapping in some of Australia’s agricultural districts is limited. For

example, only 50% of soils in the Murray Darling Basin, Australia’s most important


agricultural area covering approximately 1,000,000 km2, are mapped at 1:250,000 and 3%

at 1:100,000 (McBratney et al. 2003). However, in Western Australia and South Australia

substantive efforts have been made since the 1980s to provide seamless mapping of the

agricultural districts across the states.

In Western Australia this mapping covers the south west agricultural districts and

combines surveys at various scales. A methodology has been developed to provide a nested

hierarchy of soil-landuse mapping units that allows for pre-existing and recent surveys to

be included into a seamless mapping database (Schoknecht et al. 2004). Thus, the resultant

database includes maps at various scales from 1:20,000 – 1:250,000, however, the vast

majority is at scales no smaller than 1:100 000. In South Australia soil mapping was

carried out at scales of 1:100 000 and 1:50 000 depending on the agricultural district. Soil

landscape units were developed to provide a means of determining the suitability of land

for different uses (Maschmedt 2000). The South Australian and Western Australian

databases provide a substantive and informative regional scale land assessment tool

(Maschmedt 2000, Schoknecht et al. 2004). However, the scale of most of the mapping

does not account for property scale soil variability that can have a significant effect on land

management decisions.

1.1.3 Traditional Soil Mapping Methodology

The traditional methodology used to make soil landscape maps involves expert knowledge

and significant expense in soil sampling and analysis. Aerial photographs are examined by

experts who delineate polygons of what appear to be different soils and combinations of

soil visible on the photos or inferred from position in the landscape. Soil surveyors then

collect soil samples from representative areas for each mapping unit to characterise the

soils within each polygon. The polygons are then assigned to soil classes based on the

composition of this analysis and depicted as such on seamless landscape unit maps (Gunn

et al. 1988, Schoknecht et al. 2004). While this is a popular and effective methodology to

map soil for various applications, there are limitations associated with it.

Continuous Variability and Polygons

A major limitation of traditional soil mapping is its dependence on polygon-based unit

representation. The continuous variability of soils in the landscape is portrayed by

homogeneous polygons with discrete boundaries. This results in class assignment


generalisation which involves grouping suites of soils to single mapping units using crisp

logic (Zhu 1997). While variation within each polygon may be accounted for through

descriptors or attributes assigned to the mapping unit, that variation is not spatially located

(Zhu 1997). This is rarely a realistic representation of soils. Variation in soils is

continuous, more often demonstrating a diffuse contrast from one soil to another rather

than an abrupt change.

Scale

While traditional regional scale soil maps provide useful and reliable information for some

purposes their low resolution does not account for property-scale soil variability that can

have a significant effect on land management decisions. Only soil attributes or objects

larger than a certain size (called the ‘minimum mapping size’) can be represented on maps

at a given scale. As a result, areas smaller than this minimum mapping size are either

incorporated into surrounding soil objects or entirely omitted: this is known as spatial

generalisation (Zhu 1997). Thus the resolution of traditional soil maps may be a limiting

factor when incorporated into environmental models using other ‘fine resolution’

environmental data (Zhu 1997). To effectively map local land changes at a usable sub-

catchment scale is difficult and prohibitively expensive by conventional soil mapping

methodologies. However, most data obtained from digital terrain analyses and remote

sensing provide for discrimination of areas less than one hectare and are thus capable of

describing small areas of the environment.

2.4 Improving Soil Mapping

Due to the expense and time consuming nature of traditional soil survey, recent decades

have seen the development of new methods to extend soil property prediction from

relatively sparse traditional data sets using secondary information (Bishop and McBratney

2001, McBratney et al. 2002). These methods include pedotransfer functions, geostatistics

and continuous classifications (McBratney et al. 2003).

1.1.4 Pedotransfer functions

Pedotransfer function (PTF) is a generic term for a soil prediction method that uses some

known soil property or properties to estimate another unknown property. PTFs came about

through a desire to predict difficult and expensive to measure soil properties from more

easily measurable, surrogate properties (Minasny et al. 1999, McBratney et al. 2002).


There are many soil properties that are prohibitive to measure over large areas and

especially at fine scales. PTFs are a method by which to estimate these properties from

more available data sets. The properties predicted from PTFs can be used in further

modelling at a field and regional scale (Mayr and Jarvis 1999, McBratney et al. 2002).

Most commonly PTFs are used to predict soil hydraulic properties, although this is not

their exclusive use (McBratney et al. 2002). Many studies have been undertaken in the

estimation of soil water retention curves based on properties such as texture, bulk density

and organic carbon (Mayr and Jarvis 1999, Minasny et al. 1999, McBratney et al. 2002).

Other uses include the estimation of pesticide leaching with relation to regional water flow

(Petach et al. 1991, Soutter and Pannatier 1996), modelling heavy metal movement and

accumulation (Tiktak et al. 1999) and yield estimation (Haskett et al. 1995, Timlin et al.

1996). However, even measuring surrogate properties for PTFs requires field sampling and

laboratory analysis. Therefore to improve the resolution of soil maps through the use of

pedotransfer functions will likely require increased sampling density and laboratory

analysis of discrete samples, all of which increases the cost of mapping at finer scales.

1.1.5 Geostatistical analysis

Geostatistical analysis has been used to aid in soil attribute prediction to improve soil

mapping (McBratney et al. 2003). Traditionally geostatistics provide a means to explain

variability between sample points in soil survey but it also offers a measure of uncertainty

in soil maps that is becoming increasingly important (Bishop and McBratney 2001, Bishop

et al. 2001). Geostatistical methods include kriging, co-kriging and regression kriging.

Kriging is a univariate approach to soil prediction that improves significantly on results

obtained by traditional methods such as multiple linear regression, but limits the inclusion

of other data sets such as remotely sensed data (Bishop and McBratney 2001). Co-kriging

on the other hand is a multivariate approach that allows the inclusion of ancillary variables

correlated with the primary data sets (McBratney et al. 2003). While initially these

ancillary data sets were other soil variables (McBratney et al. 2003), later studies

incorporated crop yield, terrain data and satellite remote sensing imagery (Bhatti et al.

1991, Ishida and Ando 1999, Bishop and McBratney 2001). Regression kriging on the

other hand involves kriging of the residuals of regression models such as multiple linear

regression or regression tree regression (Bishop and McBratney 2001). While geostatistics

offer significant tools for soil prediction between data points they generally allow only a


‘crisp’ allocation of membership to any one class. That is to say, a given data point on a

soil map can only belong to one class (Burrough et al. 1997). However, there are some

methods such as indicator kriging or stochastic simulation that allow the inclusion of

categorical data (Goovaerts 1997).

1.1.6 Continuous Classification

Continuous classification or fuzzy logic was developed out of the acknowledgement that

attributes in the landscape vary continuously across space (Burrough et al. 1997,

McBratney et al. 2000, Triantafilis et al. 2001). Continuous classification offers a means of

attributing partial (or fuzzy) membership of more than one class to a single data point.

Membership of a class to a data point is assigned a value between 0 and 1, with 0

indicating no membership and 1 indicating total membership (Burrough et al. 1997,

McBratney and Odeh 1997, Stein et al. 1998). In soil science fuzzy set theory is generally

used for classification, allowing continuous class membership across continuous space

(McBratney and Odeh 1997). Fuzzy k-means (also known as fuzzy c-means) are a means

by which to compute fuzzy membership to a class based on attribute data (Stein et al.

1998). This has been used in soil science for mapping of continuous classes and soil

attributes (McBratney et al. 2000, Triantafilis et al. 2001).

1.1.7 Digital Elevation Models and Topographic Indices

Digital elevation models (DEM) are becoming increasingly important in understanding

natural processes such as the formation of soils and the subsequent erosional and

depositional processes to which they are subject. It has long been acknowledged that

topography is an important factor in soil formation (Jenny 1941) and analysis of terrain

variables in the field or from air photos has also been used historically to aid in soil survey

(Boer et al. 1996, Burrough et al. 1997). In conventional soil survey, particularly at a local

scale, qualitative terrain variables are used to extrapolate point surveys out to broader

regions (McKenzie et al. 2000). Since the development of remote sensing and adequate

computer technology terrain data has been used more and more to improve the diagnostic

and predictive power of remote sensing and earth process modelling (Odeh et al. 1994,

McKenzie and Ryan 1999, Metternicht et al. 2002, Drysdale and Metternicht 2003).

Variation in soil properties such as texture, nutrient concentration and availability and

cation exchange capacity (CEC) have been correlated with variations in topography


(Brubaker et al. 1993). Statistical prediction methods have been used with landform

attributes to predict soil properties such as subsoil clay, depth to solum and depth to

bedrock (Odeh et al. 1994, 1995, Skidmore et al. 1997). Soil parameters such as

phosphorus and pH have been correlated with terrain position (Skidmore et al. 1997)

Terrain data and airborne multispectral imagery have been used to predict soil variability

to aid in soil sampling, significantly improving the effectiveness of sampling strategies for

soil survey (Drysdale and Metternicht 2003). Topographic and landform variables have

been incorporated with gamma ray spectroscopy to predict soil profile depth, total

phosphorus and total carbon with varying degrees of success (McKenzie and Ryan 1999).

Radiometrics and digital terrain data have been used to examine the relationship between

soil, landform attributes and proteoid plants (Verboom and Pate 2003).

There are generally considered to be two types of topographic indices: primary attributes

and secondary or compound attributes (Wilson and Gallant 2000, McBratney et al. 2003).

Primary attributes are those that are derived directly from DEMs. For example slope,

defined as the gradient, affects surface and subsurface water flow, precipitation,

vegetation, soil water content, and land capability class. Aspect, measured in degrees

clockwise from north, affects solar radiation, vegetation distribution and

evapotranspiration (Wilson and Gallant 2000). Secondary attributes involve a combination

of primary attributes and are used to characterise the spatial variability of landscape

processes. For example, the topographic wetness index, which is derived from catchment

area, slope gradient and soil transmissivity, predicts soil moisture as it is affected by

topography (Wilson and Gallant 2000).

Studies have used optical remote sensing and slope to assist in the determination of soil

variability for the purposes of soil sampling design for soil survey (Drysdale et al. 2002,

Drysdale and Metternicht 2003). Secondary and primary topographic indices are also used

in combination. Field morphology and soil depth have been predicted successfully using

indices such as slope, wetness index, stream power, curvature and upslope and downslope

area (Odeh et al. 1994, Gessler et al. 1995, Boer et al. 1996). Topographic wetness index,

slope, curvature and downslope slope were successfully used with radiometrics to predict

soil depth, total phosphorus and total carbon (McKenzie and Ryan 1999). Other studies

have used digital terrain data in combination with other explanatory variables such as


optical remote sensing and radiometrics (Cialella et al. 1997, Skidmore et al. 1997, Taylor

et al. 2002, Thwaites 2002b, 2002a, Verboom and Pate 2003).

2.5 Remote Sensing and Reflectance Spectroscopy

The fields of remote sensing and reflectance spectroscopy are based on the principle of

electromagnetic radiation being reflected from a material and then detected by a sensor.

Remote sensing records the reflectance over the earths surface, collected from airborne or

satellite sensors, creating a continuous image. The image is made up of pixels, each

recorded from different ground resolution units and with reflectance spectra characteristic

of the material within the field of view. Alternatively, reflectance spectroscopy collects

information from discrete samples, typically recorded in the field or laboratory. Each

sample provides one reflectance spectrum characteristic of the material being analysed.

Remote sensing and reflectance spectroscopy can be applied across many different

wavelengths of the electromagnetic spectrum, each with different strengths and

weaknesses. This thesis focuses on what is known as the optical range covering the visible,

near-infrared and shortwave-infrared1 regions of the spectrum (Vis-NIR-SWIR, 400 –

2500 nm). The advantages of the optical range are that it is a passive technology; there is a

range of airborne and satellite sensors available, and it offers a cost effectiveness and

repeatability not available from other technologies. This range is also advantageous

because it is here that solar irradiance is at a maximum and there is sufficient reflected

radiation to be recorded by passive sensors.

The progression of improved spectral and spatial resolution has allowed for continued

development in the application of remote sensing and reflectance spectroscopy in many

areas. The development from multispectral to hyperspectral remote sensing has given users

increased diagnostic power allowing for the detection and discrimination of more and more

of the earth’s surface features.

1 The SWIR is included in the NIR in some disciplines such as chemistry and reflectance spectroscopy.


1.1.8 Spectral Characteristics of Soils

Interest in the optical properties of soils coincided with the development of spectrometers

capable of measuring electromagnetic reflectance at fine resolutions and the development

of airborne and space-based remote sensing. In this spectral range many of the constituents

of soils are optically active, absorbing radiation at specific wavelengths. Reflectance

spectrometry and remote sensing record the radiation reflected from materials and so

provides information about the active absorption processes taking place.

Electromagnetic radiation interacts with matter at atomic, molecular and structural levels.

At an atomic and molecular level, translational, rotational and vibrational motion of the

nuclei determine the interaction (Ben-Dor et al. 1999). Most important in soil reflectance is

vibrational motion which can exist at several different energy levels in an atom or

molecule and results in the stretching of molecular bond lengths or the bending bond

angles (Ben-Dor et al. 1999). Transition between energy levels can occur due to emission

or absorbance of radiation at specific wavelengths or frequencies. The locations of these

wavelengths are called fundamental bands, overtone bands and combination bands

depending on the type of transition. Absorption at fundamental, overtone and combination

bands result in absorption features within the reflectance spectra. Overtone and

combination bands are common in soil reflectance spectra over the NIR-SWIR region

whereas fundamental bands do not occur in this range (Ben-Dor et al. 1999, Clark 1999).

Examples of overtone bands in soil reflectance spectra include the oxygen-hydrogen (OH)

stretch at about 1400 nm and that associated with the CO32+ ion at 2300 to 2350 nm. An

example of a combination band is the bending and stretching of aluminium-hydroxyl (Al-

OH) at 2200 nm (Clark 1999).

There are also bands associated with electron transitions. These occur due to changes in the

state of electrons attached to atoms or molecules caused by the absorption or emission of

radiation. The location of these bands are determined by the relative energy states of

electron shells around atoms and molecules but for the most part they occur in the

ultraviolet and visible portions of the spectrum (Clark 1999). For example, iron has a

feature in the Vis-NIR that results from electron transition between the ferrous ion (Fe2+)

and the ferric iron (Fe3+) (Ben-Dor 2002).

Electromagnetic radiation also interacts with matter at a physical or structural level. This

involves the reflection or scattering of radiation by a multitude materials that make up the


soil volume but does not cause changes in the position of absorption features or

chromophores (Ben-Dor 2002). The factors which affect this physical interaction include

particle size, viewing geometry, radiation intensity, incident angle, sample geometry and

azimuth angle (Clark 1999, Ben-Dor 2002). Spectral properties that are affected by

changes in these parameters are typically absorption feature intensity and spectral curve

through changes in baseline height (Clark 1999). While these factors are relatively easy to

control in laboratory experiments they are essentially uncontrollable in field and imaging

studies.

1.1.9 Soil Reflectance Spectra

Classification of soil reflectance spectra was initially carried out by Condit (1970). After

measuring 285 soil samples (both wet and dry) from the USA he found that they could be

represented by three distinct spectral curves. However, these curves were only in the range

300 to 1000nm and no attempt was made to relate the distinct spectral curve types to

chemical or physical properties of the soils.

Stoner and Baumgardner (1981) continued this work, conducting a study with 485

individual soil samples from the USA and Brazil. They discovered 5 distinct soil

reflectance curve forms that were identified by curve shape and the presence or absence of

absorption features (Figure 2.1).

Figure 2.1: Representative reflectance spectra of soils collected in the U.S.A. and Brazil. Curves a-e

explained in text below (Stoner and Baumgardner 1981).

a1172507

Text Box

NOTE: This figure is included on page 18 of the print copy of the thesis held in the University of Adelaide Library.


The first three curves of Stoner and Baumgardner (1981) are considered the same as those

presented by Condit (1970). The organic dominated form (type a) shows low overall

reflectance characterised by a concave curve shape from 500 to 1300 nm. Strong water

absorption bands are present at 1450 and 1950 nm in ‘type a’ and most other curve forms.

The minimally altered form (type b) has overall high reflectance and a convex shape

between 500 to 1300 nm. It also has strong water absorption bands at 1450 and 1950 nm

with weaker bands at 1200 and 1770 nm. The iron-affected form (type c) is characterised

by a slight ferric iron absorption at 700 nm and a strong iron absorption band at 900 nm.

The organic affected form (type d) has an overall reflectance higher than the organic

dominated form with a concave shape from 500 to 750 nm and a convex shape from 750 to

1300 nm. The iron-dominated form (type e) has decreasing reflectance with increasing

wavelength beyond 750 nm.

Most of the research since 1980 has focused on understanding the relationship between soil

properties and soil reflectance, with the goal of using soil spectra to predict the physio-

chemical composition of the soils. Generally this research has studies northern hemisphere

soils in the U.S.A., Europe and the Middle East. However, while these regions often

present different soils and land management regimes, it is possible that techniques and

methodologies developed may be applicable to Australia.

Soil Colour

Soil colour is an important measurement made in the classification of soils. It relates to,

and influenced by, soil moisture, permeability, organic matter (OM) content, mineralogy

and texture (Murtha 1988, Metternicht et al. 2002). The Munsell soil colour chart (2000) is

usually used to determine soil colour. This consists of a three dimensional identification of

colour describing the hue, value and chroma of a soil. Hue is a measure of the dominant

wavelength of light reflected from soil and results from a combination of pigments present

(i.e. minerals and OM). Value is a measure of the lightness compared to absolute white

while chroma is a measure of the purity of the hue (Ben-Dor et al. 1999, Munsell Color

Company 2000). Spectral reflectance is a quantitative means of measuring soil colour. As

spectral reflectance of soils has a direct relationship with soil colour, it can provide

information on soil moisture, permeability, OM content and mineralogy.

Reflectance spectroscopy of soils in the visible region has been used to determine Munsell

soil colour with some success, although the accuracy of the conversion was affected by soil


texture (Fernandez and Schulze 1987). Soil colour has also been derived from reflectance

spectra and related to the hematite content of soils (Torrent et al. 1983). Studies have

established a significant relationship between the albedo (brightness) of soil and the

Munsell value (a measure of soil lightness) but no relationship with hue or chroma (Post et

al. 2000). This is probably because soil colour is a function of a range of attributes, for

example, quartz content, OM content, iron oxide and clay. Other studies have used

multispectral sensors to develop relationships between the imagery and soil colour. SPOT

imagery has been used to successfully identify variation in soil colour not represented in

soil field mapping units (Agbu et al. 1990). Another study found strong correlations

between Landsat image data to Munsell soil colour in semiarid rangelands in North

America (Post et al. 1994). Variations in soil colour were also used to map soil organic

carbon with digitised aerial photography, essentially using visible light for the predictions

(Chen et al. 2000). Some research has aimed to predict soil colour from simulated

hyperspectral sensors, and results are favourable when compared with similar multispectral

simulations. However, the increased complexity and variability of image data has limited

the application of these methods to hyperspectral images (Leone and Escadafal 2001).

Soil Moisture

It was generally accepted from early studies that as the moisture content of a soil increases

the spectral reflectance decreases (Baumgardner et al. 1985, Post et al. 2000, Galvao et al.

2001, Weidong et al. 2002). This decrease in reflectance with increasing moisture content

stems from two sources; soil particles covered with thin films of water and water on the

lattice sites of some minerals present in the soil. However, despite the changes in

reflectance intensity, the overall shape of the curve forms remain relatively unchanged

(Condit 1970, Stoner and Baumgardner 1981).

The findings of earlier investigations, while correct, have been modified somewhat by

subsequent studies. Later studies found that the decrease in reflectance with increasing

moisture content is more pronounced at longer wavelengths (>1450nm) (Weidong et al.

2002). Weidong et al. (2002) also found that at higher moisture contents the trend is

reversed and reflectance increases with increasing water content. They determined this

critical point’ of reversal to be somewhere around field capacity, although it varied for

different soils, and occurs before the point where water absorption is saturating the

reflectance signal.


Also important when considering the effect of moisture on soil spectra is the presence of

water absorption bands. These water absorption bands relate to the effects of vibrational

frequencies of water molecules beyond 2500 nm (Baumgardner et al. 1985). The

absorption bands occur strongly at 1450 and 1950 nm with sharp peaks that indicate well-

defined sites and broad bands that denote unordered sites. The broad unordered bands are

more common in naturally occurring soils (Baumgardner et al. 1985, Galvao et al. 2001).

There are also weak bands that appear at 970, 1200 and 1770 nm (Hunt 1977). It has also

been contested that soil moisture is the most important variable in determining the

reflectance differences in the 2080-2320 µm bands (as found on the middle IR bands of

Landsat 4 and 5) (Baumgardner et al. 1985). Studies have used reflectance spectroscopy

and remote sensing to develop reliable spectral models for soil moisture (Ben-Dor et al.

2002, Whiting et al. 2004).

pH

Studies have found no chromophoric properties for pH (Ben-Dor and Banin 1995, Ben-Dor

et al. 2002). Whereas Ben-Dor et al. (2002) found correlations often exist between

different soil properties that are spectrally featureless, allowing the use of prediction

equations to reliably map such properties, they were unable to determine such a

relationship for soil pH using hyperspectral image data. Other studies have successfully

used reflectance spectroscopy and advanced statistical methods (e.g. partial least squares

regression and multivariate adaptive regression splines) to predict pH (Reeves et al. 2002,

Shepherd and Walsh 2002). However, the results are generally less successful than for

other soil properties with distinctive chromophoric properties.

Soil Organic Matter

The amount of soil organic matter (SOM) and type of SOM can significantly influence soil

spectral characteristics. Increasing SOM content of soils results in an decrease in the

spectral reflectance over the visible to NIR wavelength range, especially if the SOM

content is greater than 2% (Stoner and Baumgardner 1981, Henderson et al. 1992). It has

been found that, over the range between 520-800 nm, soils with an OM content higher than

2% have a concave shape and those with less then 2% have convex shape (Stoner and

Baumgardner 1981).


Different types of SOM have varying effects on soil spectral reflectance. Humic acid

accounts for most of the dark pigment of SOM and has lower reflectance over the visible to

short-wave spectral range. Alternatively, fulvic acid has been found to have no significant

influence on soil reflectance (Henderson et al. 1992). This study also found that soil

reflectance decreased with increasing SOM and highlighted bands that respond best to

SOM differences to allow for analysis.

Reflectance spectroscopy of soils has been used to predict SOM content. A study of 10 soil

types from North America found no absorption band that could be attributed to organic

matter in the infrared region (Krishnan et al. 1980). However, they did find that the visible

region of the spectrum provided the most reliable predictor (R2 = 0.873) and that

increasing organic carbon increases the slope of the curve at 800 nm. A study of soils in

Thailand using artificial neural networks found Vis – NIR a reliable predictor of SOM (R2

= 0.86) (Daniel et al. 2003). Other studies have predicted organic carbon using similar

techniques with some success (Shepherd and Walsh 2002, Islam et al. 2003). A

hyperspectral image study found reliable features in the reflectance spectra of heavy clay

soils in Israel to map soil SOM using prediction (calibration) equations (R2m > 0.82) (Ben-

Dor et al. 2002). Another image study used digitised colour aerial photography was

successfully used to map SOM at a paddock scale (r2 = 0.997) (Chen et al. 2000). Both of

these image studies relied heavily on exposed soil and took place over largely cultivated

areas.

Mineralogy

The different minerals that make up the largest component of soils affect the spectral

reflectance of the soil through the presence of absorption bands and overall spectral

brightness. Quartz is the largest and most common component of soils; it displays no

unique absorption feature in the Vis-NIR-SWIR range although it does increase the overall

brightness.

Clay minerals do have distinctive absorption bands that are caused by unique vibrational

overtones, electronic and charge transfers, and conduction processes (for example Figure

2.2) (Clark 1999). These absorption bands provide a diagnostic tool and with reflectance

spectroscopy have been used to determine the specific mineralogy of soils (Clark et al.

1990). Spectral features characteristic of clay minerals (around 2200nm) were successfully

extracted from AVIRIS imagery and used to identify soil clay mineralogy (smectite,


kaolinite and illite) (Chabrillat et al. 2002). Similarly, absorption band position, depth and

asymmetry have been used to map alteration phases with AVIRIS imagery (van der Meer

2004). Mineralogical identification has been achieved when the target material is partially

obscured by vegetation due largely to the distinctive absorption features (Chabrillat et al.

2002).

Figure 2.2: Example of reflectance spectra Kaolinite minerals showing absorption bands (~ 2200 µµµµm)

characteristic of clay minerals (Clark 1999)

Texture and particle size

Soil texture is influenced by many factors including the amount, size and type of clay

mineralogy, organic matter, carbonates and soil structure. Particle size distributions refer

simply to the relative amounts of particles within the size classes of sand, silt and clay,

although they are probably the most determining factor of the soil texture (Murtha 1988).

There is a commonly observed relationship between soil composition and texture that

affects the determination of the contribution of soil texture to observed reflectance (Galvao

et al. 1997). For example, sandy soils have a higher reflectance, due to lower amounts of

OM, iron oxides and clay minerals, than heavy textured clay soils. These factors all

contribute to the spectral reflectance of the soils and it becomes unclear which property is

contributing to the spectral profile.

Decreases in particle size of a mineral can increase overall spectral reflectance

(Baumgardner et al., 1985). This is caused by more energy being reflected from the soil

mineral than is lost between coarser grained aggregates. Alternatively, clay (<0.002 mm)

generally has lower reflectance than soils with sand and/or silt (>0.002 mm), and finer


textured soils appear darker than coarse textured soils (Irons et al. 1989). This is possibly

due to the increased water holding capacity of clays. A study of tropical Brazilian soils

found that clay content can be more reliably measured in subsurface horizons because of

their lower OM. This is thought to be due to lignin and cellulose absorption near 2200 nm

masking clay absorption at the same location (Galvao et al. 1997, Galvao et al. 2001).

Despite these complications, reflectance spectroscopy in the Vis-NIR-SVIR has been used

to reliably predict clay content and other particle sizes in a number of studies (Chang et al.

2001, Shepherd and Walsh 2002, Cozzolino and Morón 2003).

In image studies surface crusts also affect reflectance spectra. They affect both albedo due

to particle size and also spectral absorption features due to changes in chemical

composition (Ben-Dor et al. 2003). This could have significant impacts on optical remote

sensing because it is the surface that is visible to the sensor. Surface crust may not be a

good predictor for what is under the surface because it is severely affected by management

practices as well as soil chemistry and physiology.

Iron Oxide

Iron oxide affects soil reflectance spectra with broad and shallow absorption features at

wavelengths lower than 1000 nm and overall lower albedo as iron oxide content increases

(Hunt 1977, White et al. 1997, Galvao et al. 2001). Reflectance spectroscopy has been

used to predict iron oxide content in a number of studies with ranging success. Some

studies have achieve relatively poor correlations (R2 = 0.5) (Islam et al. 2003) while others

have more successful (R2 = 0.64 and R2 = 0.9) (Chang et al. 2001, Cozzolino and Morón

2003). Iron oxide has also been correlated to surface soil reflectance within multispectral

and hyperspectral image studies (Stoner and Baumgardner 1981, Galvao et al. 2001,

Metternicht et al. 2002). Iron oxide abundance has been mapped using multispectral

imagery and changes in concentrations have been reliably predicted (r = 0.91) (White et al.

1997). Other studies have used principal components analysis to successfully map iron

oxide (Fraser 1991, Tangestani and Moore 2002). The study by White et al. (1997) was

carried out in a desert with little vegetation and a quartz-dominated desert soil. Other

studies have found that OM can interfere with the detection of iron oxide due to

interference of absorption features due to the different materials (Galvao et al. 1997).

Figure 2.3 shows the spectral characteristics of hematite with dry and green vegetation

demonstrating how they coincide in the spectral range. Other studies have also


demonstrated this interference between iron oxide and vegetation (Fraser 1991, Galvao et

al. 1997).

Figure 2.3: Showing spectral features of hematite, green grass and dry grass (Fraser 1991).

Salinity

Most of the salts responsible for soil salinity have no direct spectral features or

chromophores that allows for their discrimination. Despite this some studies have

successfully predicted salinity with reflectance spectroscopy (R2 = 0.65) (Shibusawa et al.

2001) although others have had less success (R2 = 0.1) (Islam et al. 2003). It has been

postulated that successful prediction of salinity is governed by inter-correlation between

other soil properties such as soil moisture (Ben-Dor et al. 2002).

For multispectral image studies the inclusion of topographic data is sometimes used to

mitigate the poor diagnostic power of the sensor and improve the classification. For

example a study used Landsat TM and DEM derived topographical indices to map salinity

in the Western Australian wheat belt (Caccetta et al. 2000). For these purposes the DEMs

were used to determine watershed parameters including ‘upslope area’, ‘upslope cleared

area’ and other factors effecting the formation and spread of salinity. A similar study used

topographic indices, Landsat TM imagery and conditional probability networks to monitor

increasing salinity in Western Australia also used Landsat TM imagery to predict areas at

risk of salinity using decision trees and DEMs to substantiate their data (Kiiveri and

Caccetta 1998). The use of other data sets improves the predictability of some landcovers

and provides an extra element to the diagnosis.


While hyperspectral sensors improve the diagnostic power of remotely sensed data and can

thus be used more independently of other data sets, the absence of spectral features in salt

still makes classification difficult. However, researchers have found that saline soils can be

mapped using other soil or vegetation properties as surrogates. Ben-Dor et al. (2002)

found, using DAIS-7915 hyperspectral scanner data, that soil salinity was correlated with

soil moisture (r = 0.58) in cultivated fields and was able to develop reliable prediction

equations. HyMap hyperspectral data has also been used to map salinity symptoms under

different agricultural environments. The characteristic features of samphire (Halosarcia

pergranulata) and gypsum (associated with salt scalds) were used as indicators of dryland

salinity at Point Sturt in Lake Alexandrina, South Australia (Dutkiewicz et al. 2009).

Similarly, samphire and other halophyte species such as Sea Blite (Sueda australis) and

Sea Barley Grass (Critesion marinun) have been used to map irrigation salinity with

HyMap hyperspectral imagery (Dehaan and Taylor 2002, 2003).

1.1.10 Limitations of Optical Remote Sensing for Soil Mapping

Optical remote sensing can only directly access the surface of materials covering the earth.

This presents a significant limitation in the mapping of soils. For most land uses the upper

surface of the soil is covered by material other than the soil for much of the year. This may

be photosynthetic crops, for example wheat or vines, or crop residues in the form of

stubble and loose material. These surface coverings significantly reduce the amount of

information received by the sensor that is directly about the soil itself. Furthermore, a

comprehensive soil map must consist of an analysis of the entire profile. Thus optical

remote sensing is not a tool to be used in isolation to map soil. The incorporation of other

techniques and technologies is warranted to provide comprehensive understanding of the

soils for the purposes of mapping.

1.1.11 Vegetation Discrimination and Mapping

Remote sensing is an important means by which to map vegetation and landcover on the

earth’s surface. Satellite sensors have long been used to determine the percentage cover of

vegetation and its converse, soil exposure (Bannari et al. 1995). These components of

landcover are important in understanding risk to natural resources of degradation such as

erosion and increasing salinity. Furthermore, in understanding vegetation distribution and

components it is sometimes possible to draw conclusions about the underlying soil

properties (Taylor et al. 2002). It is also important to understand vegetation reflectance and


how it interacts with that of other materials including soil. How vegetation reflectance

interacts with other materials depends on the state of the vegetation, dry or alive, and also

structural differences in vegetation, herbaceous or woody (Skidmore et al. 1997).

Multispectral imagery has long been used to discriminate and map vegetation variables

such as biomass, leaf area index and percent cover. Moreover, it has been used widely to

map vegetation condition by way of greenness indices. However, there are significant

limitations in the usefulness of multispectral imagery in discriminating variations in the

composition of vegetation (Elvidge 1990, Lewis 2000).

Hyperspectral imagery provides advantages over multispectral imagery for sub-pixel

discrimination and mapping of vegetation. The larger number of spectral bands in

hyperspectral data can potentially provide interpretation and discrimination of more sub-

pixel components. Moreover, the band placement more readily enables discrimination of

spectral features, further increasing diagnostic power of the data (Lewis et al. 2000).

There is some disagreement about the spectral regions of the EM spectrum that are useful

in the discrimination of vegetation. Some consider the VNIR provides the best spectral

information for vegetation due to water absorption features in the SWIR masking plant

spectral information (Elvidge 1990). However, studies have challenged this notion, finding

the SWIR valuable for semi-arid vegetation discrimination (Drake et al. 1999, Lewis

2000).

Using airborne multispectral sensor (AMS) hyperspectral imagery, functional components

of vegetation (i.e. trees versus shrubs), differences in species (i.e. Eucalyptus versus other

tree species) and different physiological conditions (i.e. actively growing versus dry litter)

have been adequately mapped (Lewis et al. 2000). Also multispectral satellite sensors such

as SPOT have been used to successfully map forest type, relying on vegetation structure

for discrimination (Xiao et al. 2002).

Furthermore, studies have used the normalised difference vegetation index (NDVI) and

other vegetation indices derived from multispectral airborne remote sensing to infer

variability in the underlying soil (Lamb 2000, Drysdale and Metternicht 2003). Plant

condition strongly influences vegetation reflectance spectra. Water stress, for example, has

been found to change plant spectral response and plant reflectance has been used to

estimate soil water content in cropping systems (Senay et al. 2000). However, this was


largely based on the plant biomass having a strong correlation with plant water which in

turn correlated positively with soil water. The above studies demonstrate that soil variation

and to some degree soil properties can be discriminated using multispectral remote

sensing.

Soil properties have been examined in eucalyptus forests of south-eastern Australia with

remote sensing, terrain data and GIS. Correlations were found between soil properties,

total phosphorus, exchangeable cations and electrical conductivity and spectral reflectance

(Skidmore et al. 1997). However, this study was undertaken in natural forests that were

relatively unaffected by modern agriculture. Thus there were not subject to fertiliser and

pesticide inputs which would significantly affect plant response in relation to soil

variability. Other studies have used remote sensing of vegetation as a surrogate for soil

properties in association with digital terrain data over mono-crop environments (Selige

1998). However, this is more difficult over heterogeneous cropping environments due to

variation in chemical and physical properties across different crop types affecting their

reflective properties (Skidmore et al. 1997).

2.6 Summary

Soil mapping in Australia is well advanced for various regional scale applications in some

parts of the country. However, there is much scope to improve upon the current scale of

mapping and provide a better resource for local applications. Furthermore there are large

areas of the continent where the soils are not well understood and new mapping programs

are likely in the future. Improving upon the scale of current soil maps and providing new

inventories of soils is expensive, labour intensive and time consuming by conventional

mapping methodologies. The application of remote sensing and reflectance spectroscopy

may provide a cost effective and rapid means by which to improve the resolution current

soil mapping and undertake new programs.

The spectral response of soils has been used to predict different properties in a variety of

applications. While some studies have applied reflectance spectroscopy to predicting soils

this has been with soil samples from large geographic extents and little effort has been

given to using the predictions for subsequent mapping. There is much scope to further

examine the use of reflectance spectroscopy and applying it to soil mapping. Furthermore,

the soils of southern Australia provide unique profile and landscape characteristics such as


low nutritional content and strong texture contrast resulting from extensive weathering,

low organic matter content and a high occurrence of salinity and sodicity.

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34

Chapter 3

Spectral Discrimination of Soil Properties

Published as refereed conference paper:


a1172507

Text Box

A NOTE: This publication is included on pages 34-48 in the print copy of the thesis held in the University of Adelaide Library.

a1172507

Text Box

A Summers, D., Lewis, M., Ostendorf, B. & Chittleborough, D.J. (2005) Spectral discrimination of soil properties. In SSC 2005 Spatial Intelligence, Innovation and Praxis: National Biennial Conference of the Spatial Sciences Institute, Melbourne, Australia.

49

Chapter 4

Visible near-infrared reflectance spectroscopy as a

predictive indicator of soil properties

Published as journal article:

Summers, D., Lewis, M., Ostendorf, B. and Chittleborough, D. J. (In Press) Visible near-infrared reflectance spectroscopy as a predictive indicator of soil properties, Ecological Indicators.

4.1 Introduction

The classification, mapping and monitoring of soils is an important underpinning of

modern day natural resource management. Regional scale soil maps are traditionally

produced by dividing the landscape into mapping units through air-photo and landscape

interpretation from which sample sites are chosen to characterise the soils. For regional

planning these maps provide an excellent resource, but they do not provide sufficient detail

for localised soil and land management. Whereas soil variability within each of these

mapping units is often acknowledged in the map and accompanying report, it is not

depicted or quantified. Increasing concern over land degradation, agricultural productivity

and the loss of ecological services has led to a desire for greater understanding of land

resources and processes at scales larger than 1:50 000 scales.

Around the world governments are investing in programs to better understand soil

variability and create soil databases to better inform landscape planning and management

decisions. In South Australia the soils of the agricultural districts have been mapped and

information presented on maps at 1:50 000 and 1:100 000 scale (Soil and Land Program

2007). While these maps provide an excellent regional planning tool, finer spatial

resolution information is required to improve land management decisions at farm scale,

and to assist understanding and modelling of problems such as diminishing biodiversity

and dryland salinity. Unlike the agricultural districts, there is a paucity of data on the

nature and distribution of soils in South Australia’s pastoral zones. The pastoral districts

Chapter 4: Spectral prediction 50

cover large areas and contribute substantially to the State’s economic productivity. These

areas would benefit greatly from improved understanding of soil properties and their

variability as well as vegetation condition, ecology and biodiversity. Recent studies in

Australia’s arid region for example, have called for improved understanding of soil

heterogeneity as inputs for the monitoring of ecology and biodiversity, citing the lack of

spatial resolution as an impediment (Clarke 2008). Similar inputs have been used in other

parts of the world to predict vegetation community distributions (Miller et al. 2002).

Creating these maps and improving the spatial resolution of existing maps to provide

greater detail about soil variability can be prohibitively expensive by traditional soil survey

procedures (Sumfleth and Duttmann 2007) and can only be justified for the most intensive

agricultural systems. Pedotransfer functions have been employed to reduce the expense of

intensive soil mapping by using surrogates that are relatively inexpensive to measure, as

well as to predict less readily measured soil properties. Examples of this include using soil

colour to predict organic carbon content and using mechanical resistance as an indicator of

bulk density and clay content (McBratney et al. 2002). However effective these functions

are for some applications, pedotransfer functions do not provide a direct measurement of

soil properties nor are they provided for all soil properties of interest. Information or

indicators for a wider range of soil properties is needed.

In order to overcome the expense of traditional soil survey and the limitations of

pedotransfer functions, researchers are increasingly turning to remote sensing and, in

particular, reflectance spectroscopy. This form of earth observation can provide useful

indicators for mapping and monitoring many environmental features such as geology and

minerals (Bower and Rowan 1996, Clark 1999), vegetation and soil (Lewis 2000, Ben-Dor

et al. 2002, Sumfleth and Duttmann 2007) and even ecological habitats (Tiner 2004, Bock

et al. 2005). With field and imaging spectrometers becoming increasingly sophisticated,

there is potential for substantial improvement in the speed, reliability and resolution of soil

analysis. Spectral analysis of soil cores with field or laboratory spectrometers could

provide a new means of automated, rapid and objective profile evaluation, following the

approach now being developed for mineral characterisation of geological cores (Mauger et

al. 2004). In addition, new imaging spectrometers offer the prospect of detailed raster-

based mapping of surface soil properties with higher spatial resolution than is possible with

the current approaches.


4.1.1 Spectral Reflectance Variation in Soils

Early studies of soil reflectance spectra over the visible (Vis, 400 – 700 nm), near-infrared

(NIR, 700 – 1300 nm) and shortwave-infrared2 (SWIR, 1300 – 2400 nm) region described

and classified different ‘curve forms’. For example, Condit (1970) identified three types of

curves amongst 285 soils from the United States, characterised by the overall shape of the

spectral response and changes in slope over the wavelength range. However, no attempt

was made to explain the spectral response in relation to physical or chemical properties of

the soils. A more comprehensive study by Stoner and Baumgardner (1981) described five

curve forms amongst 485 soils from the United States and Brazil, and also related specific

absorption features to soil organic carbon and iron oxide content in the soil. However, most

of the more recent research has investigated relationships between the soil properties and

soil reflectance with the aim of predicting the physio-chemical properties of the soil.

The clay mineralogy in soils has been distinguished in several studies using the short wave

infrared (SWIR) region of the spectrum (1300 – 2500 nm) (Islam et al. 2003), and

especially the 2200 nm absorption feature that is characteristic of clays (Ben-Dor 2002).

Soil texture and clay content have also been estimated from reflectance spectra, based on

the depth of specific clay absorption features (Ben-Dor and Banin 1995b) and statistical

analysis of the whole curve form (Brown et al. 2006, Viscarra Rossel et al. 2006). In a

limited study conducted in South Australia, relationships between soil texture and

laboratory and hyperspectral image spectra from the Barossa Valley region were described

(Ryan and Lewis 2000, 2001).

Early studies observed that increasing soil organic carbon (SOC) lowered albedo across the

whole visible, shortwave infrared and near infrared (Vis-NIR-SWIR) reflectance spectrum

(Stoner and Baumgardner 1981, Henderson et al. 1992). However, there appears to be a

threshold of 2% organic carbon below which the effect of SOC on soil reflectance is

greatly reduced (Baumgardner et al. 1985). SOC has been predicted from various portions

of the Vis-NIR-SWIR largely because it contains so many components. These components

include compounds such as lignin (e.g. 2050, 2351 nm), cellulose (e.g. 1370, 1725, 2347

nm), pectin (e.g. 1320, 1582, 1761, 2111 nm) and humus (e.g. 1929, 1932 nm), which are

2 The SWIR is included in the NIR in some disciplines such as chemistry and reflectance spectroscopy


optically active across this spectral region and are thought to overlap in places (Elvidge

1990, Ben-Dor et al. 1997). SOC has been reliably predicted from both laboratory

reflectance spectroscopy and image spectroscopy (Ben-Dor et al. 2002, Daniel et al. 2004).

Some studies have focused on the VIS and NIR regions of the reflectance spectra,

(Krishnan et al. 1980, Vinogradov 1981, Daniel et al. 2003, Brown et al. 2006) whereas

others have used the SWIR region to predict SOC (Morra et al. 1991, Ben-Dor and Banin

1995b, Viscarra Rossel et al. 2006). An Australian study was able to predict SOC from

reflectance spectroscopy in the spectral range 1702 – 2052 nm in a simultaneous

determination of moisture, organic carbon and total nitrogen (Dalal and Henry 1986).

Iron oxide content of soils has been predicted from different spectral regions of the VIS-

NIR-SWIR, based on characteristic absorption features at 550 – 650 nm, 750 – 950 nm

(Ben-Dor and Banin 1995a) and 1406 and 2449 nm (Ben-Dor et al. 2006). The

concentration of iron oxide as wind blown dust on mangrove foliage has been predicted

using features at wavelengths: 518, 746, 927, 1261 and 1402 nm (Ong et al. 2003). Studies

have also found that SOC as low as 1.7% can severely decrease the influence of iron oxide

on the reflectance spectra in the VIS and NIR regions, and particularly decrease the

definition of the 900 nm absorption band (Galvao and Vitorello 1998).

The detection of soil carbonate in soils is complicated by its characteristic absorption

feature shifting to longer and shorter wavelengths depending on the impurities present

(Ben-Dor et al. 1999, Clark 1999). Furthermore, the depths of these spectral features are

dependent not only on the concentrations present but also on particle size and porosity (van

der Meer 1995). Despite this, correlations between absorption feature depth and carbonate

concentration have been established (Ben-Dor and Banin 1990). Correlations have also

been established between carbonate concentration in soil and reflectance spectra based on

changes in colour and albedo, (Ben-Dor and Banin 1995b, Ben-Dor et al. 1999).

The aim of this study was to determine the extent to which high-resolution reflectance

spectra in the visible, near infrared and shortwave infrared regions (400 – 2500 nm) could

be used as an indicator to predict selected surface soil properties. An increasing number of

studies have examined the reflectance properties of soils from temperate, Mediterranean

and tropical regions with moderate to high fertility properties but evidence from low

fertility soils is still sparse. In this study we examine soils from a South Australian region

that has a unique array of profile and landscape characteristics such as low nutritional


content and strong texture contrast profile due to extensive weathering, low organic matter

content and a high occurrence of salinity and sodicity. The economic and environmental

importance of understanding variability in landscapes like these is becoming increasingly

accepted and has been highlighted by recent research (Lyle and Ostendorf In Review).

While some previous studies have applied mid-infrared spectroscopy (2500 – 25 000 nm)

to Australian soils (Janik and Skjemstad 1995, Dunn et al. 2002), we examine the optical

visible-near infrared range within which airborne and satellite-based imaging instruments

operate (400 – 2500 nm). The study is a precursor to hyperspectral image mapping of soils

in South Australian agricultural environments. For this reason, we focussed on properties

that are important determinants of soil agricultural capability and the extent to which they

can be simultaneously quantified and predicted from high-resolution reflectance spectra. In

addition, we aimed to identify the spectral regions or features that are most influential in

soil property discrimination, in order to guide future hyperspectral image enhancement and

feature-extraction methodologies. Many of the published spectral analyses of soils have

focussed on single soil properties. Here we address the combined spectral expression of

four key properties that are widely used to assess the agricultural and ecological capability

of soils. Moreover, we examine the proposal that reflectance spectroscopy could be used as

a cost effective means to improve the resolution of soil data for local and regional

inventories. Therefore, we sampled soils to encompass the range of types and variability in

properties that might be encountered in a regional mapping study. Most prior spectral

studies have assembled collections of soils from geographically disparate areas to provide

a wide range of characteristics for analysis. However, as an alternative (or complement) to

traditional soil survey, the methodology needs to be able to predict properties within a

limited region where variation is less pronounced. To further demonstrate the utility of

Vis-NIR reflectance spectroscopy for supplementing soil maps, kriging was used to create

continuous raster layers of the predicted soil properties.

4.2 Methods

4.2.1 Study site and sample collection

Soil samples were collected from the top 2 cm of 300 randomly selected sites in the

Jamestown-Belalie district, approximately 200 km north of Adelaide, South Australia

(Figure 4.1) (33.20611o S, 138.20611o E). The northern third of the study site is dominated


by a north-south trending range of hills. A broad valley extends into the south-eastern part

of the study site and is interrupted by another, smaller north-south ridgeline. Several small

ephemeral creeks also traverse the study site, some originating in the hills to the north-east

and some outside the study area and running through the valley. Landuse in this area is

predominantly rain fed cereal cropping in the low lying areas and perennial pasture in the

hills.

Figure 4.1: Jamestown study site, 200 km north of Adelaide, South Australia. Polygons show Common

Soils from the Land and Soil Spatial Data for southern South Australia (Soil and Land Program 2007),

soil sample sites marked with black dots. The legend describes the soil Order from the Australia Soil

Classification (in bold) (Isbell 2002) as well as the soil description from the Land and Soil Spatial Data

for southern South Australia.

Soils have been mapped at 1:100 000 by the Department of Water, Land and Biodiversity

Conservation, South Australia (Soil and Land Program 2007) and are predominantly

Chromosols (Isbell 2002), the key profile characteristic being a strong texture contrast

between A and B horizons. These are described as Xeralfs within the Soil Taxonomy (Soil

Survey Staff 1999). Less widely distributed soils include Dermosols that have structure in

A and B horizons and a gradational texture profile, Calcarosols that have carbonate in the

profile and Rudosols which include shallow skeletal soils on rock. Textures of the B

horizon are often heavy clays that are almost invariably underlain by a carbonate-rich clay

horizon. In higher rainfall areas and some of the ranges there are isolated patches of


Kurosols, which are acidic soils with a strong texture contrast between the A and B

horizons.

4.2.2 Laboratory soil analysis

Proportions of clay were calculated from particle size analyses using the hydrometer

method (Gee and Bauder 1986, Sheldrick and Wang 1993). It should be noted that this

methodology calculates clay fraction as determined by size (< 2 µm) and not mineralogy.

Therefore, other fine material (< 2 µm) such as iron oxides and silicates could be measured

in this fraction if it is present in the soil. The calcimeter method (Allison and Moodie 1982,

Nelson and Sommers 1986) was used to measure the carbonate concentration in the soil.

Organic carbon was determined by a modification of the Walkley and Black’s titration

method as outlined by Nelson and Sommers (1986). Iron oxide content was measured by

the sodium dithionate-citrate method (Olson and Roscoe 1986, Ross and Wang 1993).

4.2.3 Reflectance spectra

Prior to spectral measurement, soil samples were air dried in an oven at 60oC for 72 hours

and then passed through a 2mm sieve. Samples were placed in a Petri dish and screeded so

that the entire surface of the soil sample was level with the rim of the dish, thus

guaranteeing a uniform sample depth of 10mm and ensuring that reflectance measurements

recorded the soil surface and not the sample background. Soil spectra were collected using

a FieldSpec Pro spectrometer (Analytical Spectral Devices) that measures reflectance in 3

to 10 nm bandwidths over the range 350 – 2500 nm. A high-intensity contact probe was

used to optimise incidence and reflectance angles, minimise illumination differences and

atmospheric attenuation of the signal and allow for precise identification of the area

sampled. The quality of the spectral measurements was reviewed and noisy portions (350 –

400 nm) of the spectra were removed prior to analysis. The average of ten spectra for each

sample was used in subsequent statistical analysis.

4.2.4 Statistical analysis

The objective of the statistical analysis was to determine whether the reflectance spectra

could be used to predict the chosen soil properties, and to identify the spectral regions

contributing to the prediction. Multiple linear regression is a common multivariate tool

which, at its simplest level, forms a model that specifies the relationship between a

response variable (Y) and a set of dependent variables (X). However, multiple linear


regression suffers from some significant limitations, the most important being the

overfitting of data when there are large numbers of highly correlated variables

(significantly more than the number of samples), as is often the case with hyperspectral

reflectance measurements. Partial least squares regression was developed in order to

overcome this limitation (Wold et al. 1983, Otto and Wegscheider 1985), through the

incorporation of aspects of principal components analysis and multiple linear regression.

More specifically, partial least squares regression finds a series of components or latent

vectors that provide a simultaneous reduction or decomposition of X and Y such that these

components explain, as much as is possible, the covariance between X and Y. This step

approximates principal components analysis, although in the latter the components only

explain variation in X and do not necessarily have any bearing on Y. This is then followed

by regression where Y is predicted from the reduction of X (Abdi 2003). The number of

latent vectors are chosen by a process of cross validation which outputs a root mean square

error (RMSE), with the aim of minimising both the number of latent vectors and the

RMSE. Partial least squares regression has been used previously over different spectral

ranges (Vis-NIR-SWIR-MIR) for the prediction of soil properties with varying degrees of

success (Janik et al. 1998, Walvoort and McBratney 2001, McCarty et al. 2002, Cozzolino

and Morón 2003, Ong et al. 2003).

Statistical analysis was carried out using The Unscrambler (Camo Software AS).

Calibration data was mean centred and cross-validation was used to determine the

minimum number of PLS factors required. A large proportion of the samples recorded no

carbonate in the laboratory analysis, with the result that the carbonate distribution amongst

the 290 samples was strongly skewed. To provide a range of values more suitable for

statistical analysis, the data set for carbonate analysis was reduced to 75 by randomly

selecting samples that returned zero carbonate in the laboratory analysis to include in the

statistical analysis along with all of the samples that contained higher carbonate levels.

Cross-validation was carried out using the ‘leave-out-one’ method where one sample is

systematically left out from each cycle of the regression until all the samples have been

excluded once. With different sample numbers for each of the soil properties examined,

this method of validation was chosen to provide for a uniform approach for all of the

analyses.


The accuracy of the prediction models was tested with the residual predictive deviation

(RPD) which is the ratio of the standard error of performance to the standard deviation of

the reference data (Williams 2004). Interpretation of the RPD differs amongst authors and

applications. However, it is generally accepted that when applied to the prediction of soil

properties values below 1.5 indicate a poor predictive model, between 1.5 and 2.0 is

acceptable and greater than 2.0 is considered good (Chang et al. 2001, Dunn et al. 2002,

Cozzolino and Morón 2003, Janik et al. 2007). Values below one are considered

inadequate and indicate that the mean of the observed would be a better predictor

(Williams 2004).

4.2.5 Spatial Prediction

The kriging function within the spatial prediction program VESPER (Minasny et al. 2005)

was used to create raster surfaces of the measured and predicted surfaces. Local

variograms were used for clay content, organic matter content and iron oxide content while

low sample density required global variograms were used for carbonate content. Maps

were created with 100 m cell size.

4.3 Results and Discussion

4.3.1 Soil Properties

The percentage of clay in the samples ranged from 5% to 36% (Table 4.1), corresponding

to textural classes loamy sand, sandy loam, loam, silty loam, silty clay loam, clay loam and

clay (McDonald and Isbell 1990). Values for organic carbon were between 0.3 and 2.9%,

carbonate concentrations 0 to 26% and iron oxide concentrations in the range 0.8% to 3%.

Table 4.1: Summary of laboratory results from chemical and physical analysis.

CC

Clay content

(%)

OM

Organic Carbon (%)

IO

Iron oxide

(%)

CO3

Carbonate content

(%)

No. of samples 237 228 229 75

Mean 16.32 1.5 1.5 2.65

Std. deviation 5.42 0.53 0.37 5.37

Minimum 4.97 0.31 0.79 0.0

Maximum 35.98 2.9 3.05 25.67


4.3.2 Soil Spectral Characteristics

The overall form of the spectra for all the soils was quite similar. Clay (2200 nm) and

water (1400 nm, 1900 nm) absorption features were present in all spectra while there were

differences in the albedo (intensity) and in the iron oxide (850 – 900 nm) and carbonate

(2300 nm) spectral features amongst the samples.

Figure 4.2 presents mean spectra for each of the quartiles from the laboratory analysis of

clay. The quartiles were determined by dividing the samples into four groups based on

their clay content, with each group containing 25% of the total range. Noteworthy is the

increasing depth of the absorption features at approximately 1400, 1900 and 2200 nm with

increasing clay content. These absorption features are caused by bending and stretching in

the O-H bonds of free water (1400 nm and 1900 nm) and the Al-OH lattice structure in

clay minerals (2200 nm) (Ben-Dor 2002, Viscarra Rossel et al. 2006). Illitic and

montmorillonitic clays dominate the study site area and the nature of the spectra supports

this, as the single symmetrical absorption at 2200 nm is diagnostic for these clays. Other

noticeable differences are evident in the VIS and NIR regions but are likely to be the result

of other factors, such as SOC or iron oxides.

Figure 4.2: Mean spectra of quartiles for percent clay.

Figure 4.3 shows the mean soil spectra of the quartiles from laboratory analysis for SOC.

There is a clear trend with increasing SOC: the spectra have increased slope around 800

nm and lower reflectance across the 400 – 2500 nm spectral range, shifts which have been

observed in other studies (Krishnan et al. 1980, Galvao and Vitorello 1998). In addition to

variation in SOC content, differences in albedo and the slope between 400 nm and 800 nm


have been attributed also to the stage of organic carbon (OM) decomposition (Ben-Dor et

al. 1997). The spectra here show increased absorption depth at 2327 nm and 2357 nm,

features which have been attributed to differences in the OM composition (Ben-Dor et al.

1997). Although not investigated here, soils with a higher vegetative load will contain SOC

over a range of decomposition stages.

Figure 4.3: Mean spectra of quartiles for soil organic carbon.

Figure 4.4 depicts the mean spectra for the quartiles of carbonate content. The carbonate

absorption features were slight and limited to one spectral region (2325 nm). Although this

appears to be the only spectral expression of carbonate in our samples, previous studies

have used a range of wavelengths (1800 nm, 2350 nm and 2360 nm) to predict calcite in

soils (Ben-Dor and Banin 1990). Other spectral variations amongst our samples can only

be attributed to other soil properties. The iron oxide quartiles in Figure 4.5 demonstrate

increasing definition of the iron oxide features in the VIS-NIR. As the iron oxide

concentration increases, there is an increase in depth of absorption from 400 nm to 550 nm

and in the broad feature at 900 nm indicating that goethite dominates the samples rather

than hematite.


Figure 4.4: Mean spectra of quartiles for carbonate concentration.

Figure 4.5: Mean spectra of quartiles for iron oxide content.

4.3.3 Prediction of Soil Properties

Table 4.2 presents the efficiency criterion (E), root mean square error (RMSE) and

regression coefficients (R2) obtained from each partial least squares analysis. The first two

PLS loading weights for each analysis in Figures 6, 7, 8 and 9 demonstrate the relative

importance of spectral regions in the prediction of each of the soil properties. Negative

peaks in the loading weight graphs indicate spectral regions that correlate positively with

the prediction and positive peaks are those areas that correlate negatively with the

prediction.


Table 4.2: Sample numbers, residual predictive deviation (RPD), root mean square error (RMSE) and

R2 results for data sets.

Soil property Samples

(n) Factors RPD

RMSE

(%) R2

CC (%) 237 10 2.0 3.13 0.66

OC (%) 228 8 1.8 0.35 0.57

IO (%) 229 10 1.7 0.23 0.61

CO3 (%) 75 5 2.1 2.90 0.69

With ten prediction factors or latent vectors selected for the analysis (Table 4.2), 66% of

the variation in clay content was explained by the partial least squares regression model,

returning a RMSE of 3.13. An RPD of 2.0 indicates that the prediction was acceptable and

substantially better predictor than the mean of the observed clay contents. In Figure 4.6 the

first loading weight (PC1) is dominated by the clay absorption feature at 2200 nm and the

features at 1400 and 1900 nm. These three features are all related to the bending and

stretching of O-H bonds in the lattice minerals and water molecules, directly and indirectly

associated with the clay minerals. The 2200 nm region is specifically related to the

symmetric absorption feature that is diagnostic of the illite and montmorillonite that

dominate the clays in these soils. For all these spectral regions, increasing clay content

would result in more pronounced absorption features. These spectral regions were also

discriminants for field textural classes in soils from the same geographic region (Summers

et al. 2005). The second loading weight was dominated by the visible (400 – 700 nm) and

a portion of near infrared region (700 – 1300 nm) with some contribution from the same

regions as observed in the first loading weight. The importance of the visible spectral range

in this result indicates that there may be some co-variation between the clay content and

the colour of the soil. There is also a strong influence in the first and second loading

weights, starting at 2300 nm and increasing in contribution through to 2500 nm. This is the

initial stages of a water absorption feature that continues out of range to 2800 nm.


Figure 4.6: Spectral loading weight graph for the prediction of clay content.

The analysis explained 57% of the variation (Table 4.2) in SOC using eight prediction

factors with an RMSE of 0.35. The RPD value of 1.8 is evidence of an acceptable model

although it could be improved with different calibration strategies (Chang et al. 2001). The

first loading weight was dominated by a relatively broad region extending from 550 nm in

the visible to 1000 nm in the NIR, with a maximum contribution near 700 nm (Figure 4.7).

Increased SOC generally produces visibly darker soils and it is likely that this contributed

to the prediction here. The second loading weight is dominated by a couple of peaks at

2100 and 2300 nm. Other studies have found these spectral regions to be associated with

lignin and humic acids and important in the prediction of SOC (Ben-Dor et al. 1997).

Figure 4.7: Spectral loading weight graph for the prediction of soil organic carbon content.

Substantially fewer samples were available for the carbonate analysis than for the other

soil properties (Table 4.1), but the coefficient of determination was the highest for all the

soil properties in the study (0.69) using 5 prediction factors (Table 4.2). The analysis also

returned an acceptable RPD value (2.1) and a reasonable RMSE (2.9) (Table 4.2). The first

loading weight (Figure 4.8) is dominated by a peak at 2300 nm which is directly associated

with carbonate in reflectance spectra. There is also some influence from a peak at 1900 nm


that extends into a ‘plateau’ to around 2100 nm. The second loading weight shows a broad

peak from 600 to 1100 nm indicating some influence from red visible wavelengths to the

near infrared range. A previous study of a similar set of soils also found that the visible

region contributed to discrimination of carbonate classes but that the discrimination was

dominated by absorption features associated with water (1900 nm), clay (2200 nm) and

carbonate (2300 nm) (Summers et al. 2005).

Figure 4.8: Spectral loading weight graph for the prediction of carbonate content.

The prediction of iron oxide explained 61% of the variability in the samples using ten

prediction factors with an RMSE of 0.23 (Table 4.2). The RPD value was 1.7, which is the

lowest of all the soil properties examined in this study although still within the acceptable

range. The first loading weight (Figure 4.9) shows the range from 400 to 1100 nm to be

most influential in the prediction. Within this range there are two maximum ‘peaks’ one at

550 nm and one at 900 nm, both regions associated with spectral characteristics of iron

oxide species. The second loading weight is dominated by a portion (400 – 550 nm) of the

visible range, associated with the blue and green, and peaks at 1900, 2200 and 2300 nm,

associated with water, clay and carbonate respectively.

Figure 4.9: Spectral loading weight graph for the prediction of iron oxide content.


4.3.4 Mapping of Predicted Soil Properties

The kriged geographic distributions of the measured and predicted soil properties are

displayed in Figure 4.10. Comparison between the measured and predicted soil properties

demonstrate similar patterns and value ranges for the soil properties examined. The two

maps of clay content show lower values in the hills towards the north-east and in the sandy

soils of the south-west although overall the area has limited variability. The most

substantial difference between the two maps is in the centre where the predicted map

demonstrates less variability. Organic carbon shows the greatest variation between the

measured and predicted maps of all of the soil properties examined. However despite that,

the overall pattern between the two maps is consistent. In both maps organic carbon

content is lower in the valley areas which are dominated by cropping, while in the hills,

which are predominantly pasture, there is a build up of organic carbon. The small band of

sandy soils in the south-east has unexpectedly high organic carbon contents although this

too could be the product of the pasture and forestry landuse in that area.

Figure 4.10: Spatial distribution of measured and predicted soil properties following Kriging.


There are some minor differences between the measured and predicted carbonate maps,

however the same trend is evident in both. The central portion of each map shows higher

carbonate contents, particularly along the southern edge. Both the measured and predicted

carbonate maps correspond well with polygons classed as calcareous within the Land and

Soil Spatial Data (Figure 4.1). The iron oxide maps show a good match between the

measured and predicted soil properties, each demonstrating the same pattern and with a

few small differences in the centre of the map. The area is dominated by red-brown earths

and predicted iron oxide content reflects this with a relatively high and even distribution

across the study site. The lower iron oxide levels in the south-west corner are associated

with the small band of sandy soils found there.

4.4 Conclusion

Visible–near infrared reflectance spectra collected under controlled laboratory conditions

were employed as an indicator for the prediction of selected soil properties. Partial least

squares regression overcame the collinearity problems associated with large numbers of

highly correlated variables and relatively small sample numbers. We have shown that it is

possible to predict clay content, soil organic carbon, iron oxide content and carbonate

content from reflectance data produced with a high-resolution laboratory spectrometer.

Furthermore, all of the samples were collected from the same geographical area in order to

test prediction of soil properties over a naturally occurring range and provide a prediction

that can be related to a regional image analysis. The predicted soil properties have also

been examined geographically in relation to existing soil maps with some discussion of

how they relate to the landscape and the usefulness of the method in future soil mapping

projects. However, it should be noted that recalibration of PLS predictive functions would

be required for different soil types and mineralogy.

Carbonate and clay content were best predicted followed by iron oxide and organic carbon.

Validation R2 for all analyses was above 0.5 and the RPD was acceptable for all soil

properties. We showed the utility of particular regions of the 400 – 2500 nm spectrum for

prediction of clay content (1900 and 2200 nm), SOC (600 – 900 nm), iron oxides (400 –

1100 nm) and carbonate (1900 – 2300 nm). This demonstrates the ability to use this

methodology as an indicator for rapid and reliable soil mapping. Laboratory analyses of

soil samples in support of traditional survey methods are expensive and time consuming.

Field and laboratory measurement potentially offers a rapid, cost effective method for


prediction of soil properties. Such studies could also be expanded to include the analysis of

whole profiles and provide a more comprehensive understanding of the solum. Moreover,

the results from this study can inform subsequent image studies which would allow the

application of similar and related methodologies to spatially continuous remotely sensed

imagery. However, further studies on different soils are required to confirm the efficiency

of these predictors as indicators of soil properties and variability.

The Land and Soil Spatial Data in this region is produced at a relatively broad scale (1:100

000) and soil units are depicted with discrete polygons units. This provides a valuable

regional planning tool but lacks the spatial resolution for finer scale applications. For

example, the soil properties represented in any one polygon are, in some cases, only 50%

reliable (Soil and Land Program 2007). This is largely the result of scale and the absence

of soil variability depiction within polygons. The predictions of soil properties show that

reflectance spectroscopy could be used to improve the spatial resolution of soil inventories

such as these. Furthermore, we have demonstrated how simple kriging can be used to

create a raster maps of the predicted soil properties and that these maps are comparable to

the measured soil properties. It should be noted that there is room to improve the prediction

accuracy of the reflectance spectroscopy in this study and achieving higher accuracy would

benefit any soil survey carried out with these techniques. However, the improved spatial

resolution available from greater sampling density at reduced costs could counteract some

of the expected error.

While this study examines only surface soils, the spectral methodology would need to be

extended to the profile to fully supplement traditional soil survey. Vis-NIR reflectance

spectroscopy has been successfully used to catalogue and classify geological cores and in

situ soil profiles (Mauger et al. 2004, Ben-Dor et al. 2008) and a combination of those

techniques with the ones used here could provide a new methodology for complete

description of the soil profile. These combined methodologies could be used to supplement

traditional soil survey with the aim of improving the resolution of current soil mapping

programs and to expand soil mapping to areas that are currently excluded due to economic

imperatives such as arid and pastoral zones. It is also possible that the sampling density

could be increased to the point where raster based maps could be produced at reliably fine

scales.


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70

Chapter 5

Unmixing of soil types and estimation of soil exposure

with simulated hyperspectral imagery

Submitted for journal publication:


5.1 Introduction

Remote sensing is useful for mapping and monitoring many environmental features

including geology, minerals (Bower and Rowan 1996, Clark 1999), vegetation and soil

(Lewis 2000, Ben-Dor et al. 2002, Sumfleth and Duttmann 2007) and even ecological

habitats (Tiner 2004, Bock et al. 2005). With increasing sophistication of field and imaging

spectrometers, there is potential for substantial improvement in the speed, reliability and

resolution of inventory and monitoring of natural and agricultural systems. New sensors

offer the prospect of detailed raster-based mapping of land surface characteristics with

higher spatial resolution and variation than is possible with the current approaches.

Some hyperspectral image studies to discriminate and map soils in agricultural regions

have been conducted (Ben-Dor et al. 2002, Dehaan and Taylor 2003, Dutkiewicz et al.

2003, Taylor 2004), but many of them suffer from a common limitation. Unless the land is

fallow or recently ploughed, some degree of vegetation, either actively growing or as crop

residue, obscures the soil from the imaging instrument (Metternicht and Zinck 2003). In

Australia the current best practice in croplands employs a minimum tillage regime that,

where possible, minimises soil disturbance. Thus, under a well-managed agricultural

system there is little exposure of soil to allow for unobscured remote sensing.

Studies aiming to map soil types in situations with partial vegetation cover typically use

spectral unmixing methods to identify materials in mixed pixels (Asner and Heidebrecht

2002, Alemie 2005, Lu and Weng 2007, Zhang et al. 2007). Linear mixture analysis is

based on the assumption that the spectrum of a pixel is a weighted linear combination of

Chapter 5: Spectral unmixing with simulated data 71

the spectra of materials within the instantaneous field of view; spectral contributions for

the different materials are proportional to their abundances (Settle and Drake 1993,

Dennison and Roberts 2003). Endmembers, or spectra of ‘pure’ materials in the image,

theoretically representative of all materials in a scene, are used as inputs into the unmixing

process. Such methods result in estimations of fractional abundance in the form of a

greyscale image for each input spectrum. Errors in the unmixing occur when the number of

endmembers approach the spectral dimensions of the image, when endmembers are poorly

selected and not sufficiently distinct from one another, or are not sufficiently representative

of materials in the image (Malenovsky et al. 2007).

Differentiating soils and non-photosynthetic plant residue is difficult because of the

spectral similarity of the two materials (Daughtry 2001, Nagler et al. 2003). Photosynthetic

vegetation has a unique spectral signature in the visible and near-infrared (400 – 1000 nm)

that is not present in non-photosynthetic vegetation, making it much easier to differentiate

(Daughtry et al. 2005, Daughtry et al. 2006). Furthermore, studies have found variable

responses from the mixtures of different soils with the same cover type (Nagler et al.

2003). Photosynthetic vegetation cover under 30%, as typically found in arid and semiarid

regions, appears to have little effect on the determination of soil type from hyperspectral

data, but increasing plant cover severely limits the ability to accurately model soil and its

exposure (Okin et al. 2001). In addition, spectral confusion may occur when mixtures of

soil and vegetation cover mimic the spectral characteristics of some soil types with no

vegetation cover, or where the same level of plant cover on different soils produces

variable spectral responses (Okin et al. 2001).

To fully utilise hyperspectral imagery for soil studies there is a need to understand the

combined reflectance of both the soil and the cover materials as well as the pure

endmember spectra. The spectral response of a soil, even with a well defined spectral

expression, is a function of the physical constituents as well as the exposure of the soil to

the sensor. In situations with partial soil exposure variations in spectral response of, for

example, the depth of the clay absorption feature can be attributed to varying clay contents

as well as differing proportions of soil exposure. Research is needed to clarify the

influences of variable plant cover on spectral sensing of different soil types, and to identify

limits to the detection different soil types under these conditions.


Laboratory-based reflectance spectroscopy is an increasingly popular method for soil

sample analysis and has the potential to greatly improve speed of measurement. Most

successful prediction of soil properties, has been based upon high spectral resolution

reflectance spectroscopy of prepared samples in the laboratory or exposed soils in the field

(Viscarra Rossel et al. 2006b). These studies have included the spectral ranges of ultra

violet (UV) (200 – 400 nm), visible (vis) (400 – 700 nm), near infrared (NIR) (700 – 1300

nm), short wave infrared (SWIR) (1300 – 2500 nm) and mid infrared (2500 – 25 000 nm)

and in some cases different combinations of these ranges (McCarty et al. 2002, Cozzolino

and Morón 2003, Islam et al. 2003, Viscarra Rossel et al. 2006a). It should be noted that

SWIR is a remote sensing term and this range is typically included in the NIR in studies

relating to reflectance spectroscopy.

Unlike image based remote sensing conducted from airborne and satellite platforms,

samples are generally small, discrete units that are examined in the laboratory, often after

some form of preparation. The illumination of samples is achieved with an active source;

for visible near-infrared analysis this is typically a halogen light. Like remote sensing,

reflectance spectroscopy allows for the rapid examination of materials, and in the case of

soil analysis, eliminates much of the laboratory work usually associated with conventional

measurement. Reflectance spectroscopy also eliminates many of the complications

associated with remote sensing, such as atmospheric attenuation. Studies have found it

useful for the determination of soil properties including clay content, carbonate, organic

matter, iron oxide, cation exchange, pH and many more (Janik and Skjemstad 1995,

McCarty et al. 2002, Cozzolino and Morón 2003, Viscarra Rossel et al. 2006a).

Reflectance spectroscopy analysis is typically performed on isolated samples, and the

applicability of findings to imagery is, in some cases, limited. However, some studies have

attempted to apply laboratory spectra to problems encountered with image based remote

sensing. Differentiation and quantification of soil, vegetation and crop residue has been

carried out using laboratory reflectance spectra of material combinations within controlled

experiments. These studies have used wavelength-specific vegetation indices, including the

normalised difference vegetation index (NDVI), soil adjusted vegetation index (SAVI) and

the cellulose absorption index (Nagler et al. 2000, Daughtry 2001, Nagler et al. 2003).

Furthermore, laboratory-measured spectra have also been used to create artificial images

for testing and retrieval of spectral mineral components using image analyses such as


spectral mixture analysis (Hussey 1998). These laboratory based techniques and artificial

image analyses have been useful for evaluating and comparing analytical techniques,

allowing determination of pixel composition and accuracy of results without the

requirements of extensive field work.

This study examined the vis-NIR-SWIR (400 – 2400 nm) spectral expression of different

mixes of vegetation cover and surface soils from a southern Australian agricultural region

and the ability to distinguish material abundances and soil types with spectral unmixing. In

particular the study aimed to examine the extent to which soil exposure could be reliably

quantified from variable mixes of soils with photosynthetic and non-photosynthetic

vegetation cover. Furthermore, we aimed to examine the influence of soil spectral

characteristics on the estimation of abundance and the degree to which different soil types

can be isolated from mixed and pure pixels using linear mixture analysis.

The ability to accurately estimate soil exposure and identify soil types was evaluated

through linear unmixing of spectra derived from controlled mixes of four different soils

and three plant cover types. The linear mixture analysis was applied to two types of

artificial hyperspectral imagery: a ‘laboratory image’, created from physical mixes of

various soils with different vegetation, and a ‘virtual image’ created by weighted linear

combinations of pure soil and vegetation spectra. The virtual image was seen as a control

for the spectra of physical mixtures of soil and vegetation, in that the mixing proportions

were precisely known and the mixture of spectra was strictly linear. Further to this,

comparison of the virtual and laboratory image results was included to determine the utility

of ‘virtual’ images in future investigations.

5.2 Materials and methods

5.2.1 Soil and vegetation samples

Four soils, two photosynthetic vegetation types and a non-photosynthetic crop residue were

chosen to simulate the range of soils and cover types commonly found in natural and

agricultural settings in southern Australia.

Soils for the study, each with differing physical and chemical properties, were

representative of surface horizons from the Monarto agricultural region, 50 km east of

Adelaide, South Australia. This region consists mostly of Chromosols and Calcarosols in


the Australian Soil Classification (Isbell 2002) which translate roughly as Xeralfs and

Calciargids or Calciorthids respectively in the Soil Taxonomy (Soil Survey Staff 1999).

The soils were a sodic clay, loam, silty loam and a clay loam. Samples were analysed for

particle size fractions (Gee and Bauder 1986) carbonate content (Allison and Moodie

1982), organic carbon content (Nelson and Sommers 1986) and free iron oxide content

(Ross and Wang 1993) (Table 5.1).

Foliage from a native Australian Eucalyptus tree (Spotted Gum, Eucalyptus maculata H.)

and a perennial horticultural tree (orange, Citrus sinensis L.) was used to provide

photosynthetic vegetation cover for the experiment, while dry crop residues of agricultural

field pea (Pisum sativum L.) provided samples of non-photosynthetic vegetation. These

vegetation types were chosen to represent native vegetation (eucalyptus), irrigated

horticulture (orange) and the most prevalent non-photosynthetic material in southern

Australian broad acre agricultural landscapes (crop residue).

Table 5.1: Laboratory measured soil properties of four soils used in the study.

Soil Clay

(%)

Carbonate (%)

Iron Oxide

(%)

Organic Carbon (%)

Munsell Soil Colour

Sodic Clay 32.2 0.2 1.1 0.2 5 YR 5/6

Loam 11.4 10.7 0.6 1.6 10 YR 5/3

Silty Loam 18.8 23.3 0.7 0.6 2.5 Y 7/2

Clay Loam 29.2 0.0 0.7 1.5 10 YR 3/3

5.2.2 Collection of spectra and image creation

Prior to spectral collection, soils were air-dried and sieved to 2 mm. Soil samples were

placed in a 200 x 100 x 20 mm tray and the soil surface was screeded level to the rim of

the tray to provide a uniform soil depth of 20 mm. Air drying and sieving the soil removed

some of the complexities that would be encountered in a traditional image scene analysis

enabling the study to focus on the spectral distinction of endmembers. Furthermore,

replicating the micro-variability of soil properties such as micro topography and surface

crusting was deemed unrealistic for a laboratory experiment such as this. Also, most soil

image studies carried out in southern Australia would focus on data collection at times of

peak soil exposure when the soil is very dry.

Spectra of the orange and Eucalyptus foliage were measured within one hour of collection,

while the dry field pea was collected several days prior. Eucalyptus and orange leaves were


placed three layers deep and overlapping the soil surface to cover an area of 100 cm2. This

was found sufficient to prevent light transmission through the leaves and thus deemed

adequate to ensure no spectral interference from the soil below. Field pea stalks were glued

together on a thin piece of plastic to cover an area of 100 cm2: both the plastic and the glue

were tested and found to be spectrally featureless. The stalks were placed in layers to

provide complete coverage and prevent the transmission of light through the stalks to

ensure spectral sampling was not influenced by the underlying soil. Samples of plant and

residue cover were placed in small troughs within the soil to ensure a level sampling

surface between the different materials. Replicating variations in leaf orientation was not

attempted, partly to focus on the spectral characteristics but also as a practical measure.

The orientation of field pea stalks did mimic that of crop residue in a typical agricultural

environment in southern Australia at the end of summer.

Spectral measurements were made with an Analytical Spectral Devices FieldSpec Pro

Spectrometer, a 2150 band sensor which collects data between 350-2500 nm. It has a

sampling interval of 1.4 nm in the 350 -1100 nm range (FWHM = 3 nm) and of 2 nm in the

1000 – 2500 nm range (FWHM = 10 -12 nm). The spectrometer was calibrated against a

white Spectralon reference panel to prevent drift and ensure consistency across

measurements. Wavebands below 400 nm were considered noisy and removed, reducing

the number of bands to 2101, spanning the range 400-2500 nm. Each spectrum used in the

analysis was the averaged combination of 10 measurements collected with the

spectrometer. An ASD high intensity reflectance probe (A122000) with internal halogen

lamp was used for data collection. This probe is configured with the optical fibre

approximately 20o off nadir and 60 mm above the sample, and the halogen lamp in the

nadir position. The probe was fitted with a field of view (FOV) limiter which provided a

sample spot size of 30 mm diameter.

To collect spectra for the laboratory image the probe was placed in a clamp so that the field

of view was filled by the plant cover in the tray. The tray was then moved incrementally,

so that the soil exposure in each field of view increased in 10% increments from 0% to

100% (Figure 5.1). As the FOV is circular, the linear distances moved for each 10%

increase in area are not the same. The distance the tray was moved for each 10% increase

in area was determined using Newton’s Method (Kelley 2003). Thin pieces of wood, each

a specific width corresponding to the different distances required for 10% increases in area,


were placed between the tray and a solid stop at the appropriate intervals. Through this

collection method it was expected that spectral mixing would be purely linear. This was

repeated for each of the three vegetation types over the four soils. The small field of view

of the probe and the very small distance that the probe was moved for each increment

provides some possibility of errors in the collection of the different mixes. While every

effort was taken to measure each increment and ensure that mixes were accurate this is one

possible source of error in creating the physical mixes. Pure soil and vegetation spectra

were collected for creation of the virtual image in the same manner as for the laboratory

image. However, the mixed spectra of the virtual image were created as weighted linear

combinations of the pure spectra. Ten percent increments were again used to create an

image of the same mixes as the laboratory image.

a

b

Figure 5.1: (a) Demonstrates the configuration of the ASD high intensity reflectance probe held in a

clamp over the tray containing soil and leaves. (b) Demonstrates the incremental movement of probe

field of view over plant and soil interface. The solid lines indicate soil where pure soil and vegetation

spectra were collected. The dashed lines indicate the 10% increments as the probe was moved. Not to

scale.

The measured reflectance spectra and the calculated mixed spectra were incorporated into

a spectral library and then converted into artificial hyperspectral images. Spectra from the

four soils, the three different vegetation cover types and 11 cover fractions were

represented in each image of 12 samples, 11 lines and 2101 spectral bands (Figure 5.2).

The images created in this way allowed for the examination of the unmixing process with

known endmembers as inputs into the algorithm and measured or known fractions of each

of the soil and vegetation mixes.

100% vegetation within field of view

100% soil within field of view


Figure 5.2: The ‘laboratory image’ created from the measured spectra. Soil type is listed at the bottom,

vegetation cover type at the top and percent soil exposure on the left.

5.2.3 Spectral unmixing

The images were analysed using linear spectral unmixing in order to determine the relative

abundance of soil from mixed spectra for comparison with the known fractions measured

during spectral collection. The unmixing process is based on the principle that the

reflectance spectrum of a given pixel is the weighted linear combination of spectra in the

field of view. The procedure assumes that the photons interact with only one material and

that ‘non-linear’ mixing does not occur (as when photons have multiple interactions with

materials)(Ray and Murray 1996, Zhu 2005). Input reference spectra for the unmixing

were the four pure soil spectra (100% soil exposure) and the three pure plant cover spectra

(0% soil exposure) for each of the images.

Linear unmixing is summarised in Equation 1:

iij

n

j

ji erfR +=∑=1

(1)

where Ri is the reflectance of a pixel in band i, ƒj is the fractional abundance of endmember

j in band i, rij is the reflectance of the pure endmember j in band i, ei is the residual error


associated with band i and n is the number of endmembers. Equation 1 is constrained by

the assumption that the sum of the spectral components in each pixel should equate to 1.0

as defined by Equation 2:

11

=∑=

n

j

jf (2)

Spectral unmixing may be constrained or unconstrained. Constrained unmixing forces the

algorithm to assign fractional abundances that sum to one for each pixel and where no

negative abundances are permitted, whereas unconstrained allows unlimited negative and

positive abundances. Unconstrained unmixing has the advantage that the algorithm is not

being forced to unity and erroneous output abundances (less than zero or greater than one)

indicate a poor unmixing solution but do provide an avenue to improve the analysis

(Malenovsky et al. 2007). Erroneous output abundances theoretically arise from

incomplete assessment of the ‘pure’ materials within the image, i.e. an improper number of

endmembers, inadequate selection of endmembers to represent those materials, or a high

degree of collinearity between endmembers. Image noise and atmospheric attenuation are

also known to also affect the unmixing process (Settle and Drake 1993). The reality is that

if the outputs are negative or do not sum to unity the abundance fractions become

unrealistic and lose their meaning in the physical world and forcing them to do so will not

improve the analysis (van der Meer and De Jong 2000, Graña and D'Anjou 2004,

Malenovsky et al. 2007). In this study we used unconstrained spectral unmixing. The

unmixing was carried out using the ENVI 4.4 software package (RSI 2007).

Linear mixture analysis produces an image with estimates of each endmember fraction

within each pixel, and an estimate of the root mean squared error (RMSE) associated with

the unmixing. Endmember fractions for each pixel in the images were tabulated and

compared with the input measured or calculated fractions.

5.3 Results

5.3.1 Spectral characteristics

Reflectance spectra of all the soils (Figure 5.3) show pronounced water absorption features

(1400 and 1900 nm) and a clay absorption feature (2200 nm) with differences in symmetry

and depth indicating different clay species present. The sodic clay and clay loam show


largely symmetrical clay absorption features, indicating likely domination by illite and

smectite minerals. The loam and silty loam have asymmetrical absorption features at 2200

nm indicating they contain higher proportion of kaolinite group minerals but are still likely

dominated by illite and smectite. Also present in the loam and silty loam is an absorption

feature at 2316 nm, characteristic of carbonates and reflecting the high content determined

from the laboratory analysis (Table 5.1). The sodic clay spectra contains a distinctive iron

oxide response over the visible-near infrared range (400-900 nm), as expected from the

high iron oxide content (Table 5.1). A subtle iron oxide spectral feature is also present in

the silty loam but not in the loam and clay loam, despite very similar concentrations found

in laboratory analysis (Table 5.1). This is likely the result of the higher organic matter

content in the loam and silty loam which has been shown to mask the spectral response of

iron oxide (Galvao and Vitorello 1998). The silty loam and loam also have an absorption

feature at 2388 nm that is possibly the result of organic matter in the soil (Henderson et al.

1992, Ben-Dor et al. 1997) despite very different concentrations found in laboratory

analysis (Table 5.1).

Figure 5.3: Pure soil spectra (endmembers) from soils used in this experiment.

Figure 5.4 shows the spectra of the vegetation used as cover in the experiment. Although

both the Eucalyptus and orange foliage showed overall characteristics typical of actively

photosynthetic vegetation, they differ in particular spectral regions. The orange has a more

pronounced chlorophyll green reflectance maximum at 550 nm, as well as more

pronounced water absorption features at 1400 nm and 1900 nm. The field pea residue

showed spectral characteristics typical of dry senescent organic matter. Most noticeably

there is a broad absorption at 2100 nm with two smaller absorption at 2261 and 2327 nm,


which result from cellulose and lignin in the plant residue. Unlike the photosynthetic

vegetation spectra, the pea straw shows some similarity in overall form and albedo to the

soil spectra. There is increasing reflectance through the visible and near infrared and

specific absorption features in the 2000-2300 nm region.

Figure 5.4: Pure vegetation spectra (endmembers) from soils used in this experiment.

5.3.2 Mixes of spectra

Both methods of spectral mixing, the physical mixes (laboratory image) and the linear

weighted combinations (virtual image), created spectra that showed an even progression

from pure vegetation to soil. Examples of spectra from the sequences of physical mixes of

soil and plant material can be seen in Figure 5.5 and Figure 5.6. Because the soil and

photosynthetic vegetation differ markedly in albedo across most of the measured spectral

range, the sequence of mixed spectra shows pronounced gradients in reflectance intensity

from 500-2500nm (Figure 5.5). In addition, the increased influence of soil spectra in the

reflectance data can be seen with the reduction of the chlorophyll absorption at 650 nm,

along with a reduction in the red-edge and overall albedo in the near-infrared range (700-

1300 nm). There is also a change in shape of water absorption features at 1400 and 1900

nm and the appearance of a clay absorption feature at 2200 nm. In Figure 5.6 the changes

in reflectance characteristics are less evident as the soil fraction increases in the mix with

dry plant residue. There is little difference in albedo between the soil and non-

photosynthetic plant spectra, other than in the near infrared (800-1400 nm). However, the

clay absorption feature at 900 nm and the water absorption features at 1400 and 1900 nm

become more pronounced as the fraction of soil increases.


Figure 5.5: Spectra collected from actual mixes of Sodic Clay and photosynthetic Eucalyptus

vegetation. Mixes of 0%, 20%, 40%, 60%, 80% and 100% soil are shown for clarity.

Figure 5.6: Spectra collected from actual mixes of Sodic Clay and the non-photosynthetic field pea.

Mixes of 0%, 20%, 40%, 60%, 80% and 100% soil are shown for clarity.


5.3.3 Unmixing

The unmixing process results in a grey scale image for each of the input endmembers and a

root mean squared error (RMSE) image that indicates the level of error associated with the

unmixing of each pixel. For the virtual image, the RMSE (Figure 5.7) for all pixels was

low. Higher RMSE was found in pixels dominated by vegetation for all soils except the

loam which had higher errors in soil pixels. The clay loam had double the RMSE of the

other soils except under Eucalyptus where the loam errors were highest. For the laboratory

image, the RMSE (Figure 5.7) of unmixing was generally higher than that from the virtual

image. Under Eucalyptus all soils between 20% and 60% exposure had increased error.

Under the orange and the pea straw the clay loam had substantially higher RMSE, more

than twice that of the other soils. All pixels with high soil content (>60%) returned low

RMSE under each cover type.

Figure 5.7: RMSE from ‘virtual’ (a: Eucalyptus, b: orange, c: pea straw) and ‘laboratory’ (d:

Eucalyptus, e: orange, f: pea straw) images for each of the vegetation cover types.


Sodic clay

Abundance fractions for the sodic clay were comparable to the known soil exposure for

both the synthetic and measured spectral mixes (Figure 5.8). The sodic clay was

recognised as the input endmember and the fractional combinations of soil and the

vegetation were unmixed accordingly. Furthermore, the non-target soil spectra were not

confused with the sodic clay spectra. The unmixing fractions for the virtual image retrieved

the calculated soil exposure more accurately than the fractions from the measured

mixtures. The errors in unmixing the laboratory image were restricted to over estimation

between 50% and 100% soil exposure. However, in all cases, the errors in estimation of

soil fraction were less than 0.1 (10%).

Figure 5.8: Unmixing with the Sodic Clay endmember of the ‘virtual’ (a: Eucalyptus, b: orange, c: pea

straw) and ‘laboratory’ (d: Eucalyptus, e: orange, f: pea straw) images for each of the vegetation cover

types.


Silty loam

Fractions of the silty loam endmember were retrieved correctly from unmixing of the

virtual image (Figure 5.9) in most instances. However, when mixed with the orange cover,

the soil fraction was underestimated by at least 10% at all exposures, with under-estimation

greater at lower soil fractions. The laboratory image (Figure 5.9) demonstrated the same

over estimation of soil exposure between 50% and 100% that was evident in sodic clay

(Figure 5.8). Soil abundance under the orange was also underestimated to a similar extent

as in the virtual image. Not present in the laboratory image unmixing is the

misclassification of the loam as silty loam as seen in the virtual image.

Figure 5.9: Unmixing with the Silty Loam endmember of the ‘virtual’ (a: Eucalyptus, b: orange, c: pea


types.


Loam

The loam soil fraction was unmixed less successfully than previous soils from both the

virtual and laboratory images. Although the loam fraction unmixed from the virtual image

(Figure 5.10) showed a linear increase from 0-100% exposure, the magnitude of the soil

fraction was underestimated by up to 50%. This error was substantially worse under the

Eucalyptus cover type compared to the orange and pea straw. The unmixing pattern from

the laboratory image (Figure 5.10) with the loam endmember is quite different from the

pattern with the virtual image. The unmixing fractions generally followed a sigmoidal

trend with increasing soil exposure. Under orange and pea straw the range of estimated

fractions was feasible (0-1), but negative soil abundances were recorded below 50%

exposure under Eucalyptus cover. Errors in estimation of the soil fraction were greatest

below 60% soil exposure under Eucalyptus and orange but over 40% exposure under pea

straw.

Figure 5.10: Unmixing with the Loam endmember of the ‘virtual’ (a: Eucalyptus, b: orange, c: pea


types.


Clay loam

The clay loam endmember unmixing of the virtual image (Figure 5.11) returned accurate

fractional abundances for the target soil under Eucalyptus but misclassified the silty loam

at low exposures and the loam at high exposures. Under the orange the target soil showed a

one to one increase but underestimated the soil fraction by up to 20%. Unmixing of the

laboratory image (Figure 5.11) showed substantial misclassification of up to 0.6 (60%)

with all the non-target soils under Eucalyptus. The fractional abundance of the target soil

was also overestimated at most soil exposures. Under the orange there was again a

negative abundance below 30% exposure and an overestimation above 40%. The pea straw

unmixing returned low fractional abundances at all exposures above 20%.

Figure 5.11: Unmixing with the Clay Loam endmember of the ‘virtual’ (a: Eucalyptus, b: orange, c:

pea straw) and ‘laboratory’ (d: Eucalyptus, e: orange, f: pea straw) images for each of the vegetation

cover types.


5.4 Discussion

5.4.1 Unmixing

Of the soils examined, the sodic clay was unmixed most accurately. This is likely due to

the very distinct spectral features such as iron oxide (400-900 nm), water (1400 and 1900

nm) and clay (2200 nm). In addition it has a moderately high albedo over most of the

wavelength range examined. The silty loam was also unmixed well and has an iron oxide

absorption feature (400-900 nm), distinctive clay feature (2200 nm) and carbonate

absorption feature (2316 nm). This soil also has the highest albedo but relatively small and

uncharacteristic water absorption features (1400 and 1900 nm). Unmixing of the loam and

clay loam was the least accurate; these soils have the least distinct spectra. Loam had a

carbonate absorption feature (2316 nm) and moderate albedo while the clay loam had the

lowest albedo.

5.4.2 Discrimination of soils

There were few cases of non-target soils being classified as target spectra but they occurred

largely in unmixing the virtual image. Pure loam was classified as up to 0.33 (33%) of the

target soil silty loam (Figure 5.9) and as up to 0.4 (40%) of the clay loam (Figure 5.11).

Despite a substantial difference in albedo, the clay loam and loam were very spectrally

similar; water (1400 and 1900 nm) and clay (2200 nm) absorption features are of

comparable intensity and shape with little else to differentiate them other than

chromophores at 2316 nm and 2388 nm in the loam which are not present in the clay loam

spectra. The silty loam and loam are also spectrally similar, again despite contrasting

reflectance intensity, differing mostly in the mild iron oxide absorption feature present in

the silty loam and not in the loam. It is unexplained why this occurred only in the virtual

image but not in the laboratory image. Given the spectral similarity of the soils some

degree of misclassification such as this was expected from both images.

The observed misclassification of mixed pixels as a different soil-vegetation combination

potentially undermines the unmixing of airborne and satellite imagery in resource

management and mapping applications. It appears from these results that the unique

spectra of different soils can affect the ability of unmixing algorithms to correctly estimate

mixed abundances. Contrary to expectations (Asner and Heidebrecht 2002, Bannari et al.

2006, Daughtry et al. 2006), the greatest misclassification here was observed with


photosynthetic vegetation and soil mixes, rather than non-photosynthetic soil mixtures.

Nonetheless, despite these complications, for the soils examined in this study, few

difficulties were encountered in isolating pure soil pixels.

5.4.3 Discrimination of soil and vegetation

Typically the separation of soil and non-photosynthetic vegetation with unmixing is more

difficult than with soil and photosynthetic vegetation (Bannari et al. 2006). In our

experiments however, the soil and field pea residue pixels were unmixed better than the

soil-photosynthetic vegetation mixes. In both the virtual and laboratory images there was

evidence of confusion between the target soil endmembers and pure photosynthetic

vegetation. This was particularly unexpected given the spectral characteristics of the soils

and photosynthetic vegetation are so distinct. Pure vegetation spectra, with significant

water absorption features and red edge, bear little resemblance to the soil spectra examined

in this study.

In Figure 5.10a and Figure 5.10d pure Eucalyptus spectra over sodic clay were unmixed as

having up to 0.12 (12%) fractional abundance of loam spectra. Comparing the spectra of

these materials (Figure 5.3 and Figure 5.4) there were spectral characteristics, such as the

iron oxide feature (400-900 nm), that bears some resemblance to the characteristic red-

edge of photosynthetic vegetation. This misclassification only occurred with the

Eucalyptus and not the orange which has a higher overall albedo and more pronounced

red-edge. Similarly, pure Eucalyptus and orange over silty loam were incorrectly given

nearly 0.2 (20%) fractional abundance of the target spectra clay loam (Figure 5.11).

However, unlike the sodic clay there is no obvious spectral similarity between soil (Figure

5.3) and the photosynthetic vegetation (Figure 5.4).

There was some misclassification of mixes of soils and vegetation as the pure soil

endmember or target soil. The unmixing with sodic clay (Figure 5.8) and to a lesser extent

silty loam (Figure 5.9), gave expected results where there was a clear recognition of pixels

containing target and non-target soils mixed with vegetation. Alternatively, for loam

(Figure 5.10) fractional abundances of soil and vegetation are substantially incorrect

despite successful separation of different soil and soil-vegetation mixes. For the clay loam

(Figure 5.11) mixtures of soil and vegetation were incorrectly classified as soil. Similar

observations have been made in a previous study (Okin et al. 2001) where some vegetation

and soil mixes were confused with pure soil spectra in the unmixing process. However, in


that field study, undertaken in a semiarid environment, the vegetation was observed to

have minimal water absorption features and a very small red edge, which is not the case

here.

5.4.4 Unmixing errors

There were many instances where the unmixing resulted in a negative abundance (e.g.

Figure 5.11) and some with abundances greater than unity (e.g. Figure 5.10). In traditional

image unmixing a negative result would be common and due to the difficulty in isolating

all endmembers within the scene and ensuring that those spectra chosen as inputs are pure

and not mixtures of different materials. However, in this experiment all the endmembers in

the ‘scene’ were known and they were all known to be pure. Nonetheless errors in our

experiment would be expected and would prevent the algorithm from perfectly inverting

the mixed spectra. Firstly some correlation between the endmembers should be expected.

In both images there are similarities between the soil spectra and between the

photosynthetic vegetation spectra. Secondly, there is some variation between each of the

‘pure’ spectra used in the image and the reference spectra used in the unmixing because

each was collected separately by the spectrometer. The algorithm cannot account for this

variation and errors are unavoidably introduced to the unmixing.

The disparity between the known soil exposure and the fractional abundance coincides

with the areas of high RMSE. For the laboratory image the areas of largest RMSE

corresponded with the 20% to 60% soil-vegetation mixes that were incorrectly unmixed as

some fraction of the target soil. These inaccuracies were evident with the clay loam (Figure

5.11a) where soil and vegetation mixes were given a high reading (up to 60%) and with the

loam (Figure 5.11a) where a substantial negative fractional abundance was returned. For

the virtual image there is substantially lower RMSE; however, the areas of largest RMSE

still correspond with the poor estimation of soil exposure. For example, the

misclassification of loam as clay loam (Figure 5.11) under all cover types is reflected in the

relatively high error given loam in the RMSE graph (Figure 5.7).


5.4.5 Virtual versus laboratory images

The virtual image served as a control to determine the accuracy of measured spectra for the

laboratory image. As spectral mixes in the virtual image were created from weighted linear

combinations they contained exact proportions of the pure spectral endmembers. If the

fractions of soil and vegetation in the laboratory image were accurately measured, and the

radiance recorded by the sensor was a linear mixture of the reflectance from these

components, then retrieval of the endmember spectra should be similar. There are two

potential reasons for the substantial differences between the unmixing results from the

laboratory and virtual images. Firstly, fractions of soil and vegetation could have been

inaccurately measured and secondly the radiance measured by the spectrometer is not a

perfect weighted linear mix of the fractional constituents.

Small errors in measurement of cover fractions in the laboratory spectra may account of

some apparent errors in unmixing. The one to one relationships evident in some virtual

image outputs (Figure 5.8 and Figure 5.9) but not the outputs from the laboratory image

(Figure 5.8 and Figure 5.9) are to some degree a reflection of this. However, the difference

in other unmixing results between the two image types far exceeded the expected error

from the collection of the laboratory image spectra (e.g. Figure 5.10 and Figure 5.11).

Nonetheless, the errors evident in Figure 5.10 (although negative) and Figure 5.11 appear

consistent across all the soils despite each soil and vegetation combination being measured

independently. For example, the misclassification of the soil-vegetation combinations

evident in the unmixing of the laboratory image (Figure 5.11d) were not present in the

unmixing of the virtual image (Figure 5.11a) but the misclassification of the non-target

soils as the target soil (clay loam) in the laboratory image was relatively uniform.

Therefore it appears that much of the discrepancy between the laboratory and virtual image

outputs resulted from non-linear mixing of the material constituents in the laboratory

image. The relatively accurate retrieval of soil fractions from the virtual image compared

with the laboratory image suggest that real world mixing is not linear. Previous studies

have used a similar methodology to create virtual mixes of spectra and found the results

adequate for measuring constituent material abundance. One such study (Daughtry 2001)

used ratio indices such as the cellulose absorption index to quantify crop residue on

different soils but did not attempt to identify the soils themselves. Another study (Hussey


1998) examined spectral mixture analysis of different mineral combinations made through

weighted linear combinations and compared them to the spectra of physical mixes. Some

differences were observed between the virtual and physical mixes but these were attributed

to minor discrepancies in mineral purity, and while successful, the study aimed to

determine the physical limits for unmixing based on band numbers and signal noise rather

than material constituents.

Our comparison of virtual and physical mixing suggest that radiance measurement by the

instrument is not a consistently linear mix of component spectra. Many remote sensing

studies using spectral mixture analysis assume linear mixing yet suggest ‘non-linear

mixing’ as a cause for unmixing errors. Generally accepted causes of the non-linear mixing

include transmission of light through the vegetative cover and the scattering of light off

multiple surfaces before reaching the sensor. This experimental design sought to minimise

these factors by using multiple layers of vegetation to reduce transmission of light through

the leaf and collecting spectral mixes from a flat surface to reduce scattering. Thus these

two parallel experiments have tested, and provided stronger evidence that, mixing of soil

and cover types is not perfectly linear. While this is generally well accepted within the

remote sensing community , there is little systematic experimentation to quantify and

explore it under controlled laboratory conditions.

5.5 Conclusions

This study used a technique combining laboratory reflectance spectra and spectral mixture

analysis to identify soil fractions from mixed pixels containing soil, photosynthetic

vegetation and non-photosynthetic vegetation. The methodology provided images that

could be analysed by standard hyperspectral feature extraction algorithms. It should also be

emphasised that this study was conducted in a controlled laboratory. The effects of

atmospheric attenuation, soil surface roughness, soil moisture content and leaf orientation

are not considered.

Results also show the unmixing process successfully recognised and classified the different

soils within both image types. However, not all soil spectra were isolated from mixed

pixels equally or successfully to provide accurate abundance fractions. This highlights

potential problems of techniques like linear spectral mixture analysis with evidence of

confusion between pixels of mixed constituents (soil and vegetation) and other materials


(different soil types). While other studies have suggested this possibility (Okin et al. 2001),

this research shows conclusively that spectral confusion occurs even in images with limited

and well understood endmembers. The comparison between virtual and laboratory images

cast some doubt on assumptions regarding the combination of pure spectra in mixed pixels

with further evidence that it is not consistently linear. This is largely accepted for image

studies conducted in heterogeneous terrain with rough and undulating soil surfaces, and

differing plant geometry with variable leaf orientation (Ray and Murray 1996, Malenovsky

et al. 2007). These results demonstrate the limitations of this technique even carried out in

an essentially linear environment. Furthermore, spectra for the two image types were

collected under identical conditions and as such the comparison is that between the mixing

processes alone.

Visible and near-infrared remote sensing provides enormous scope in monitoring and land

management to improve our understanding and practices in agricultural and environmental

applications. However, the power of these techniques is limited by our understanding of

processes on the ground. Confusion of mixed pixels with pure pixels and the non-linear

spectral mixing evident in this study has impacts on real world image studies undertaken to

monitor ground cover or soil. Further research is required to better understand the process

at work here.

5.6 References

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Asner, G. P. and Heidebrecht, K. B. 2002 Spectral unmixing of vegetation, soil and dry carbon cover in arid regions: Comparing multispectral and hyperspectral observations, International Journal of Remote Sensing, 23, 3939-3958.

Bannari, A., Pacheco, A., Staenz, K., McNairn, H. and Omari, K. 2006 Estimating and mapping crop residues cover on agricultural lands using hyperspectral and IKONOS data, Remote Sensing of Environment, 104, 447-459.

Ben-Dor, E., Inbar, Y. and Chen, Y. 1997 The reflectance spectra of organic matter in the visible near-infrared and short wave infrared region (400-2500 nm) during a controlled decomposition process, Remote



Sensing, 23, 1043-1062.

Bock, M., Rossner, G., Wissen, M., Remm, K., Langanke, T., Lang, S., Klug, H., Blaschke, T. and Vrscaj, B. 2005 Spatial indicators for nature conservation from European to local scale, Ecological Indicators, 5, 322-338.


Bower, T. L. and Rowan, L. C. 1996 Remote mineralogic and lithological mapping of the Ice River alkaline complex, British Columbia, Canada, using AVIRIS data, Photogrammetric Engineering and Remote Sensing, 62, 1376-1143.




Daughtry, C. S. T. 2001 Discriminating crop residues from soil by shortwave infrared reflectance, Agronomy

Journal, 93, 125-131.

Daughtry, C. S. T., Doraiswamy, P. C., Hunt, J., E.R., Stern, A. J., McMurtrey III, J. E. and Prueger, J. H. 2006 Remote sensing of crop residue cover and soil tillage intensity, Soil and Tillage Research, 91, 101-108.

Daughtry, C. S. T., Hunt, E. R., Jr., Doraiswamy, P. C. and McMurtrey, J. E., III 2005 Remote sensing the spatial distribution of crop residues, Agronomy Journal, 97, 864-871.

Dehaan, R. and Taylor, G. R. 2003 Image-derived spectral endmembers as indicators of salinisation, International Journal of Remote Sensing, 24, 775-794.

Dennison, P. E. and Roberts, D. A. 2003 Endmember selection for multiple endmember spectral mixture analysis using endmember average RMSE, Remote Sensing of Environment, 87, 123-135.

Dutkiewicz, A., Lewis, M. and Ostendorf, B. 2003 Evaluation of hyperspectral imagery for mapping the symptoms of dryland salinity, In Spatial Sciences Coalition 2003, Canberra,

Galvao, L. S. and Vitorello, I. 1998 Role of organic matter in obliterating the effects of iron on spectral reflectance and colour of Brazilian tropical soils, International Journal of Remote Sensing, 19, 1969-1979.

Gee, G. W. and Bauder, J. W. 1986 Particle-size analysis, In Methods of soil analysis, part 1. Physical and

mineralogical methods, Vol. 2 (Ed, A. Klute) American Society of Agronomy - Soil Science Society of America, Madison, Wisconsin, USA, pp. 383-409.

Graña, M. and D'Anjou, A. 2004 Feature extraction by linear spectral unmixing, In Knowledge-based

intelligent information and engineering systems, pp. 692-698.


Hussey, M. C. 1998 Surface detection of alkaline ultramafic rocks in semi-arid and arid terrains using

spectral geological techniques, PhD Thesis, Open University, Southampton, United Kingdom.

Isbell, R. F. 2002 The Australian soil classification, CSIRO Australia, Melbourne.


Janik, L. J. and Skjemstad, J. O. 1995 Characterisation and analysis of soils using mid-infrared partial least squares: II. Correlations with laboratory data, Australian Journal of Soil Research, 33, 637-650.

Kelley, C. T. 2003 Solving nonlinear equations with Newton's methodology, Society for Industrial Applied Mathematics, Philadelphia.


Lu, D. and Weng, Q. 2007 A survey of image classification methods and techniques for improving classification performance, International Journal of Remote Sensing, 28, 823-870.

Malenovsky, Z., Bartholomeus, H. M., Acerbi-Junior, F. W., Schopfer, J. T., Painter, T. H., Epema, G. F. and Bregt, A. K. 2007 Scaling dimensions in spectroscopy of soil and vegetation, International Journal of

Applied Earth Observation and Geoinformation: Advances in airborne electromagnetics and remote sensing

of agro-ecosystems, 9, 137-164.


McCarty, G. W., Reeves, J. B., III, Reeves, V. B., Follett, R. F. and Kimble, J. M. 2002 Mid-infrared and near-infrared diffuse reflectance spectroscopy for soil carbon measurement, Soil Science Society of America

Journal, 66, 640-646.

Metternicht, G. I. and Zinck, J. A. 2003 Remote sensing of soil salinity: Potentials and constraints, Remote


Nagler, P. L., Daughtry, C. S. T. and Goward, S. N. 2000 Plant litter and soil reflectance, Remote Sensing of


Nagler, P. L., Inoue, Y., Glenn, E. P., Russ, A. L. and Daughtry, C. S. T. 2003 Cellulose absorption index (CAI) to quantify mixed soil-plant litter scenes, Remote Sensing of Environment, 87, 310-325.

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analysis, Vol. 2 (Ed, A. L. Page) Soil Science Society of America, Madison, pp. 53-579.

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Ray, W. T. and Murray, B. C. 1996 Nonlinear spectral mixing in desert vegetation, Remote Sensing of


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surveys, U.S. Government Print Office, Washington D.C.

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Viscarra Rossel, R. A., McGlynn, R. N. and McBratney, A. B. 2006a Determining the composition of mineral-organic mixes using UV-VIS-NIR diffuse reflectance spectroscopy, Geoderma, 137, 70-82.

Viscarra Rossel, R. A., Walvoort, D. J. J., McBratney, A. B., Janik, L. J. and Skjemstad, J. O. 2006b Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties, Geoderma, 131, 59-75.

Zhang, L., Wu, B., Huang, B. and Li, P. 2007 Nonlinear estimation of subpixel proportion via kernel least square regression, International Journal of Remote Sensing, 28, 4157-4172.

Zhu, H. 2005 Linear spectral unmixing assisted by probability guided and minimum residual exhaustive search for subpixel classification, International Journal of Remote Sensing, 26, 5585-5601.

95

Chapter 6

Mapping soil variability with hyperspectral image data

Published as a refereed conference paper:

Summers, D., Lewis, M., Ostendorf, B. and Chittleborough, D. J. 2009 Mapping soil variability with hyperspectral image data, In SSC 2009 Spatial diversity: The Biennial International Conference of the Surveying and Spatial Sciences Institute, Adelaide, Australia, September-October, 2009.

a1172507

Text Box

A Summers, D., Lewis, M., Ostendorf, B. & Chittleborough, D.J. (2009) Mapping soil variability with hyperspectral image data. In SSC 2009 Spatial diversity: Biennial International Conference of the Surveying and Spatial Sciences Institute, Adelaide, Australia.

a1172507

Text Box

A NOTE: This publication is included on pages 95-112 in the print copy of the thesis held in the University of Adelaide Library.

113

Chapter 7

Discussion and Conclusion

7.1 Introduction

The overall goal of this thesis was to contribute to the development of tools to understand

and map soils in southern Australia. This was identified as a gap in knowledge from the

point of view of precision agriculture, where improved understanding of soil variability is

an important input in improving farming efficiency and productivity. Furthermore,

improved understanding of soil variability in the landscape is seen as vital to improve the

accuracy and precision of models to better understand landscape processes for applications

including ecology, biodiversity and soil hydrology.

The thesis has contributed to this goal by examining spectral reflectance methodologies

that have the potential to improve the efficiency of soil sample analysis, allowing for

sampling densities greater than is typical for regional soil analysis and mapping.

Additionally, this work examined the spectral unmixing of hyperspectral image data to

map surface soil variability, exploiting the continuous nature of remotely sensed images

and the high diagnostic power of hyperspectral reflectance data.

Chapters 3 and 4 examined the use of hyperspectral reflectance spectroscopy to

discriminate select soil field survey classes and predict laboratory measured soil properties

respectively. The discrimination of soil field survey classes (Chapter 3) provided some

insight into the complex relationships and collinearity of soil properties such as clay

content, carbonate content and soil colour. However, this study also highlighted the

inherent problems of soil field survey, a relatively subjective measure of soil properties, for

quantitative research. Alternatively, Chapter 4 examined the prediction of quantitative soil

properties determined from laboratory analysis using partial least squares regression and

achieved substantially better results. Following the regression analysis, kriging of the

measured and predicted data was used to create soil raster layers. Comparison of the

measured and predicted raster layers found they mapped similar variability in the

landscape over comparable ranges in soil properties.

Chapter 7: Conclusion 114

Chapters 5 and 6 examined the possibility of using hyperspectral image data to identify soil

variability in the landscape. Vegetative cover was identified as a major problem in

achieving this aim as it obscures the soil surface from the sensor. In order to examine the

complexities of this problem, two types of simulated imagery were developed which

provided known mixes of the various constituents for subsequent analysis and comparison

(Chapter 5). Finally, HyMap airborne hyperspectral imagery was used to map soil types in

the landscape. Endmembers were isolated from the imagery and were used in partial

unmixing algorithms in an attempt to identify soil variation (Chapter 6).

7.2 Summary of specific contributions to knowledge

7.2.1 Spectral discrimination of soil properties (Chapter 3)

The major aim of Chapter 3 was to investigate the ability of visible, near infrared and

shortwave infrared reflectance spectroscopy to predict various field survey soil properties

in a localised geographical region in order to supplement soil survey. These were clay

content, carbonate content and the components of Munsell colour (hue, value and chroma).

The primary motivation behind this study was to determine the compatibility of reflectance

spectroscopy to complement soil field survey. While soil field survey is conducted

extensively in southern Australia’s intensive agricultural areas it is prohibitively expensive

in broadacre and dryland agricultural areas. Reflectance spectroscopy was investigated as a

means to expand the areas mapped using field survey methodologies more cost effectively

while maintaining some continuity between methodologies.

The study involved the collection of 293 soil samples from the Jamestown – Belalie

district, a northern agricultural region in South Australian. Samples were analysed using

conventional field survey methodologies and reflectance spectra were collected before the

development of penalised discriminant analysis models to discriminate classes. The

chroma component of Munsell colour was the only soil property that was adequately

discriminated using the hyperspectral reflectance data. All the other properties examined

were well discriminated in one or two of their classes but overall accuracy was poor.

Findings from Chapter 3 also indicate that there was substantial co-variation in the spectral

properties of the soil properties examined. Consideration has been given that this co-

variation substantially limited discrimination of soil properties.


However, it is also possible that the subjective nature of field survey classification

introduced a considerable source of variation into the classification. For example, while

field texture analysis provides a useful and repeatable assessment of the physical behaviour

of soil in the field, it is nonetheless subjective and prone to user error (McDonald and

Isbell 1990). Similarly, soil colour is also subjective; individuals can perceive colour

differently, but also, the soil colour classification involves matching soil to the closest

colour chip and there is rarely a perfect match (McDonald and Isbell 1990). In this study

efforts were made to minimise error, firstly through the analysis of replicates and secondly

by using a single trained and competent soil scientist to carry out the analysis.

7.2.2 Visible near-infrared reflectance spectroscopy as a predictive indicator of soil

properties (Chapter 4)

The major aim of Chapter 4 was to investigate the use of visible near-infrared spectroscopy

as a predictor of laboratory measured soil properties in a localised geographical region in

order to supplement soil survey. The motivation behind this was to overcome the

subjective nature of the field survey used in Chapter 3, provide a more rigorous test of

spectroscopic prediction and an objective cost-effective methodology to improve the

spatial resolution of soil mapping in dryland agricultural regions. The soil properties used

in the analysis were clay, carbonate, organic carbon and iron oxide contents. These were

chosen because they are important determinants of soil agricultural capability and also

because they are considered important as inputs into soil hydrology models.

This study involved the analytical determination of soil properties in the laboratory, the

collection of further reflectance spectra of soil samples using the ASD Field Spec Pro. and

the development of prediction models using partial least squares regression (PLSR).

Following the prediction of soil properties kriging was employed to model surface soil

properties across the landscape from both the measured and predicted datasets. These

layers were then compared to determine the utility of the predicted data as a supplement

for soil survey.

The results show that all soil properties were adequately predicted. The model explained

more than 65% of the variability in clay and carbonate content and residual predictive

deviations (RPD) of 2.0 and 2.1 respectively indicate substantially better prediction than

the mean of the observed values. Soil organic carbon and iron oxide were less successfully

predicted but still achieved r2 values of 0.57 and 0.61 respectively and acceptable RPD


values of greater than 1.5. The different measured and predicted layers produced following

kriging of the point sample sites show similar patterns indicating that soil spatial variability

was similarly represented in both approaches. The PLSR results and comparison of the

surface layers produced demonstrates that the PLS prediction from spectroscopic

measurements provides a suitable method to efficiently supplement and enhance traditional

soil survey.


hyperspectral imagery (Chapter 5)

The major aim of Chapter 5 was to examine the complex interaction of soil and vegetative

cover of different types. The goal was to better understand how soil surface properties can

be measured with remote sensing imagery in environments where soil exposure is limited.

The assessment involved the collection of four distinct soil types from the Monarto region

in South Australia, chosen to provide physical and chemical diversity as well as spectral

variability. Vegetation types typical of common landuses in southern Australia were also

collected, namely native trees (eucalyptus), horticulture (orange) and dryland agriculture

(crop residue). These materials were used to create two types of simulated imagery: one,

called the laboratory image, created from real mixes of the soil and vegetation,

incrementally increasing the amount of vegetation of in the field of view during spectral

collection. The other, called the virtual image, created by weighted linear combinations of

pure soil and vegetation spectra. The pure soil endmembers were then used as inputs into

linear unmixing algorithms. The classification of soils types in mixed pixels and the

determination of fractional soil exposure were then assessed from the output images.

Results show that the soils were successfully recognised and classified within both image

types. However, not all soil spectra were isolated from mixed pixels equally or

successfully to provide accurate abundance fractions. For example, the only soil that

showed accurate unmixing abundances at most exposures was the sodic clay. Importantly,

in some cases, such as with the unmixing of loam and clay loam, mixed pixels were

classified as non-target soils indicating that the unmixing process interpreted mixed pixels

as a different soil endmember. This presents a complication when attempting to unmix

soils and vegetation in the landscape for the purposes of mapping soil variability.


7.2.4 Mapping soil variability with hyperspectral image data (Chapter 6)

The major aim of this research was to map soil types from airborne hyperspectral image

data without using a priori knowledge of soil variability or composition in the landscape to

inform unmixing endmembers. HyMap hyperspectral imagery was collected over the

Jamestown – Belalie district, a dryland agricultural region 200 km north of Adelaide. The

image, which is dominated by crop residue, covers an area of broad valleys and small hills

where the landuse is mostly cropping and grazing. Soil endmembers were determined

through a process where pure pixels were isolated statistically in n-dimensional space.

These endmembers were then used in the unmixing to map soil variability and the results

were compared with quantitative soil properties determined from sample sites within the

mapped areas. Further to this, the endmember abundance was compared to visual field

assessment of soil exposure made during sample collection.

Four distinct endmembers were isolated in the pixel purity process and each mapped

different areas in the landscape using the partial unmixing algorithm. However, the

laboratory analysis of soil samples was unable to characterise any difference between the

areas mapped. Furthermore, the coefficients of determination between the image derived

soil abundance and the field estimated soil exposure indicate that little of the variance was

captured through the image analysis. While the use of partial unmixing to identify surface

soil variation in the landscape may provide a useful tool to inform soil survey, the results

here were limited. Possible explanations for this include poor endmember selection

through the pixel purity process and the lack of variation in the surface soils within the

hyperspectral image. However, it is also appears that the dominant influences on the soil

response as recorded by the airborne hyperspectral sensor are related to land management

(e.g. tillage), or properties such as moisture and colour not quantified by the laboratory

measurements.

7.2.5 Overall assessment of thesis topic

The research summarised above represents a substantial contribution to the use of soil

reflectance and hyperspectral remote sensing to better understand soil variability and map

soil properties with these technologies. This thesis strengthens existing knowledge by

testing the prediction of soil properties from reflectance spectroscopy over a limited

geographical area. The research also provided an assessment of how that prediction can be

used to generate soil maps. Simulated hyperspectral imagery was used to assess the


spectral unmixing of soil and vegetation to assess the use of hyperspectral image data in

mapping soil variability through vegetative cover of different types. The mapping of soil

variability in a dryland agricultural region dominated by crop residue was also examined

using HyMap airborne hyperspectral data.

7.3 General discussion: wider significance and limitations

The work conducted and presented in this thesis has made some important contributions to

knowledge. The significance and limitations of the research specific to the aims of the

different studies has been discussed within the relevant chapters. The following section

covers the wider significance and the limitations to generalisation of the research.

7.3.1 Spectral discrimination of soil properties (Chapter 3)

The spectral discrimination of soil properties presented in Chapter 3 was conducted using

field survey methodologies. These field survey techniques provide a cost effective and

useful assessment of the soil properties for land managers, largely targeted at improving

irrigation efficiency and environmental sustainability. It was proposed that spectral

discrimination of field survey soil properties may provide a cost effective means to apply

similar classifications in dryland areas. Furthermore, the spectral discrimination of field

classes may provide a more quantitative, objective means by which to discern field classes.

However, results from this study indicate limited success in this regard. The limited results

may stem from inaccurate assessment of the field classes, which may be overcome through

the incorporation of multiple individuals undertaking the field classification. Alternatively,

poor classification of the spectral data through the penalised discriminant analysis may

have been a factor. This could be caused by co-variance between the different soil classes

and subsequent analyses may be improved through stratification. However, given the

successful prediction of soil properties using partial least squares regression, no further

attempt to improve the classification of field survey classes was made for this research.

7.3.2 Visible near-infrared reflectance spectroscopy as a predictive indicator of soil

properties (Chapter 4)

The use of visible near-infrared reflectance spectroscopy as a predictive indicator of soil

properties in this study was successful. One of the main goals of the research was to

predict properties over a range of soil variation encountered in a limited geographical area

that would normally be the subject of soil survey. Because of this, the application of the


model developed here to different geographical areas is limited. In order to apply a similar

methodology to different geographical areas new prediction models would need to be

developed. Although previous research (Janik et al. 1995, Viscarra Rossel et al. 2006) has

had some success applying similar methodologies to soils from broad geographical extents,

little attempt has been made predict values over local areas. Furthermore, the broad

geographical extents of these studies limits the utility of reflectance spectroscopy

methodologies to supplement soil survey to improve the resolution of regional soil maps.

Nonetheless, the results of the model developed here provide clear evidence that the

methodology can be applied to areas of limited variability with relative success. Moreover,

this research shows that the prediction of soil properties from reflectance spectroscopy can

be used with geostatistical methods such as kriging in order to develop soil maps.


hyperspectral imagery (Chapter 5)

The classification of soils and determination of exposure from mixed pixels of various soil

and vegetation types was examined using simulated imagery. Limitations of this

methodology arise from the small number of soils and vegetation cover types used in the

simulated imagery. It is quite likely that greater variation in soil and vegetation would be

encountered in some image studies. Nonetheless, there can be little doubt that the results

presented in this study demonstrate spectral confusion in the unmixing. Further limitations

include the simulated data itself providing a near perfectly linear environment and the

absence of topographic variation, which is unlikely to be encountered in image studies.

However, while these factors are a limitation, they also provide for quantitative assessment

of the unmixing itself by restricting the number of variables. The wider implications of this

study are that combined soil and vegetation mixes can be confused for different soil types

and this must be considered in future work.

7.3.4 Mapping soil variability with hyperspectral image data (Chapter 6)

The mapping of soil variability with hyperspectral imagery provided limited success in

identifying surface soils of measurably different properties. Four spectrally distinct soil

endmembers were extracted from the image and used to map distinctly different areas of

agricultural landscape. However, the methodology failed to identify measurably different

soils. Causes of this may be similar to those outlined in Section 7.3.3 where linear spectral

unmixing was found to confuse soil endmembers with mixed pixels of vegetation and soil.


However, the complexity of the image data does not allow for such a definitive conclusion

to be drawn. Other possible causes include poor identification of endmembers, insufficient

sampling points to measure differences in the unmixing outputs and lack of soil variation

in the landscape. Time and funding restrictions have prevented return field visits that

would allow for further soil sampling and the opportunity to refine conclusions. While

these results are disappointing they do raise many questions for further avenues of

research. The scope for using methodologies like this to map soil variability is ever

increasing with new satellites planned that would allow for finer temporal resolution

through repeat visits. Greater image data availability and improved field work may provide

definitive results that were not achieved in this study.

7.4 Recommendations for future research

The following areas of necessary research were identified through the work presented in

this thesis.

• Further assessment of the utility of spectral discrimination of soil field survey

classes may provide more useful results. The work presented in this thesis could be

improved through the introduction of quality tests of field survey classifications.

The easiest way to do this is to utilise multiple operators and compare field survey

results before the applying the discriminant analysis.

• The next step in the prediction of soil properties using reflectance spectroscopy is

to incorporate sub-surface soils analysis. This could be achieved through the

analysis of soil cores similar to that currently done with geological cores (Mauger

et al. 2004, Ben-Dor et al. 2008). Such an analysis would provide a three

dimensional understanding of soil variability crucial for complete landscape

management.

• Follow up investigations should be made into the spectral unmixing of soil and

vegetation. This would require a more exhaustive physical soil analysis to provide

validation data. The inclusion of properties such as soil water, soil colour and

surface crusting in the analysis may improve the results.

• The introduction of new satellite hyperspectral image sensors will provide the

ability to repeat sample areas of the landscape at a high spectral and spatial


resolution. One possible advantage of such instruments is that complete soil

coverage within regions could be built up over time as seasonal changes in land

cover provide for direct soil exposure to the sensor.

7.5 Conclusion

This thesis has contributed significantly to improving the use of reflectance spectroscopy

and remote sensing in mapping and understanding soil variability in the landscape. The

prediction of soil properties using reflectance spectroscopy is a powerful tool and could

certainly aid in improving the resolution of soil maps. This technique could be applied to

other regions with the development of new prediction models and could also be expanded

to include sub-surface soil properties, thus providing a three dimensional soil map.

Understanding the spectral unmixing of soil and vegetative cover is an important

component for successful image mapping of surface soil variability. The simulated

imagery provided a useful tool to demonstrate some of the problems encountered when

using unmixing algorithms with hyperspectral imagery. While less successful, the partial

unmixing of image derived soil endmembers form hyperspectral image data may yet

provide a useful tool in understanding soil variability at relatively fine scales and over

large extents. However, further research in this area with improved datasets is required to

develop a useful tool for this application.

7.6 References

Ben-Dor, E., Carmina, K., Heller, D. and Chudnovsky, S. 2008 A novel combined optical method for (sic) objectively map soil in a near real time domain, In The 21st Congress of the International Society for Photogrammetry and Remote Sensing, Beijing, China, 3-11 July 2008.

Janik, L. J., Skjemstad, J. O. and Raven, M. D. 1995 Characterization and analysis of soils using mid infrared partial least-squares, I. Correlations with XRF-determined major-element composition, Australian Journal of

Soil Research, 33, 621-636.

Mauger, A. J., Keeling, J. L. and Huntington, J. F. 2004 Bringing remote sensing down to earth: CSIRO Hylogger as applied to the Tarcoola goldfield, South Australia, In 12 Australasian Remote Sensing and Photogrammetry Conference, Fremantle, Western Australia,



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