remote sensing of terrestrial ecosystems

P A R T

I

Remote Sensing ofTerrestrial Ecosystems

3

C h a p t e r

1

Remote Sensing of Soils and Soil Processes

Alfredo HueteUniversity of Arizona

Tucson, Arizona

1 . 1 I N T R O D U C T I O N

The soil layer, or pedosphere, is a unique biologically active dynamic layer, forminga continuous interface between the lithosphere, atmosphere, hydrosphere, and bio-sphere (Figure 1.1). The pedosphere is essentially the ‘‘skin’’ of the terrestrial Earth,functioning as Earth’s geomembrane, regulating the biogeochemical and hydrologiccycling of matter and energy within terrestrial surfaces. Soils have a strong influenceon the atmosphere by acting as the source and sink of greenhouse gases (CO2, CH4,N2O, and H2O). Soils influence the hydrosphere in partitioning rainfall betweeninfiltration and runoff, which affect the lithosphere through erosional processes.Soils also buffer and filter pollutants that affect water quality, a process in whichthe buffering properties of soils are adjusting continuously to changing environ-mental conditions. Furthermore, soils are an integral part of the biosphere, inter-acting with the biogeochemical and physical climate systems, affecting productivity,carbon fluxes, and biodiversity (Figure 1.2). Human-induced changes in land usealter the biogeochemical system, which affects the physical climate system. A betterunderstanding of these fundamental soil processes is needed to solve many environ-mental challenges facing society.

Space and airborne sensor systems, with their synoptic and repetitive coverage ofthe land surface, are increasingly being relied on to characterize and map the spatialvariation of soil physical and biogeochemical properties for environmental and nat-ural resource management purposes. Soils exhibit great spatial variability across anylandscape as a result of morphogenetic properties unique to the environment underwhich they were formed. The pedogenic processes include climate, geology, topog-

Remote Sensing for Natural Resources Management and Environmental Monitoring:Manual of Remote Sensing, 3 ed., Vol. 4, edited by Susan Ustin.ISBN: 0471-31793-4 � 2004 John Wiley & Sons, Inc.

4 REMOTE SENSING OF SOILS AND SOIL PROCESSES

Figure 1.1 Pedosphere in the biosphere–geosphere system.

Atmospheric Physics / DynamicsClimateChange

HumanActivities

TerrestrialEnergy / Moisture

Soil GreenhouseGases

TerrestrialEcosystems

LandUse

Tropospheric Chemistry

Pollutants / Greenhouse Gases

External Forcings

Biogeochemical Cycles

PhysicalClimateSystem

Figure 1.2 Role of soils in Earth system science. See CD-ROM for color image.

1.2 PROPERTIES CONTROLLING SOIL REFLECTANCE 5

raphy, biotic factors, human activities, and history (natural and anthropogenic). Soilproperties are thus a function of both the current history and the paleohistory ofthe soil environment and soil responses to land management schemes, and environ-mental forcings are determined largely by these natural soil properties (Bouma,1994).

Effective management and monitoring of soil resources require spatial data atvarious scales in order to incorporate land use patterns, geomorphology, topogra-phy, and hydrologic and vegetation parameters. Remote sensing may be the onlyfeasible means of providing such spatially distributed data at multiple scales and ona consistent and timely basis. In this chapter we examine the application of remotesensing instruments and techniques in providing information for soil studies relatedto ecosystem sustainability, drought mitigation, human health, biogeochemical andcarbon cycling, erosion and sediment yield, water balance and water quality, tracegas models, and land use and land cover change. We examine how remote sensingis utilized to characterize soils and monitor changes in response to land use andclimate patterns. We also look at how remote sensing can be integrated with geo-graphic information systems (GIS), dynamic process models, and field data at variousspatial and temporal scales in working toward sustainable land use.

1 . 2 P R O P E R T I E S C O N T R O L L I N G S O I L R E F L E C TA N C E

Much is known about soil optical properties through extensive laboratory analysesand field-based radiometric studies. Optical remote sensing techniques measure theradiation emitted and reflected from the immediate soil surface, as there is very littlepenetration of electromagnetic energy through the opaque soil medium. Liang(1997) found the sensible depth to be only four or five times the particle size effec-tive radius. The spectral composition of soil-reflected and emitted energy is primarilydependent on the biogeochemical (mineral and organic) constituents, optical–geometric scattering (particle size, aspect, roughness), and moisture conditions ofthe surface (Baumgardner et al., 1985; Irons et al., 1989; Ben-Dor et al., 1999).The aim of remote sensing is to exploit and model these complex relationships andpatterns of surface energy interactions for the purpose of mapping and extractinginformation about the biophysical and biochemical character of soils.

1.2.1 Biogeochemical Properties

Soil spectral reflectance signatures result from the presence or absence as well as theposition and shape of specific absorption features of its constituents. Absorptionsare brought about by various chemical–physical phenomena such as intermolecularvibrations and electronic processes in atoms. The visible and near-infrared (NIR)regions (0.4 to 1.3 �m) are characterized by broad spectral absorption features suchas the yellowish ferrous iron absorption feature near 1 �m, and weaker absorptionsat 0.7 and 0.87 �m attributed to red, ferric iron. Strong charge transfers betweenFe and O in the blue and ultraviolet result in fairly steep decreases in reflectancewith shorter wavelengths. As a result, most soils exhibit increasing reflectance withwavelength over the visible to NIR portion of the spectrum, since iron is fairly


Figure 1.3 Five unique soil spectral signature types. (After Stoner and Baumgardner, 1981.)

ubiquitous (Mulders, 1987). Soils have distinct spectral features in the shortwave-infrared (SWIR) region (1.3 to 2.5 �m) caused by vibrational processes, which in-clude two broad water absorption bands at 1.4 and 1.9 �m. Minerals with OH,CO3 (calcite), and SO4 (gypsum) exhibit vibrational features in the 1.8 to 2.5 �mregion, while layer silicates with OH absorb near 1.4 and 2.2 �m (Baumgardner etal., 1985; Mulders, 1987).

Soils are mixtures of a number of inorganic and organic constituents, so it is notstraightforward to evaluate the composition of soils from their spectral signatures(Ben-Dor et al., 1996). Many soil spectral signatures match closely, making it dif-ficult to distinguish them. As a result, only a limited number of soil spectral curveforms have been found discriminable with remote sensing. Condit (1970) analyzed160 soil spectral reflectance curves from 0.32 to 1.0 �m and found only threeprincipal spectral curve shapes. Stoner and Baumgardner (1981) analyzed a greaternumber and geographic range of soils (485 soils), from 0.50 to 2.45 �m, and doc-umented five unique soil spectral curve shapes related primarily to their relativecontents in organic matter and iron and modulated by their textures (Figure 1.3).These, along with numerous laboratory and field studies, have shown that soil spec-tral signatures are controlled largely by the iron oxides, organic molecules, and waterthat coat soil particle surfaces. The curve forms represent (A) organic-dominatedsoils with high organic matter contents and low iron contents; (B) minimally alteredsoils with both low organic matter and low iron contents; (C) iron-affected soilswith low organic matter contents and medium iron contents; (D) organic-affectedsoils with high organic matter contents, not fully decomposed, and low iron con-tents; and (E) iron-dominated soils with high iron contents and lower organic matteramounts.

Courault et al. (1988) formulated various spectral indexes, such as an SO index(SO � �750 nm/�450 nm), which differentiates most organic soils from calcareous soilsand an RF index (RF � �700 nm � �900 nm), which distinguishes soils with high ironcontents. Ben-Dor and Bannin (1994) utilized a visible and near-infrared analysisscheme, involving spectral compression and spectral derivative analysis, to predicta wide variety of soil chemical constituents from fine resolution spectra of arid andsemiarid soils. Their technique required between 15 and 350 spectral bands in thevisible–NIR for optimal prediction of soil chemical constituents, including CaCO3,Fe2O3, Al2O3, SiO2, free iron oxides, and K2O.

1.2 PROPERTIES CONTROLLING SOIL REFLECTANCE 7

The presence of leaf litter and other nonphotosynthetic vegetation (NPV) on thesoil surface influences the resulting soil spectral signatures. Many studies have in-vestigated the chemical and optical properties of soil organic matter as a functionof plant source and aging (Aber et al., 1990). Stoner and Baumgardner (1981)showed three unique spectral signatures representative of various stages of litterdecomposition in soils. A fibric curve had the most tissue morphology intact andthus high reflectance properties; hemic curves resulted from intermediate levels ofdecomposition; and sapric curves represented mostly decomposed litter and werevery low reflecting. Ben-Dor et al. (1997) found that the slopes in the visible–NIRspectral region and specific absorption features in the NIR–SWIR region were usefulin assessing the optical properties of soil organic matter at several stages of biologicaldecomposition. McLellan et al. (1991) used a near-infrared analysis (NIRA) meth-odology to predict the amounts of nitrogen, lignin, and cellulose during the decom-position process of leaf materials to soil organic matter.

1.2.2 Optical–Geometric Interactions

Most soil surfaces scatter incident radiation anisotropically, a consequence of itsthree dimensional structure. Scattering occurs as diffuse and specular reflection andis a function of the geometric properties (particle size, aspect, roughness) of thesurface, sensor viewing angle, solar illumination angle, and the relative azimuthalpositions of the sun and sensor relative to the surface (Kimes et al., 1984; Irons etal., 1989; Ben-Dor et al., 1999). With the shortest wavelengths most affected,roughness and sun–soil–sensor geometries alter a soil’s spectral signature and theinferences of basic soil properties such as soil mineralogy. Remote sensing data takenunder different sun and viewing geometries are not necessarily comparable withoutcorrection for these angular effects.

Particle size distribution and surface height variation (roughness) are the mostimportant factors influencing the directional reflectance of bare soils. They cause adecrease in reflectance with increasing size of roughness elements, as coarse aggre-gates contain a lot of interaggregate spaces and light traps. Smooth, crusted, andstructureless soils generally reflect more energy and are brighter. Despite having afiner particle size distribution, clayey soils tend to be darker than sandy soils, sinceclays aggregate and behave as larger, ‘‘rougher’’ surfaces.

The bidirectional reflectance distribution function (BRDF) specifies the behaviorof surface reflectance and scattering as a function of view and illumination anglesfor a given wavelength. The equation describing this function is

L(� ,� ,� ,� , �)s s v vBRDF � (1.1)E(�)

with units of sr�1 � �s and �s refer to the solar zenith angle and solar azimuth angle,respectively, while �v and �v are the sensor view zenith and view azimuth angles,respectively. Often, however, it is defined simply as the bidirectional reflectancefactor (BRF � �BRDF) at a multitude of view zenith and azimuthal angles for agiven sun position (Walthall et al., 1985). The BRDF is an intrinsic physical property


of the surface that may be used to derive geometric descriptors of a soil, such assize, shape, and orientation of surface roughness elements.

Considerable effort has been made toward the development of empirical andradiative transfer models to describe the BRDF patterns of soil surfaces (Pinty et al.,1989; Deering et al., 1990; Irons et al., 1992; Jacquemoud et al., 1992; Liang andTownshend, 1996; Ben-Dor et al., 1999). In general, the angular behavior of thesurface is a function of coherence effects, which largely account for the hotspot;volume scattering effects associated with porous media; and surface effects involvingshadowing and surface geometry. Knowledge of the BRDF of a soil surface allowsfor the correction of angular reflectance variation by normalizing responses to agiven viewing geometry, such as nadir. When integrated across a hemisphere, theBRDF provides albedo, the ratio of shortwave (0.4 to 4 �m) radiant energy scatteredin all directions to the downwelling irradiance incident on the surface. Albedo is afundamental variable in energy balance studies, climate modeling (Middleton et al.,1987), and soil degradation studies. Jacquemoud et al. (1992) developed the SOIL-SPECT model to describe soil optical properties from 450 to 2450 nm. They showedthat the single scattering albedo spectrum represents the intrinsic soil spectral prop-erties, dependent on the biogeochemical constituents and moisture conditions whilebeing independent of the measurement geometry. This is of great importance incomparing soil spectra acquired under different angular measurement conditions.

1.2.3 Soil Moisture

Soil moisture has a strong influence on the amount and composition of reflectedand emitted energy from a soil surface, and thus information about soil moisturecondition can be derived from measurements in all parts of the electromagneticspectrum. In the shortwave region, the major effect of adsorbed water on soil re-flectance is a decrease in reflected energy, making soils darker when moistened,particularly in the water absorption bands centered at 1.45 and 1.9 �m (Reginatoet al., 1977). The decrease in reflectance is proportional to the thickness of thewater film around the soil particles and can be related to gravimetric water contentas well as the energy status of the adsorbed water (Idso et al., 1975).

Soil thermal behavior is largely a function of soil moisture, which modulates theheating and cooling of a soil through a partitioning of radiant energy into latentand sensible heat components. The difference in the amplitude of the diurnal vari-ation in temperature across soil surfaces is a result of their differences in thermalinertia, which is related primarily to their moisture content and texture properties(Mulders, 1987). Reginato et al. (1976) showed how daily maximum–minimumsurface soil temperatures as well as maximum–minimum soil–air temperatures wereinversely related to the soil water content (0 to 2 cm) in soils.

Both active and passive microwave remote sensing can accurately measure surfacesoil moisture contents in the top 5 cm of the soil. The longer wavelengths (�5 cm)are best suited for soil moisture determinations (Ustin et al., 1991; Engman, 1995).The theoretical basis for microwave remote sensing measurements of soil moistureresult from the large contrast between the dielectric properties of liquid water anddry soil. An increase in soil moisture content results in higher dielectric constants.At L-band frequencies (1 to 2 GHz or 15 to 30 cm), the dielectric constant of water

1.3 DERIVING SOIL PROPERTIES AT THE LANDSCAPE SCALE 9

is 80 and that of dry soil is 3 to 5, and the dielectric constant can increase to over20 with increasing soil moisture. In passive microwave remote sensing of soils, thebrightness temperature (TB) is measured, which is the product of the surface tem-perature and surface emissivity. Surface emissivities can typically vary from 0.95when dry to less than 0.6 when wet, and are also sensitive to surface roughness.For active microwave remote sensing of soils, the measured radar backscatter, ,0�s

is related directly to soil moisture but is also sensitive to surface roughness.

1 . 3 D E R I V I N G S O I L P R O P E R T I E S AT T H EL A N D S C A P E S C A L E

At the landscape level, it is much more difficult to measure soil properties andextract soil information with air- and spaceborne sensors. This is due to the extremespatial variability of soil properties and masking of the soil surface by vegetationand litter. One must further separate spectral variation associated with external mea-surement conditions, instrument calibration, topography, and atmosphere fromthose caused by variations in ‘‘inherent’’ soil properties and surface materials (Huete,1996). The atmosphere has a strong effect on the spectral signatures of dark surfaceswhile having minimal effects on brighter, sand surfaces. The discrimination andmapping of soil types and soil properties become not only a function of the opticalproperties of the surface materials but also sensor characteristics such as number ofwavebands, bandwidths, spatial resolution, and instrument noise. The wealth ofknowledge available from laboratory, field, and model studies, however, provide astrong foundation and starting point for the extraction of soil information at themore heterogeneous landscape level.

There is a wide array of airborne and satellite sensor systems currently availablefor the study of soils at the landscape level and for advancing management of thesoil resource from space (Table 1.1). Airborne and satellite sensors can measure theenergy reflected and emitted from the land surface over a wide range of spatial andtemporal scales and as a function of wavelength, sun-view geometry, and polariza-tion state. Generally, improved soil discrimination capability is achieved with finerspatial and spectral resolutions, with directional measurements providing furtherinformation. Remote sensing data are used to characterize and map soils in severalways:

• Remote sensing can provide ‘‘direct’’ measurements comparable to traditionalfield forms. Direct measurements are extracted from exposed soil surfaces invegetated (e.g., canopy gaps) and nonvegetated areas and are interpreted usingthe extensive knowledge gained from laboratory and field studies. Direct mea-surements provide information on soil spectral signatures, moisture, and tex-tural properties.

• Mixture models and soil indexes are employed in partially vegetated areas toisolate and discriminate soil properties and to generate soil component images.

• Soil type and soil properties, including subsoil characteristics (e.g., water-holding capacity), may also be inferred from remote sensing measurements ofthe overlying vegetation canopy.

10

TABL

E1.

1Fin

eRe

solu

tion

Sens

ors

Usef

ulin

Soil

Stud

ies

Sens

orPi

xelS

ize,m

‘blue

’nm

‘gre

en’n

m‘re

d’nm

‘NIR

’nm

‘SW

IR’�

m‘T

herm

al’�

m

Land

sat

MSS

Swat

h18

5km

8050

0–60

060

0–70

070

0–80

080

0–11

00La

ndsa

tT

M4,

5Sw

ath

185

km30 12

0(T

IR)

450–

520

520–

600

630–

690

760–

900

1.55

–1.7

52.

08–2

.35

10.4

–12.

5La

ndsa

tE

TM

+Sw

ath

185

km30 60

(TIR

)45

0–51

552

5–60

563

0–69

077

5–90

01.

55–1

.75

2.09

–2.3

510

.4–1

2.5

SPO

T1,

2,3

(XS)

Swat

h60

kmSP

OT

-4(H

RV

IR)

Swat

h60

kmSP

OT

-5Sw

ath

120

km

20 10(p

an)

20 10(m

ono)

10 20 2.5

or5

(pan

)48

0–71

0

500–

590

500–

710

500–

590

500–

590

610–

680

610–

680

610–

680

610–

680

790–

890

790–

890

780–

890

1.58

–1.7

5

1.58

–1.7

5

AST

ER

Swat

h60

km15

(VN

IR)

30(S

WIR

)

90(T

IR)

520–

600

630–

690

760–

860

1.60

0–1.

700

2.14

5–2.

185

2.18

5–2.

225

2.23

5–2.

285

2.29

5–2.

365

2.36

–2.4

38.

125–

8.47

58.

475–

8.82

58.

925–

9.27

510

.25–

10.9

510

.95–

11.6

5IR

S-1A

,1B

Swat

h14

0km

IRS-

1C,

1-D

32 20 5(p

an)

190

(WiF

S)

450–

520

520–

590

520–

590

500–

750

620–

680

620–

580

620–

680

770–

860

770–

860

770–

860

1.55

–1.7

0

Ikon

osSw

ath

13km

4 1(p

an)

450–

520

520–

600

630–

690

760–

900

Qui

ckB

ird

Swat

h16

.5km

2.44

0.61

(pan

)45

0–52

052

0–60

063

0–69

076

0–90

0


• Remote sensing measurements may be used to generate topography, land cover,and land use information for soil studies.

1.3.1 Direct Measurements

In arid and hyperarid regions, there has been much success in mapping soils withremote sensing due to dominance of the soil spectral signal and minimal interferencefrom vegetation. The spectral variety of the surface tends to be high, as soils varywith the underlying geologic and depositional materials and soil mineralogy domi-nates spectral reflectances. Bright sands, volcanic materials, and iron-rich weatheredsoils can be found in close proximity to each other, and the presence of biogeniccrusts adds further to the spectral variety of arid and semiarid soils (Figure 1.4).

Aerial photos have been used routinely in soil survey and mapping in the UnitedStates since the 1930s. In the early 1960s, color photos greatly improved delineationof soil mapping units and soil drainage characteristics (Irons et al., 1989). In 1972,the Landsat satellite program introduced multispectral data sources and pattern rec-ognition techniques for soil mapping. The higher-quality, consistency, and synopticdata coverage greatly improved the efficiency of soil survey procedures over aerialphotography. The first three Landsat satellites contained the Multispectral Scanner(MSS) sensor, consisting of four broad bands in the visible and NIR with a pixelsize of about 80 m. Donker and Mulder (1977) demonstrated the utility of multi-spectral digital data by generating band ratio and principal components analysis(PCA) images, which greatly enhanced the discrimination of soil spectral classes.Ratios were able to enhance soil absorption features while minimizing illuminationinfluences. Weismiller et al. (1977) combined pattern recognition techniques of MSSdata with various ancillary digital data sets, such as watershed characteristics, toproduce more detailed delineations of soil mapping units. Other studies furtherdemonstrated the utility of MSS data as an aid in soil surveying and mapping inarid and semiarid regions (Horvath et al., 1984; El-Hady et al., 1991).

The Landsat 4 and 5 satellites, launched in 1982 and 1984, respectively, includedthe Thematic Mapper (TM) in addition to the MSS sensor. The TM sensor has sixbroad spectral bands with a pixel size of 30 m, and one band in the thermal, in-creasing greatly the multispectral differentiation of soil surfaces and allowing moreopportunity to image ‘‘pure’’ soil surfaces. Escadafal and Pouget (1986) conductedsoil survey projects in southern Tunisia with the higher spatial and spectral capa-bilities of the TM and were able to identify soil types and make soil resource de-scriptions and land use suitability. Prasad et al. (1990) used TM data to map soiland land resources of northern Karnataka, India and produced land capability classesfor development and land use planning. Baumgardner et al. (1985) presented a goodsummary of the utility of the spectral and spatial data provided by the Landsatsensors in aiding soil survey, mapping, and management. The Enhanced ThematicMapper (ETM�), launched in April 1999, further improved Landsat capabilities forsoil mapping with an improved 15-m panchromatic band and a 60-m thermal band(Table 1.1). Landsat TM and ETM� provide fine-resolution SWIR and TIR bandsuseful in soil moisture and drought studies (see Section 1.4.3).

Coarse- and moderate-resolution satellites have also been found useful in map-ping soils in arid and hyperarid zones where the dominance of the soil signal is


Figure 1.4 MODIS image of the Nile from Khartoum to Aswan showing the large spectral variety of soil surfaces in aridregions (bands 1, 3, and 4 reflectance composite at 250-m resolution; November 4, 2000). See CD-ROM for color image.(Courtesy of Jacques Descloitres, MODIS Land Rapid Response Team, NASA / GSFC.)

relatively scale independent. Escadafal and Pouget (1986) utilized the visible andnear-infrared channels of the National Oceanic and Atmospheric Administration(NOAA) Advanced Very High Resolution Radiometer (AVHRR) 1.1-km data to mapsoils over extensive and poorly mapped areas in the Sahara (Table 1.2). The Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) was launched in August 1997 and alsoprovides global monitoring image data sets in eight narrow color bands between400 and 900 nm, at 1.1-km resolution, yielding useful information related to soilphysical properties and surface color. Soil color provides a simple surrogate measureto estimate many other difficult-to-measure soil constituents and is widely used insoil mapping as an indicator of the presence of organic matter, iron oxides, andcarbonates, as well as moisture.

13

TABL

E1.

2M

ediu

man

dCo

arse

Reso

lutio

nSe

nsor

sUs

eful

inM

onito

ring

Soil

Proc

esse

s

Sens

orPi

xelS

ize,m

‘blue

’nm

‘gre

en’n

m‘re

d’nm

‘NIR

’nm

‘SW

IR’�

m‘T

herm

al’�

m

SPO

T-V

EG

ET

AT

ION

Swat

h22

50km

1150

430–

470

610–

680

780–

890

1.58

–1.7

5

NO

AA

-A

VH

RR

Swat

h27

00km

1100

570–

700

710–

980

3.5–

3.93

10.3

–11.

311

.5–1

2.5

SeaW

iFS

Swat

h28

01km

1100

402–

422

433–

453

480–

500

500–

520

545–

565

660–

680

745–

785

845–

885

MO

DIS

(Ter

ra,

Aqu

a)Sw

ath

2330

km25

050

0

1000

459–

479

405–

420

438–

448

483–

493

545–

565

526–

536

546–

556

620–

670

662–

672

673–

683

841–

876

743–

753

862–

877

890–

920

915–

965

931–

941

1.23

–1.2

51.

628–

1.65

22.

105–

2.15

51.

360–

1.39

03.

66–3

.84

3.92

9–3.

989

4.02

–4.0

84.

433–

4.49

84.

482–

4.54

96.

535–

6.89

57.

175–

7.47

58.

400–

8.70

09.

580–

9.88

010

.78–

11.2

811

.77–

12.2

713

.185

–13.

485

13.4

85–1

3.78

513

.785

–14.

085

14.0

85–1

4.38

5M

ISR

(Ter

ra)

Swat

h36

0km

9an

gles

,0�

70.5

�

275

425–

467

543–

572

660–

682

847–

886

SAC

-C/M

MR

SSw

ath

360

km17

548

0–50

054

0–56

063

0–69

079

5–83

51.

55–1

.70


Spatial and temporal variations in soil surface color yield important clues to landdegradation processes such as salinization, erosion, and drainage status of a soil (Latzet al., 1984; Mougenot et al., 1993; Thompson and Bell, 1996). The presence ofgrayish soil colors, for example, is indicative of poor drainage and waterlogging.Many studies have shown the relationships between soil color and remotely sensedoptical measurements, which enable the coupling of extensively published soil colorinformation with remote sensing data (Fernandez and Schulze, 1987; Escadafal etal., 1988, 1989; Escadafal, 1993; Post et al., 1994; Galvao et al., 1997; Mattikalli,1997; Mathieu et al., 1998).

The Moderate Resolution Imaging Spectroradiometer (MODIS) is a key instrumentonboard the Earth Observing System (EOS) Terra and Aqua platforms launched inDecember 1999 and May 2002, respectively (Salomonson et al., 1989; Running etal., 1994). The Terra and Aqua satellites are in sun-synchronous orbits with local-time equator crossings of 10:30 A.M. and 1:30 P.M. respetively. Each MODIS in-strument provides spatial and temporal Earth surface observations in 36 bands withnominal spatial resolutions of 250 m, 500 m, and 1 km, one- to two-day repeatcycles, with a wide (�55�) field of view, scanning 2300 km (Figure 1.5). MODISincludes a set of nine ocean color bands in the range 405 to 877 nm, which provideinformation on coastal zone sediments from the land. There is also a set of thermalbands applicable to fire detection and surface temperature monitoring.

1.3.2 Landscape Mixtures

In more mesic environments, there are opportunities to sense the soil surface re-motely; however, more often than not, soil surfaces will contain significant quan-tities of litter and vegetation, masking the soil signal and rendering the extractionand interpretation of soil information more difficult. The problem becomes moreprevalent in coarser-resolution imagery, where there is less likelihood of finding puresoil pixels. Despite these difficulties, soil influences on the reflectance properties ofthe landscape are clearly evident and potentially can be analyzed. Approximately70% of Earth’s terrestrial surface consists of open canopies with vegetation overlyingvarious proportions of exposed soil and litter background (Graetz, 1990). Opencanopies include deserts, tundra, grasslands, shrublands, savannas, woodlands, wet-lands, and many open-forested areas and result in complex spatial and temporalvariations in their surface components.

Resolving the extent and spatial heterogeneity of soil, litter, and vegetation coveris important for assessments of landscape, hydrologic, and biogeochemical processesand is crucial for functional analyses of these environments (Asner and Lobell,2000). For example, soil erosion processes are dependent on the amount and dis-tribution of protective vegetation and litter cover, while carbon and nitrogen uptakeand evapotranspiration are dependent on the green vegetation fraction.

1 .3 .2 .1 MIXTURE MODEL ING

The remotely sensed spectral response from open canopies is a function of thenumber and type of reflecting components, their optical properties, and their relativeproportions. The optical properties and mixture proportions further vary seasonally


Figure 1.5 MODIS image showing sediment transport at the mouth of the Yangtze River, China (bands 1, 3, and 4reflectance composite at 250-m resolution; September 16, 2000). See CD-ROM for color image. (Courtesy of Jacques Descloitres,MODIS Land Rapid Response Team, NASA / GSFC.)

and with land cover conversions (Adams et al., 1995). Spectral mixture analyses arewidely used to unmix the soil–plant canopy measurement into their respective soil,vegetation, and NPV signal contributions. Spectral mixture models have utility in avariety of applications, including biogeochemical studies, leaf water content, landdegradation, land cover conversions, fuelwood assessment, and soil and vegetationmapping (Smith et al., 1990; Gillespie, 1992; Ustin et al., 1993; Gao and Goetz,1994).

Mixture modeling generally involves three steps: (1) assessment of the dimen-sionality or number of unique reflecting materials in a landscape, (2) identificationof the physical nature of each of the landscape components or endmembers within


a pixel, and (3) determination of the amounts of each component in each pixel. Thefirst step can be accomplished with principal components analysis (PCA), whichcompresses (data reduction) the data into a minimal number of relevant componentimages, equivalent to the dimensionality of a scene. The second step is achievedwith endmember analysis whereby various reference spectra are used to model ascene. Several types of endmember spectral signatures are utilized in spectral mixtureanalyses, including image endmembers, reference endmembers, and bundled spectraendmembers, and which may be laboratory or field measured. Typical endmembersused in remote sensing include green vegetation, soil, NPV, and shade. Shade is usedto model pixels with variable illumination conditions resulting from topography,sun angle, and shadowing (Adams et al., 1986). Reference spectra of specific ma-terials are catalogued into spectral libraries. Elvidge (1990) created a spectral libraryof nongreen vegetation components from many plant species and materials. Onecan also model the spectral variability of a scene with several image endmembersfollowed by the use of reference endmembers to determine the composition of theimage endmembers (Farrand et al., 1994). Spectral libraries are used not only toidentify and classify soils, vegetation, and NPV, but also to provide a reference forcomparing temporal sequences and compositional changes of the fractional com-ponents.

The products of mixture modeling include a set of component fractional imagesand a set of residual images containing the differences between modeled and mea-sured data. In matrix notation, this is expressed as

[D] � [R] [C] � [] (1.2)

where [D] is the multiband scene data, [R] the reflectance matrix of the independentreflecting components (endmembers), [C] the loadings matrix or fractional images,and [] the residual images. Residual images contain the bulk of the noise and errorin images; however, they are also a quick way to find outlying pixels not adequatelyexplained by the mixture model and may indicate the presence of unknown orunusual reflecting components. The main limitation of scene unmixing is that thecomponents extracted must have unique spectral signatures (i.e., be differentiablein at least one part of the measured spectra). If the soil signature can be unmixedand extracted successfully, direct soil remote sensing methods can be used to derivesoil properties.

Soil–plant spectral mixtures may be modeled as linear or nonlinear mixtures. Inthe linear case, also known as the checkerboard model, photons interact with onlya single material and the fractions of each material are equivalent to their aerialproportions. Linear models are easy to use and work quite well over certain typesof land cover conditions, such as desert shrub canopies (McGwire et al., 2000).

1 .3 .2 .2 HYPERSPECTRAL SENSORS

The use and application of mixture models have become more relevant with thedevelopment of imaging spectrometers such as the Airborne Visible /Infrared ImagingSpectrometer (AVIRIS) and the spaceborne Hyperion satellite sensor. Hyperspectralimaging sensors, with a large number of contiguous bands, carry valuable diagnosticinformation about many Earth surface materials and have improved the feasibility


(a) (b)

Figure 1.6 (a) AVIRIS-derived cover fraction images of green canopy, litter, and bare soil at the Jornada LTER site; (b)their standard deviations using a mixture analysis model. (After Asner and Lobell, 2000.)

of unambiguously identifying numerous soil and plant absorption features, relatedto mineralogy, liquid water, chlorophyll, cellulose, and lignin content. The AVIRISsensor is of particular interest in soil spectral studies. AVIRIS operates in the region400 to 2450 nm collecting 224 spectral bands with a nominal 10-nm spectral re-sponse function (Vane et al., 1993). This enables one to observe fine resolutionspectra associated with unique absorption features of various minerals and organicconstituents, which are typically lost in coarse waveband data. AVIRIS is a whisk-broom scanner with four grating spectrometers that is normally flown on an ER-2at an altitude of 20 km, resulting in a swath width of 10 km and a 20-m pixel size.AVIRIS can also be flown on a Twin Otter aircraft at lower altitudes with corre-spondingly finer resolutions (pixels of 4–5 m).

Asner and Lobell (2000) developed and successfully implemented an automatedSWIR (2100 to 2400 nm) spectral unmixing algorithm to estimate the extent ofbare soil and vegetation over a diverse range of 17 arid and semiarid sites in Northand South America. The approach involved a Monte Carlo unmixing (MCU) strat-egy to derive the subpixel cover fractions with statistical confidence intervals. UsingAVIRIS imagery over the Sevilleta and Jornada long-term ecological research (LTER)sites in New Mexico, they derived the fractional cover of green, litter, and soil coverwith a standard deviation of approximately 5% (Figures 1.6 and 1.7). These figuresdisplay the fractional amounts of exposed soil along with litter and green vegetation.Residuals in the SWIR region, attributable to cellulose and lignin in the vegetation,have been used to resolve nonphotosynthetic vegetation (NPV) signals from soil(Roberts et al., 1993).


Deep Well

Five Points

Valley de la Joya

BroncoWell

RioSalado

Green Canopy

Litter

Soil

Figure 1.7 AVIRIS color composite image of green canopy, litter, and soil fractions from a mixture model applied at theSevilleta LTER site. See CD-ROM for color image. (After Asner and Lobell, 2000.)

Okin et al. (2001) applied multiple-endmember spectral mixture analysis(MESMA) on AVIRIS imagery collected over the Manix Basin of the Mohave Desertin California and were able to reliably distinguish and map soil surface types as wellas fractional soil and vegetation cover (Figure 1.8). They found soil type retrievalsparticularly robust in cases where soil spectral signatures are significantly different,as in the dark-armored desert soils and blown sands retrieved in Figure 1.8. Theareas modeled as ‘‘sandy/blown soil’’ provided information on wind erosion pro-


Figure 1.8 AVIRIS-derived vegetation type (a), vegetation cover (b), and soil surface type (c) retrievals using a MESMAmixture analysis scheme. See CD-ROM for color image. (After Okin et al., 2001.)

cesses resulting from abandoned agricultural fields and other anthropogenic distur-bances. They also noted that the accuracy of retrievals of soil type was partlydependent on the type and fractional cover of overlying vegetation employed in themixing model. Drake et al. (1999) employed spectral matching and mixture tech-niques to AVIRIS data to generate maps of different clay minerals.

There are many other imaging, hyperspectral sensors operated onboard aircraft,including the Compact Airborne Spectrographic Imager (CASI), the Geophysical andEnvironmental Research Imaging Airborne Spectrometer (GERIS), and HyperspectralMapping (HyMap) system, some of which can provide imaging spectrometer cov-erage in the thermal region. The Hyperion hyperspectral imager is a pushbroomsensor, launched in November 2000 onboard the Earth Observing-1 (EO-1), thefirst satellite in NASA’s New Millennium Program Earth Observing series (Ungar etal., 2003). Hyperion collects data in 220 10-nm bands covering the spectrum from400 to 2500 nm. The EO-1 satellite flies in formation with Terra and ETM�, pro-viding hyperspectral data of common targets. Various studies documenting the ca-pabilities of spaceborne hyperspectral imagery for studies on soil, mineralogy,canopy chemistry, and environmental degradation have been conducted (e.g., Kruseet al., 2003; Huete et al., 2003; Smith et al., 2003).

1.3.3 Inferring Soil Properties through Vegetation

In more humid climates, the soil signal becomes a minor component of scene spec-tral content, and it becomes very difficult to find exposed soil surfaces not covered


by leaf litter or vegetation. About 25 to 30% of terrestrial land surfaces have closed,dense vegetation canopies with leaf area index (LAI) values exceeding 1, encom-passing tropical forests, temperate forests, and cultivated cropland. Where the sur-face is strongly vegetated, soil properties must be inferred from measurements ofthe vegetated surface in conjunction with models, field data, and knowledge of land-form. Soil contributions to the sensor measured signal may become significant againwith land conversion activities that expose the soil surface, such as in clearcuttingof forests, biomass burning, blowdowns, agricultural and pasture expansions, andnatural disasters (Adams et al., 1995).

1 .3 .3 .1 VEGETAT ION INDEXES

Spectral vegetation indexes (VIs) as well as mixture models are commonly used tomap vegetation characteristics from which one can infer soil properties. The nor-malized difference vegetation index (NDVI) has been most commonly used to mapspatial and temporal variations in vegetation (Tucker, 1979). The NDVI is a nor-malized ratio of the NIR and red bands,

� � �NIR redNDVI � (1.3)� � �NIR red

where �NIR and �red are the surface bidirectional reflectance factors. The strength ofthe NDVI is in its ratioing concept, which reduces many forms of multiplicativenoise (illumination differences, cloud shadows, atmospheric attenuation, certain top-ographic variations) present in multiple bands. As a result, the NDVI is sufficientlystable to permit meaningful comparisons of seasonal and interannual changes invegetation growth and activity.

Levine et al. (1987) studied soil and vegetation patterns over South America usingthe NDVI from the AVHRR sensor. They found positive, although not strong cor-relations between NDVI and soil properties such as percent base saturation, acidity,water-holding capacity, and bulk density, after grouping soils by climate. Levine etal. (1994) found significant statistical relationships between NDVI and soil mappingunits using finer-scale AVIRIS-derived NDVI in mixed conifer forests near Howland,Maine. Moderately well-drained soil classes had the highest net primary productivityand vegetation vigor, as explained by the NDVI, while very poorly drained organicsoils had the lowest NDVI values. It was more difficult to relate the NDVI withother, highly spatially variable soil properties; however, many of these properties(e.g., water-holding capacity) were not limiting factors and thus had minimal effectson landscape variability. They concluded that spatial information on disturbance,forest stand history, and land management (clearcutting, herbicide use, planting) hadto be taken into account to provide additional means of relating soil and ecosystemproperties with remotely sensed data. Lozano-Garcıa et al. (1991) also utilizedAVHRR–NDVI data and found clear relationships between soil associations andbiomass development for study areas in Indiana. Establishing such linkages is a com-plex task that requires a basic understanding of the interactions among soils, land-scapes, and vegetation.

Climate-driven interannual variations in satellite-derived NDVI have been de-tected and used in studies of desertification and drought in the Sahel (Nicholson et


al., 1998). In arid and semiarid environments, linear relationships between NDVIand rainfall have been found in areas receiving less than 500 mm of annual rainfall,beyond which the NDVI response to rainfall saturates (Smith et al., 1990; Nicholsonand Farrar, 1994). Nicholson and Farrar (1994) found edaphic influences in thesavannas in Botswana whereby NDVI–rainfall relationships, including the time lagof NDVI response to rainfall, were controlled largely by soil type. The highest NDVIvalues occurred in the more clayey soils (higher moisture retention) for given rainfallamounts. Farrar et al. (1994) further found the ratio of NDVI to soil moisture tovary significantly among the soil types studied in Botswana, thus limiting use of theNDVI as an indicator of rainfall. A better interpretation of NDVI–soil moisture–rainfall relationships could be achieved by removing the NDVI–soil brightness op-tical effects so that NDVI values could be related unambiguously to vegetationamounts and condition.

There are some concerns on the use of the NDVI to infer soil properties. TheNDVI is strongly influenced by brightness variations in canopy background (soils,litter, etc.), making it difficult to distinguish between vegetation and soil-inducedchanges on NDVI values (Huete et al., 1985; Huete and Tucker, 1991). This isparticularly troublesome if the goal is to derive soils information from knowledgeof the vegetation condition. In a satellite monitoring of Sahelian grasslands, Kam-merud (1996) suggested that independently derived soil spectral maps would beneeded to decouple the strong effect of soils on the NDVI. There are other red–NIR vegetation indexes that are not prone to soil influences, such as the perpen-dicular vegetation index (PVI; Richardson and Wiegand, 1977), the soil-adjustedvegetation index (SAVI; Huete, 1988), and first-derivative hyperspectral vegetationindexes.

Kauth and Thomas (1976) developed the tasseled cap model, an orthogonal setof spectral indexes that separated soil brightness from greenness and yellownessscene components in four-band MSS imagery. Crist and Cicone (1984) later mod-ified the tasseled cap transformation for use with six-band TM images, and Jackson(1983) provided an orthogonalization methodology to create n-space indexes ca-pable of decoupling a wide variety of unique spectral features in any number (n) ofbands. These orthogonal, linear-based indexes utilize the soil line concept for pur-poses of vegetation monitoring by decoupling the green vegetation signal from thatdue to the spectral variety of soil backgrounds. The soil line, however, is an over-simplification, as there is much variability among soils, forming different soil lineaxes (Huete et al., 1984; Galvao and Vitorello, 1998).

Another concern is the saturation effect of NDVI behavior over densely vegetatedlandscapes, which would inhibit the detection of landscape spatial variations result-ing from differences in soil properties. Solutions to this problem include the use ofa green or SWIR–based NDVI in which the chlorophyll-saturated red reflectanceband is replaced by the green or SWIR band (Miura et al., 1998). The enhancedvegetation index (EVI), which is an atmosphere-resistant version of the SAVI, alsoextends vegetation sensitivity into high-biomass conditions (Huete et al., 2001).

1 .3 .3 .2 SEASONAL AND INTERANNUAL PROF I L ES

Moderate-resolution sensors with spatial resolutions ranging from 250 m to 1 kmhave the capability of acquiring consistent and cloud-free seasonal data sets over a


landscape at weekly to biweekly temporal resolutions. Seasonal data sets are verydifficult to acquire with finer-spatial-resolution sensors that typically have 16-dayrepeat cycles and are plagued with cloud contamination problems. Moderate-resolution sensors have one- to four-day viewing capabilities, permitting the com-positing of multiday images into cloud-free image products. Soil biogeochemical andhydrologic models often require high-frequency data sets depicting seasonal patternsof greening and drying of vegetation useful in soil erosion models, land degradationstudies, fire detection, and albedo studies. Moderate-resolution satellite systems use-ful in multitemporal land process studies include the AVHRR, SeaWiFS, MODIS,SPOT-4 Vegetation, and the Multispectral Medium Resolution Scanner (MMRS) on-board SACC-C (Table 1.2).

1 .3 .3 .3 GEOBOTANY

Hyperspectral remote sensing is a powerful tool for inferring specific soil typesunderlying vegetated areas through foliar measurements of the biogeochemistry ofthe overlying canopy (Card et al., 1988; Peterson et al., 1988; Ustin et al., 1999).Cwick et al. (1998) mapped the forest soils in north-central Manitoba through hy-perspectral reflectance measurements of black spruce (Picea mariana) needles. Theymapped the soils by utilizing the concentrations of potassium in the needles, whichreflected the potassium distributions in the soil rooting zone with primary variationsassociated with either poorly or well-drained soils. Smith and Curran (1995) wereable to estimate and predict various foliar biochemical amounts (macronutrients andchlorophyll) of a slash pine canopy with AVIRIS imagery.

Okin et al. (2001) noted the difficulties in detecting subtle foliar chemistry par-ameters with hyperspectral data in sparse-cover arid and semiarid areas, where therange of spectral variability and uncertainties are high. Asner et al. (2000) reportedthat soil reflectance variation has a greater impact on landscape reflectance than doleaf optical properties in semiarid ecosystems. Franklin et al. (1993) attributed thehigh spatial variability of the exposed soil surface as a major factor inhibiting thecharacterization of arid and semiarid landscapes with remote sensing. For thesereasons it is very difficult to infer soil properties from vegetation measurements.

1.3.4 Optical–Geometric Variations

Optical–geometric variations of the soil surface yield important geomorphologicalinformation relevant to soil characterization and mapping that is not obtainedthrough spectral means. Prior to digital remote sensing imagery, soil mapping wasconducted using textural and stereoscopic analyses of high-resolution aerial photog-raphy. Raina et al. (1993) were able to incorporate image textural features andsurface terrain characteristics in mapping erosion and salinity classes from TM im-agery in India. The launch in 1986 of the French CNES satellite, Systeme Probatoirepour l’Observation de la Terre (SPOT), with a high-resolution visible (HRVIR) sen-sor greatly improved surface terrain characterization by providing both fine spatialresolution (10-m panchromatic band and 20-m multispectral bands) and stereo ca-pabilities with a pointable (�27�) sensor (Table 1.1). The SPOT sensor systemgreatly improves the mapping of geomorphic features, such as drainage patterns,


floodplains, terraces, relief, soil erosion potential, landscape stability, surface rough-ness, and other structural features. Gastellu-Etchegorry et al. (1990) mapped thesoils in central Java with SPOT data and found the high geometrical fidelity valuablein analysis of textural and tonal differences among soils.

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)onboard the Terra satellite also offers fine-spatial-resolution stereoscopic imagery.The instrument has three bands in the visible and near-infrared at 15-m pixel res-olution. In addition, there are six bands in the spectral range 1000 to 2500 nm with30-m pixel resolution, and five bands in the thermal infrared with 90-m resolution.A backward-viewing telescope provides same-orbit stereo data in a single near-infrared channel at 15-m resolution (Table 1.1).

As we proceed further to finer spatial resolutions, one can discern gullies andravines and finer-scale drainage patterns. The Space Imaging Ikonos and Digital-Globe QuickBird instruments were launched in September 1999 and October 2001,respectively, as commercial satellites. Ikonos is pointable (�26�) and can image agiven area of land at 1-m (panchromatic) and 4-m (multispectral) resolutions.QuickBird images Earth at 61-cm resolution with a panchromatic band and 2.44-mresolution in four spectral bands (blue, green, red, and NIR). At such fine hyper-spatial resolutions, Ikonos and QuickBird data offer the opportunity to provide amore complete representation of the landscape and its dynamic drainage patternsand surface processes (Figure 1.9).

BRDF data sets can now be acquired routinely with air- and spaceborne sensors.The Advanced Solid-State Array Spectroradiometer (ASAS; Irons et al., 1991) is anairborne instrument that acquires nadir and off-nadir digital image data in 62 spec-tral bands in the visible and near-infrared at 10-nm spectral resolution. Using push-broom scanning, ASAS is able to acquire images at off-nadir look angles up to 70�fore and 55� aft along track. The Multi-angle Imaging Spectro-Radiometer (MISR)is a moderate-resolution sensor onboard the Terra platform that routinely providescontinuous multiangle coverage of Earth on a nine-day repeat cycle (Diner et al.,1991) (Table 1.2). MISR images the same point on the ground with nine separatepushbroom CCD cameras to image at nine along-track angles (0, �26.1�, �45.6�,�60�, �70.5�) in four narrow spectral bands. MISR is an excellent instrument formoderate-scale BRDF studies and for decoupling atmospheric effects from the sur-face. The integration of the along-track angular observations from MISR with thewide across-track observations of MODIS has further advanced BRDF studies forthe dynamic and seasonal characterization of land surface structure.

Profiling and scanning airborne altimeters provide rapid and highly accurate mi-cro and macro roughness information directly at the landscape level (Menenti andRitchie, 1994; Ritchie et al., 2001). Airborne scanning lasers are capable of provid-ing three-dimensional representations of landscape features with detailed and ac-curate morphological characteristics useful in mapping of erosion features,vegetation cover and height, topography, and aerodynamic surface roughness (Rit-chie et al., 2001). Laser altimeters have been shown to measure roughness at dif-ferent scales, from micro roughness effects along a floodplain to macro topographicfeatures. Large landscape features such as gullies, channels, and floodplain roughnesscan be measured and utilized to estimate their effects on overland flow as well asresistance to flow. Figure 1.10 is an example of scanning laser data collected overa mesquite dune site in Jornada Experimental Range in southern New Mexico.


Figure 1.9 Fine-resolution color composite Ikonos image (red, NIR, green at 4-m resolution) of sand dune ‘‘medanal’’vegetation community at the Nacunan Reserve, Mendoza, Argentina (June 2001). See CD-ROM for color image.

Radar provides useful information in characterizing surface roughness. In themicrowave region, the roughness of a surface is a function of the wavelength,incidence angle, and polarization of the radar sensor. A surface is consideredrough if

�h (1.4)

8 cos �

where h is the mean variation in height of the surface, � the wavelength, and � theradar incidence angle (Dobson et al., 1995). Metternicht (1998) and Sano et al.(1998) studied the relationships found between radar data such as JERS-1 and sur-face roughness.

1 . 4 M O N I T O R I N G S O I L P R O C E S S E S

With the advent of repetitive global measurements from satellites, mapping caninclude the dynamic aspects of soils. Soil surface processes occur at different scalesand render soil properties extremely variable and difficult to measure. Short-termprocesses may be daily or seasonal and include thermal changes, wetting–drying

1.4 MONITORING SOIL PROCESSES 25

(a)

Figure 1.10 (a) Photo and (b) laser profile image of mesquite dunes at the Jornada Experimental Range, New Mexico.See CD-ROM for color image. (After Ritchie et al., 2001.)

cycles, and many biological processes. Mesoterm processes are on the order of 10to 100 years and include accelerated soil erosion and deposition, compaction, sali-nization, oxidation–reduction (soil color), organic matter turnover, and biogeo-chemical cycling processes. Long-term processes involve erosion, secondary mineralformation, and both physical and chemical rock weathering and are on the orderof thousands to millions of years. Soils act as an archive of such long-term processes,associated with past environmental change, and one can infer past climate of landsurfaces from current soil properties with the help of isotopes (Scholes et al., 1995).

Satellite data are most useful in monitoring short- and mesoterm soil processesat different scales and as a function of land management schemes and land useactivities. Satellite data offer the opportunity to integrate highly spatially variablesoil properties at sufficiently large spatial resolution and coverage for landscapeprocess generalizations to be made (Palacios-Orueta et al., 1999). Satellite measure-ments are particularly value over sensitive transition regions where rapid changes insurface processes occur in response to climate change and land use activities. In thefollowing sections we examine the integration of systematic and temporal satellitecoverage into various soil surface process studies involving land degradation, erosionmonitoring, productivity assessment, salinization, drought and soil water storage,carbon balance, and climate change.

1.4.1 Land Cover and Land Use Change

Land cover is subject to change through natural cycles (droughts, fires, succession,floods, volcanic activity) and anthropogenic activities, such as shifting cultivation,


Projection : UTM NAD27 Zone 13N Units : meters

contours at 0.25m intervalinterdune area (includes vegetation on interdune)dune (includes dune covered and not covered with mesquite)

(b)

Figure 1.10 (Continued ).

natural resource management, grazing, urbanization, and agriculture. The doublingof the human population over the past half-century has had profound consequences,in terms of destruction of the soil resource base, degradation of the environment,and effect on global systems (Spurgeon, 1993). Land use and land cover changesresult in simultaneous variations of many interrelated and coupled soil parametersyielding soils with characteristics different from those associated with the factors ofsoil formation (Yaalon and Yaron, 1966). Soils are in a dynamic equilibrium withtheir environment, and their properties are always changing, due to anthropogenicpertubation as well as climatic changes. The resulting transient soil properties rep-


resent the cumulative effect of all current and historical local and regional land useactivities.

There are two major forms of land cover changes: (1) conversion and (2) mod-ification (Turner et al., 1990). Land cover conversion is most pronounced and isdirectly observable with remote sensing, such as when a land cover class is changedinto another land cover class (e.g., deforestation, urbanization, agriculture). Landcover modification is more subtle and involves changes made within the same landcover class, as with acid rain and land degradation due to overgrazing, and fuelwoodoverharvesting. Land cover changes often accelerate soil erosion with a resulting lossof organic matter, fine soil particles, nutrients, and microbial populations in soils(Schimel et al., 1985; Harper and Marble, 1988). As much as 60% of the globalterrestrial surface demonstrates some degree of large-scale conversion, and an im-portant goal of satellite sensor systems is to determine the rate at which anthropo-genic land surface alteration is occurring within the global environment (Turner etal., 1995).

There is an abundance of studies in which remote sensing is utilized to detectand quantify land cover change. Adams et al. (1995) studied the spectral historiesfrom time-series TM images in the Amazon forest near Manaus subjected to pastureconversions and secondary forest growth. They applied spectral mixture analyses tomeasure quantitative changes in land cover. Changes in the fractional amountswithin an image and from image to image reflected biophysical processes and humaninfluences on land use. Tracing the spectral history of each pixel, along with theirspatial contents, provided a powerful method to monitor change and land surfaceprocesses, greater than was possible with field-based studies alone. Imhoff et al.(1997) merged nighttime ‘‘city lights’’ imagery from the Defense Meteorological Sat-ellite Program Operational Linescan System (DMSP OLS) with census data and aUnited Nations Food and Agriculture Organization (FAO) digital soils map to as-certain the extent of built-up land and its potential impact on soil resources in theUnited States. Their results showed a trend in development that followed soil re-sources, with the better agricultural soils being the most urbanized and some soiltypes almost eliminated by urban sprawl.

Currently, much attention is directed at the creation of comprehensive data setsto document current and past land use practices. NASA’s Land Cover and Land UseChange (LCLUC) program and the International Geosphere–Biosphere Programme(IGBP) are utilizing MSS data sets going back to the early 1970s to analyze land useand land cover change. The MODIS instrument land science team has developedland cover and land cover change products at 1-km spatial resolution which cate-gorizes land cover change processes on a global scale (Running et al., 1994). Wherechange is detected, further analyses can be conducted with higher-resolution satellitedata. Although remote sensing offers great potential in monitoring land cover, landuse, and land disturbances, there is little quantitative understanding of how thesechanges affect the soil resource.

1.4.2 Soil Biogeochemistry

There is great interest in analyzing the importance of soils as a source or sink ofprincipal greenhouse gases, such as carbon dioxide, methane, and nitrous oxide.


Biota620 Pg

Atmosphere760 Pg

Geologic5000 Pg

Ocean38000 Pg

Soil2500 Pg

62 Pg

? ?107 Pg

105 Pg

0.4 Pg

Deposition

Soil erosio

n ?Soil respira

tion ?

61.1 Pg

Figure 1.11 Principal global carbon pools and annual fluxes between them. See CD-ROM for color image. (After Lal,1998a.)

Soils can either contribute greenhouse gases to the atmosphere or remove gases,depending on local conditions of moisture, temperature, and land use. Soils alsoplay a role in the global ozone cycle through the release of nitric oxide and carbonmonoxide (Bouman, 1990). The global soil carbon pool is the third-largest poolafter the oceanic and geologic pools, and consists of soil organic carbon and soilinorganic carbon (carbonates) (Figure 1.11). The total amount of soil carbon isapproximately 2500 pg, which exceeds the amount of carbon in living vegetationby a factor of 4 and the amount present in the atmosphere by a factor of 3 (Post etal., 1982; Sundquist, 1993; Lal, 1998a). Even in the most arid regions, biogeniccrusts may inhabit the surface and contribute substantial quantities of organic matter(Johansen, 1993). In the context of global sustainability, it is essential to understandhow the source–sink function of soils can be managed and controlled for carbonsequestration and to mitigate the impact of climate change.

It has been a difficult challenge to develop remote sensing techniques to monitorthe spatial and temporal variations in soil organic carbon within terrestrial ecosys-tems. Some of the processes that influence carbon dynamics include decompositionand nutrient mineralization, soil heat and water flux, and vegetation dynamics. Therelevant driving variables include spatially distributed climatic data (temperature,precipitation, etc.); soil properties such as texture, topography, and land cover; andland use. Land conversions alter the biogeochemistry (root tissue distribution, leaflitter inputs, rates of organic matter turnover) of soils, and at the present time, landuse change is responsible for approximately one-third of the annual increase inglobal warming potential (Scholes et al., 1995; Jackson et al., 2000). Reliable esti-


(a)

(b)

Iron Content

Organic Matter Content

Valley Boundaries

Valley Boundaries

2 202 2Kilometers

N

0.0 - 2.02.0 - 2.52.5 - 3.03.0 - 3.53.5 - 4.04.0 - 5.0

Iron Content (%)

Organic Matter %0.0 - 2.02.0 - 2.52.5 - 3.03.0 - 4.04.0 - 4.55.0 - 6.0

Figure 1.12 Derivation of (a) soil organic matter and (b) iron contents in AVIRIS data using a sequential mixture modelingtechnique known as hierarchical foreground / background analysis (HFBA). See CD-ROM for color image. (After Palacios-Oruetaet al., 1999.)

mates of carbon reserves in undisturbed soils, however, are not available, and dataon rates of decomposition following disturbances are lacking (Lal, 1998a).

Hyperspectral remote sensing techniques appear promising for soil organic car-bon analysis at the landscape level. Huete and Escadafal (1991) presented a mixturemodel decomposition scheme that enabled extraction of the organic matter signalover a wide spectral variety of bare soils. In their spectral unmixing scheme, organic-based eigenspectrum were identified and loadings were found to correlate well withthe percent organic matter contents. Palacios-Orueta et al. (1999) used a sequentialspectral mixture analysis technique known as hierarchical foreground/backgroundanalysis (HFBA) that utilized laboratory-derived training vectors to extract soil or-ganic matter and iron sources of spectral variation (Smith et al., 1994). They con-ducted a two-step hierarchical process on hyperspectral AVIRIS data over twowatersheds in the Santa Monica Mountains in California in which discrimination ofsoil types occurred as the first step, followed by the mapping of the spatial variationin soil properties in the second step (Figure 1.12). Both soil organic matter and ironcontent spatial variations were delineated successfully in the partly vegetated AVIRISpixels.


>1.5%<1.5%

Soil OrganicCarbon Content

BrazilBrazil

Ground Measurement Sites

Figure 1.13 Soil carbon analysis in the Amazon using a neural net model that combines ground data with AVHRR satelliteimagery. See CD-ROM for color image.

The main limitations to the direct assessment of soil organic carbon are thattextural attributes of a soil modify the relationship between soil organic mattercontent and reflected energy and the optical properties of the humic substances varyunder different climates and land cover types (Baumgardner et al., 1970). Kimes etal. (1993) and Levine and Kimes (1998) have employed neural networks togetherwith GIS to relate complex soil properties with satellite imagery. Their approachwas tested on Mollisols and was found capable of estimating and tracing soil carbondynamics and other properties. They used their neural net approach further in theAmazon Basin by using a limited set of 200 soil sampling sites and land cover datato ‘‘train’’ their neural net model to recognize patterns in satellite imagery and derivesoil organic carbon content throughout the basin (Figure 1.13).

Integrative models for regional analysis of ecosystem properties are being utilizedto quantify existing soil carbon stocks and predict changes in soil carbon as a func-tion of changing land use patterns and climate change (Parton et al., 1987; Paustianet al., 1997). The evaluation of carbon dynamics under land use change is notstraightforward and requires a detailed history of land use activities in time andspace. The types of information required include (1) area affected by land coverchange, (2) soil organic carbon contents and soil bulk density values for differentsoils to at least a 1-m depth, and (3) fraction of soil organic carbon lost by changein land cover or use. Coupled with spatially explicit databases of vegetation, soils,topography, land use, and climate, simulation models are developed and integratedwith site-specific data. Spatial databases of land use and management practices arederived from remotely sensed data, whereas organic matter decomposition, waterbalance, and heat fluxes are handled through ecosystem simulation models. GIStechniques provide the means of organizing such information and merging data setswith remotely sensed imagery. Paustian et al. (1997) concluded that predicting soilcarbon response to the forces of climate change and land conversions requires betterpredictions of the impact of land use activities, more effective integration of remotesensing data in ecosystem models, and interfacing ecosystem models with globalclimate models (Figure 1.14).


-Land use data-Agricultural statistics-Land resource maps-Remote sensing

-land cover types-vegetation cover indices-radar imaging

Regional information

Process informationAgroecosystem

SOMmodel

Geographic information

Long-term experiments

Soil C dynamics:-responses to management-- soil C inventories-- sustainability assessment-- CO2 mitigation strategies

Prediction

ValidationParameterization

Experiments-Lab microcosms-Growth chambers-Field

Process models

-Decomposition-Nutrient cycling-Heat transfer-Water/solute flow-Crop growth

-North American SOM network-European SOM network -Australian SOM network -Global (SOMNET) network-Ecosystem site networks (e.g., LTER)

Climate data-weather station records-climate simulationsSoils/topography data-Soil maps (e.g., FAO, USDA)-Soil pedon databases -Digital elevation maps (DEM)

Res

earc

h di

rect

ion R

esearch direction

Prediction

Figure 1.14 Framework for regional analysis to predict soil carbon dynamics. (After Elliott and Cole, 1989; Paustian etal., 1997.)

Asner et al. (1999) combined a land surface biogeochemistry process model(TerraFlux) with remote sensing data and field data to monitor above- and below-ground carbon production and estimate and predict soil carbon accumulation overthe southwestern rangelands of U.S. The satellite data provided information on landcover, land cover change, the seasonal cycle of woody plant LAI, percent green coverfractions, the timing and magnitude of litterfall, and net primary production. Sea-sonal profiles of LAI are readily obtained with moderate-resolution sensors, andNPV and litter can be assessed with spectral mixture models. Quantifying the littercover is important in assessing the flow of nutrients, carbon, water, and energy interrestrial ecosystems as well as for evaluating soil erosion potential (Aase and Ta-naka, 1991; van Leeuwen and Huete, 1996; Nagler et al., 2000). Changes in litteramount and composition can alter the fluxes of carbon and nutrients within eco-systems and hence affect productivity and carbon sequestration.

1.4.3 Soil Moisture and Drought

Soil moisture is an important component of the global energy and water balanceand is needed as input into various hydrologic, meteorologic, plant growth, biogeo-


chemical, and atmosphere circulation models. Soil moisture influences the partition-ing of surface-available energy into sensible and latent heat fluxes, and precipitationinto evapotranspiration and surface runoff (Munro et al., 1998). Most important,soil moisture acts as an integrator of the occurrence, distribution, and amount ofprecipitation and is thus an important indicator of climate change. Despite the im-portance of the soil moisture variable, there has been limited success in implement-ing soil moisture observations at the appropriate time and space scales needed forcurrent hydrology, climate, and biogeochemical models (Sellers et al., 1995). Al-though there are strong limitations in the use of remote sensing techniques for soilmoisture assessment, remote sensing is being used increasingly for its spatial cov-erage and ability to integrate the spatial variability within an area, allowing for scale-dependent studies (Vinnikov et al., 1996).

Soil moisture mapping is very difficult, for several reasons. Soil moisture exhibitsextreme temporal and spatial variability across landscapes, with unknown scale de-pendencies. The distribution and amount of water stored in a soil varies not onlywith rainfall distribution but is dependent on landscape properties such as soil type(texture), topography, and vegetation. The spatial and temporal variability of soilmoisture is significant even at fine scales, which makes in situ soil measurements forcalibration of remote sensing systems particularly difficult. The Global Soil WetnessProject (GEWEX) Report (Sellers et al., 1995) concluded that the dense network ofpoints needed to characterize soil moisture on the ground is lacking and that soilmoisture cannot yet be considered a measurable variable at global or regional scales.

Another problem in soil moisture assessment is that near-surface soil moisturechanges more quickly than soil moisture at greater depths, which makes inferringgeneral soil wetness difficult from surface observations alone. Idso et al. (1974) usedreflectance measurements to demonstrate three spatial–temporal stages of drying:

• Stage 1: wet soil surface at potential evaporation• Stage 2: transition between wet and dry whereby subsurface capillary water is

unable to move to the surface fast enough to meet the evaporative demand ofthe atmosphere

• Stage 3: dry soil surface with low, nearly constant evaporation rate

Under conditions of high evaporative demand, a fairly wet soil may ‘‘dry’’ rapidlyat the surface, where optical measurements are made. Since the soil-drying processis spatially nonuniform, all three stages of drying may contribute to the integratedresponse of energy reflected and emitted from remotely sensed measurements.

1 .4 .3 .1 OPT I CAL APPROACHES AND L IM I TAT IONS

In the shortwave portion of the spectrum, the SWIR region is considered mostsensitive to surface moisture content, and TM bands 5 (1.55 to 1.75 �m) and 7(2.08 to 2.35 �m) offer potential soil water indicators. Water absorption in thesetwo bands can be expressed as a ratio relative to the other bands, or in linearcombination (Crist and Cicone, 1984; Levitt et al., 1990), and then related to soilwater content for discrete soil textural classes (Musick and Pelletier, 1988). How-ever, Huete and Warrick (1990) reported difficulty in assessing soil water content


at the surface (0 to 5 cm) with TM moisture bands or with various wetness indicatorsunder partial vegetation canopies with low (10 to 20%) vegetative covers.

An indirect approach to measuring the water status of soil is to measure thethermal infrared energy emitted by the overlying vegetation canopy. Plant stressindexes such as the surface moisture index (SMI; Nemani et al., 1993) and the waterdeficit index (WDI; Moran et al., 1994) utilize thermal and vegetation index mea-surements of soil and vegetated surfaces to estimate soil moisture status through therate of evaporative water loss. When soil moisture is abundant, plants are able totranspire water at maximal rates for the given meteorological conditions. As the soildries out, it becomes more difficult for plants to extract the necessary water to meetatmosphere evaporative demands and transpiration is reduced, resulting in higherleaf temperatures than for plants with an ample water supply.

1 .4 .3 .2 DROUGHT INDEXES

Combining land surface temperatures (Ts) with vegetation indexes (VIs) is of greatinterest in drought monitoring (Nemani and Running, 1989). Drought is defined asan extended period of deficient rainfall relative to the statistical multiyear averagefor a region. There are various measures used to assess the occurrence and extentof drought based on meteorological, hydrological, or soil moisture criteria. Mete-orological drought is usually based on long-term precipitation departures from nor-mal. An example is the weekly produced Palmer drought index, which has beenused by land and water managers in evaluating moisture stress across landscapes(Palmer, 1986). Drought may also be defined in terms of insufficient soil moistureto meet the needs of the overlying vegetation at particular times during the growthcycle of the vegetation. A deficit of soil moisture during critical periods of thegrowth cycle can result in significant reductions in forage yields and net primaryproduction.

Nemani et al. (1993) have operationally employed a remotely sensed droughtindex known as the surface moisture index (SMI), based on the slope of theAVHRR–NDVI and maximum temperature values. The SMI utilizes the VI–Ts ap-proach to monitor evapotranspiration and soil drying and assess the water stresscondition of the surface for a given level of vegetation cover. Carlson et al. (1995a,b)and Gillies et al. (1997) later applied inversion techniques to estimate available soilmoisture from VI–Ts scatterplots. More recently, Nishida et al. (2002) further mod-ified the SMI and developed a stand-alone moderate-resolution satellite-based VI–Ts approach to estimate the relative evaporation (RE) fractions from both bare soiland vegetated surfaces of mixed pixels (Figure 1.15).

Moran et al. (1994) developed the water deficit index (WDI), utilizing surfaceminus air temperatures and a spectral vegetation index to estimate the relative soilmoisture status. The WDI takes into account surface temperature variations resultingfrom plant water stress in partial canopies. The patterns of surface minus air tem-perature differences as a function of vegetation amount and water status form atrapezoid (Figure 1.16). The line on the left-hand side formed by points 1 and 3represents ample soil moisture conditions, while the line on the right-hand side frompoints 2 and 4 represents the ‘‘dry’’ edge or stressed conditions. Vegetation startsto experience stress when the WDI values fall to the right of a line formed betweenpoints 1 and 4 (Clarke, 1997). The lower and upper lines represent the cases of

34

Figur

e1.

15Re

lative

surfa

ceev

apor

ation

from

soil

and

vege

tatio

nfra

ction

sby

comb

ining

vege

tatio

nind

ex(V

I)an

dsu

rface

tempe

ratu

res

(T s).Th

isMO

DIS

prot

otyp

epr

oduc

tuse

sNOA

AAV

HRR

data

ofth

eco

nter

mino

usUn

ited

States

.See

CD-R

OMfo

rco

lorim

age.

(Cou

rtesy

ofth

eRe

mote

Sens

ingLa

b/NT

SG,S

choo

lof

Fore

stry,

Unive

rsity

ofMo

ntan

a.)


A C B

43

21

Nontranspiringfull-cover crop

Well-wateredfull cover

Dry bare soilWet bare soil

IncreasingSurface minus Air Temperature

0

100

Per

cent

Cov

er(a

ppro

xim

ated

by

a ve

geta

tion

inde

x)

Figure 1.16 Water deficit index (WDI) trapezoid combining remotely sensed vegetation index and thermal data. See CD-ROM for color image.

bare soil and full vegetation cover conditions, respectively, under a complete rangeof soil moisture conditions. Seiler et al. (1998) used an AVHRR-based vegetationcondition index (VCI) simultaneous with a temperature condition index (TCI) fordrought detection in Argentina. They were able to assess the spatial characteristicsof drought along with duration and severity.

Improved remote sensing from satellites, as well as the use of thousands of dailyin situ precipitation measurements, has dramatically improved drought-monitoringcapabilities. Palmer (1986) reported a strong relationship between global patternsof sea surface temperature (SST) anomalies and drought frequency in the Sahelregion. Los et al. (2001) used satellite data of SST and NDVI to demonstrate directrelationships between sea surface temperature variations in the Atlantic and Pacificoceans and large-scale atmospheric circulation patterns that bring moisture ordrought conditions. One of the most exciting developments in mitigating droughtimpacts may be advances made in forecasting the conditions that result in drought.The NOAA Climate Prediction Center uses sophisticated computer models that linkground and ocean conditions to the overlying atmosphere to create forecasts oftemperature, precipitation, and soil moisture months ahead of time.

1 .4 .3 .3 MICROWAVE APPROACHES

Currently, global near-real-time soil moisture indexes are now compiled using mi-crowave remote sensing information, although such indexes can give only a roughindication of soil wetness and are best suited to identifying areas of extreme moistureconditions. Microwave remote sensing techniques are most effective in deriving soilmoisture of the upper surface over sparsely vegetated areas and become less reliablein measuring subsurface moisture (Choudhury, 1992; Jackson, 1993). There areboth passive and active microwave approaches that appear promising for measuring


soil moisture (Engman, 1995). Numerous aircraft and field experiments have dem-onstrated that a 1.4-GHz radiometer is sensitive to the moisture content of thesurface soil layer for a wide range of vegetation conditions. Schmugge et al. (1992)employed passive microwave remote sensing methods successfully to measure soilmoisture over vegetated sites during the HAPEX, FIFE, and Monsoon ‘90 experi-ments. They used an imaging microwave radiometer at these sites at a frequency of1.42 GHz and were able to observe drydown of the soil following heavy rains andto map its spatial variation.

Vegetation strongly influences the backscattered signal and brightness tempera-tures, and much work is being conducted to address the influences of roughness,vegetation, and topography in order to better relate the microwave signal to thesurface soil moisture content (Engman and Chauhan, 1995; Sano et al., 1998). Veg-etation not only attenuates the microwave emission from the soil but also adds itsown emission, and thus vegetation models as well as vegetation indexes have beenused to correct for vegetation emissions. With radar, one must also determine soilroughness effects as well as the effect of the vegetation canopy. Change detectionmethods involving scene-to-scene differences have been found useful in subtractingor minimizing the effects of surface roughness and vegetation, since these tend tochange much more slowly than does soil moisture (Engman and Chauhan, 1995;Sano et al., 1998).

The Advanced Scanning Microwave Radiometer for EOS (AMSR-E) was launchedonboard the Aqua satellite on May 4, 2002 and is a cooperative effort betweenNASA and the Japan Aerospace Exploration Agency of Japan. This is a passive,forward-looking scanning microwave radiometer with 12 channels at six discretefrequencies in the range 6.9 to 89 Ghz that provide soil moisture and vegetationwater content products of high value for hydroclimatological applications. In ad-dition to land surface moisture content, AMSR-E data can measure cloud properties,precipitation rate, snow cover, and sea surface temperature. The Aqua satellite is insun-synchronous orbit with a local-time equator crossing of 1:30 P.M. and providesspatial and temporal soil moisture observations generated at a nominal 25-km equal-area Earth grid. The lowest frequency of the AMSR and AMSR-E is 6.9 GHz, lim-iting soil moisture retrievals primarily to regions of low vegetation biomass.

Future global soil moisture monitoring systems will consist of some combinationof in situ model estimates and derived estimates from satellite remote sensing. Thiswould involve the use of reference sites over the major soil types and satellite dataand land surface models to extend the in situ point measurements to larger areas.Land surface models can provide the large-scale coverage that is impractical forground-based measurements, and can provide a complete profile of information thatremote sensing cannot detect. Land surface parameterization (LSP) schemes are be-ing used to generate large-scale data sets of soil moisture and related surface fluxesof water and energy. These computational models have evolved from simple mass-balance schemes to sophisticated models that account for nonlinear interplay be-tween processes such as soil compaction and aeration, plant moisture stress, nutrientavailability, photosynthetic chemistry, and competition between plant genera (Sellerset al., 1995). Given the critical role of soil moisture on surface moisture and energybalance and covariability with subsequent influences on the atmosphere, it is im-portant to create the infrastructure to estimate and observe this quantity routinelyand operationally (Entekabi et al., 1996).


1.4.4 Soil Degradation Processes

Soil degradation is a major environmental concern that affects critical environmentalissues such as food security, reduced productivity, preservation of natural resources,loss of biodiversity, and global climate change. Land degradation creates the con-ditions for accelerated soil erosion and diminished quality of freshwater resources,and can eventually lead to desertification, a major socioecological problem world-wide. Large-scale land surface changes have produced documented changes in re-gional climate (e.g., land degradation in the Sahelian zone of Africa over the lastfew decades has resulted in decreased rainfall) (Nicholson et al., 1998). Tucker andNicholson (1999) used AVHRR–NDVI values to observe variations in Saharan de-sert contraction and expansion in response to climate variations. The InternationalConvention to Combat Desertification (CCD) defines desertification as ‘‘land deg-radation in arid, semi-arid, and dry sub-humid areas resulting from various factors,including climate variations and human activity.’’ General information and data re-garding the degree and extent of soil degradation and the resulting impact remainpoorly understood. There is an urgent need to develop a comprehensive set ofguidelines and standards to assess soil degradation by different processes. Remotesensing can play a major role by providing a quantifiable and replicable techniquefor monitoring and assessing the extent and severity of soil degradation.

Soil degradation can be viewed in terms of its adverse effects on important soilfunctions such as sustaining biomass production and biodiversity, and maintenanceof water and air quality by filtering, buffering, detoxification, and regulating geo-chemical cycles (Lal, 1998b). The main soil degradative processes, with severe ad-verse on- and off-site impacts, can be divided into physical, chemical, and biologicalprocesses and includes (1) soil erosion, (2) soil compaction, (3) nutrient depletion,(4) acidification, (5) reduction in soil organic matter, and (6) salinization (Lal,1998b) (Figure 1.17). The severity of degradation is normally based on the loss ofpotential productivity, which can be classified as (1) slight degradation with little orno degradation; (2) moderate; (3) severe; and (4) very severe, which is consideredas economically irreversible degradation (Lal, 1994).

1 .4 .4 .1 IND ICATORS OF DEGRADAT ION

Space- and airborne remote sensing observations have been used successfully as astarting point in the monitoring and control of degradation and desertification (Ro-binove et al., 1981; Mishra et al., 1994; Tripathy et al., 1996). There are manyindicators and early warning signals of soil degradation and desertification, whichlend themselves to remote sensing–based monitoring. These include (1) loss of veg-etative cover, (2) increases in albedo, (3) wind and water erosion, (4) soil saliniza-tion, (5) soil structure deterioration and crusting, (6) less soil moisture, (7) changesin iron oxide contents, (8) reduced soil organic matter levels, (9) land cover typeand species changes, (10) increased spatial variability of soil properties, (11) sandactivity and movement, and (12) increases in rock surface area.

The spatial variance and heterogeneity of an area have been utilized as sensitiveindicators of landscape instability, with increases in variance indicative of soil deg-radation and erosion. Pickup and Nelson (1984) showed that changes in the varianceof pixel subareas in Australia were strongly related to soil degradation. Land deg-


Soil Degradation

Physicaldegradation

• decline in soil structure• compaction/crusting• erosion by water and wind

• leaching• acidification• salt accumulation• elemental toxicity• nutrient depletion

• decline in biomass productivity• reduction in amount of biomass returned to the soil• disruptions in cycles of H2O, C, N, P, S• emissions of greenhouse gases to the atmosphere (CO2, CH4, N2O)

•• of soil organic matter

Biologicaldegradation

Chemicaldegradation

decline in soil biodiversity reduction in quality and quantity

Figure 1.17 Major soil degradation processes. See CD-ROM for color image. (After from Lal, 1998b.)

radation resulted in the loss of vegetative cover and increased spatial variation ofsoil spectral properties, owing to the loss of topsoil and exposure of subsoil layers.In a study on the Jornada Experimental Range in New Mexico in which long-termgrazing has promoted desertification, Schlesinger et al. (1990) found an increase inthe spatial and temporal heterogeneity of water, nutrients, and other soil resources.This has favored the invasion of desert shrubs, which in turn have further localizedsoil resources under the shrub canopies. In the barren areas between shrubs, nutri-ents are washed away and gases lost through erosion, thus creating a positive feed-back leading to desertification.

Ghosh and Tripathy (1994) investigated desertification processes in the arid andsemiarid regions of the Gulbarga district in India using IRS-1A imagery and MSSimages (1984–1991) to monitor desertification (Table 1.1). They analyzed multi-temporal albedo and NDVI and generated albedo-change images, which helped themlocate the sites of desertification. The albedo correlated quite well with factors suchas potential soil erosion and reduced soil moisture conditions. They used NDVI-derived estimates of vegetation growth as their biologic indicators. Aguiar et al.(1988) produced maps of desertification in the Patagonia region of Argentina withAVHRR and MSS. Their methodology included recording data on degradation ofvegetative cover and of soil water erosion, wind erosion, soil crusting and compac-tion, and salinization/alkalinization (Soriano, 1983). They recommended mainte-nance of landscape diversity when managing natural resources, along withrestoration of degraded areas and exclusion of grazing from the areas most affected.


In studying the abandoned agricultural lands in the Manix Basin of the MohaveDesert, Okin et al. (2001) showed how adjacent, downwind shrubland areas havebeen affected by sand blown from abandoned fields. This has resulted in decreasedshrub density, changes in soil texture, and increased soil albedo, which they mappedeffectively using hyperspectral AVIRIS imagery (see Figure 1.8). As they point out,in some cases the abandoned fields that are most disturbed have more vegetationcover than the undisturbed surroundings.

1 .4 .4 .2 ALBEDO

Satellite data are increasingly utilized to monitor the albedo of arid and semiaridlands, given the importance of albedo as an indicator of land degradation and as aphysical parameter with possible impacts on climate. Otterman (1977) and Robinoveet al. (1981) noted sharp increases in albedo in MSS imagery due to anthropogeniceffects with brightening denoting land degradation. Land degradation and denuda-tion in arid regions cause increases in albedo, with resulting impacts on both soiland lower-atmosphere circulation patterns (Figure 1.18). Baumgardner et al. (1985)found deserts and bare sands to have albedo values of 0.30, compared with 0.10 to0.15 for forests and 0.20 to 0.25 for grasslands and savannas. Hummel and Reck(1979) showed that deserts vary from 0.20 to 0.40 and presented seasonal albedovariations, which change due to snow and moisture conditions of the surface. Soilcolor, moisture, structure, and roughness all affect soil albedo, and structureless soilsmay increase soil albedo by 15 to 20% (Cierniewski, 1987; Potter et al., 1987; Postet al., 2000).

Remote sensing data have been used to estimate albedo over extensive areas (Brestand Goward, 1987). Gutman (1988) presented a methodology for deriving meanmonthly albedo from AVHRR data. However, most satellite sensor systems haveonly been able to approximate the surface albedo, due to limited nadir views andpartial spectral sampling. Jackson et al. (1985) and Liang et al. (1999) have derivedempirical relationships using narrow spectral bands to determine albedo over het-erogeneous surfaces. Directly measured surface albedo products, at 1-km resolution,are available from both MODIS and MISR sensors onboard the Terra and Aquasatellites (Table 1.2). This provides the opportunity not only to monitor changes inthe radiative energy balance seasonally but also to assess the effects of land degra-dation and denudation on the climate system. Both a black-sky albedo, representinga directional–hemispheric albedo dependent on the solar zenith angle, and a white-sky albedo, or bihemispheric reflectances, are available.

1 .4 .4 .3 SAL IN IZAT ION

Soil degradation related to salinization and alkalinization represents an increasingenvironmental hazard to extensive natural and agricultural ecosystems, and conse-quently, to the human environment (Scharpenseel et al., 1990). It involves the ac-cumulation of salts (chlorides, sulfates, carbonates) of sodium, magnesium, orcalcium in the root zone as salts move upward in the soil and are left at the surfaceas the water evaporates (Figure 1.19). Many complex problems are created by thecombination of salts, soils, and climatic conditions, and there are many soil prop-erties, such as pH, salt content, electrical conductivity, and exchangeable sodiumpercentage, that determine the salinity status of soils. Variations in the reflectance

40

(a)

(b)

(c)

Figure 1.18 Soil degradation near the Nacunan Reserve in Mendoza, Argentina, showing the higher albedos of the severelydegraded ‘‘peladal’’ areas in (a) aerial photo, (b) Ikonos panchromatic 1-m imagery, and (c) color composite (bands 3, 4,and 7) Landsat ETM� imagery. See CD-ROM for color image. (Photo courtesy of Greg Asner.)


Figure 1.19 True-color composite ASTER image of the Colorado River delta region, Mexico, showing salinity accumulationresulting from the controlled flow of the Colorado River by upstream dams (September 8, 2000). See CD-ROM for colorimage. (Courtesy of NASA / GSFC / MITI / ERSDAC / JAROS and U.S./Japan ASTER Science Team.)

spectra of soils thus cannot be attributed to a single soil property and there are noknown narrow absorption bands linked to salinity status (Szilagyi and Baumgardner,1991).

Numerous remote sensing studies have involved the mapping and monitoring ofsalt-affected soils with MSS and TM and SPOT data (Szilagyi and Baumgardner,1991; Dwivedi, 1992; Dwivedi and Rao, 1992). Rahman et al. (1994) used multi-spectral SPOT images converted to the brightness index (an albedo measure) to mapsalinity classes. Csillag et al. (1993) suggested that the potential exists for spectralrecognition of salinity status with hyperspectral imagery. They used a modified step-wise principal component analysis procedure and discriminant function analysis forselection of optimal bands for salinity mapping in the San Joaquin Valley, Californiaand Tiszantul region of eastern Hungary. Key spectral ranges were identified in thevisible (550 to 770 nm), near-infrared (900 to 1030 nm), and middle-infrared (2150


to 2310 nm, 2330 to 2400 nm) portions of the spectrum, from which they identifiedsix broad bands, greatly improving salinity discrimination. There is also interest indetection of salt-tolerant vegetation as indicators of saline/alkaline-affected areas.

The application of microwave remote sensing to soil degradation assessment hasbeen limited despite studies showing relationships between soil moisture, soil salin-ity, soil roughness, and crusting (Chanzy, 1993; King and Delpont, 1993). Metter-nicht (1998) used an L-band synthetic aperture radar (SAR) image from the JapaneseEarth Resource Satellite (JERS-1) to classify and map areas degraded by salinity–alkalinity processes in Bolivia. Metternicht 1998 used fuzzy classification logic todepict more realistically the ‘‘continuous’’ salinity classes that intergrade graduallyover salt-affected degraded areas. Four fuzzy sets—nonalkaline, alkaline, saline, andnon-saline—were used to characterize salt-affected soils in accordance with theranges established by the U.S. salinity laboratory staff (Richards, 1954):

1. Normal soil: electrical conductivity (EC) � 4 dS/m and the pH � 8.52. Alkaline soil: EC � 4 dS/m and pH 8.53. Saline soil: EC � 4 dS/m and pH � 8.54. Saline–alkali soil: EC � 4 dS/m and pH 8.5

Management of salts without degrading water quality downstream is a criticalissue, particularly in arid regions where high salt levels have already impaired theuse of significant quantities of irrigated land. Timely detection and diagnosis of theseprocesses have an especially significant role in the prevention and reclamation ofthe salt-affected areas (Csillag et al., 1993). Land use activities may or may not beconducive to enhancing or alleviating salt problems, and the interaction of land useactivities with salt-affected soil characteristics, particularly soil structure, adsorbedions, microbial activity, organic matter, and moisture movement in the root zone ofsoils, remains poorly understood.

1.4.5 Soil Erosion Processes

One of the earliest space observations made from the Space Shuttle was the ‘‘bleed-ing island’’ effect, whereby eroded red soil sediments from dissected landscapes indeforested portions of Madagascar were being transported by rivers into the Moz-ambique Channel and Indian Ocean (Wells, 1989). Woodland depletion due toharvesting of fuelwood around villages in semiarid Africa also has resulted in in-creased water- and wind-induced erosion, which combined with persistent droughtare responsible for the aggravated African dust transport known as Harmattan duststorms (Figure 1.20). The Loess Plateau of China, drained by the Yellow River, islosing soil at an alarming annual rate exceeding 100 Mg/ha (Fu, 1989; Dazhong,1993) (Figure 1.21). Generally, it is the most fertile topsoil that is eroded anddeposited into waterways, resulting in loss of arable land and the silting up of res-ervoirs. Increased sediment loads also alter environmental conditions of coastalzones and are considered to be one potential contributor of coral bleaching anddegradation (Holden and LeDrew, 1998). Soil erosion is one of the most importantprocesses contributing to land degradation over large areas of the terrestrial Earth.Our understanding and knowledge of the global rates of erosion, transport, and


Saharan Dust and Canary IslandsSeaWiFS (March 6, 1998)

Figure 1.20 True-color composite SeaWiFS image at 1-km resolution, showing the Saharan dust storms (March 6, 1998).See CD-ROM for color image. (Courtesy of SeaWiFS Project, NASA / Goddard Space Flight Center, and ORBIMAGE.)

sedimentation are very limited, yet are vital for continued development of effectivegeochemical cycling models.

Air- and space-borne multispectral data have been used extensively in conjunctionwith aerial photos and ground data for mapping and deriving information on erodedlands (Mathews et al., 1973; L’Vovich et al., 1990; Saha and Singh, 1991). Directspectral measures indicative of soil erosion include changes in organic matter con-tent, mineral composition, albedo, roughness, and soil structure (Mulders, 1987;Irons et al., 1989). Remote sensing also provides temporal and spatial informationthat can be coupled with soil erosion models, such as measures of the vegetationprotective cover, soil moisture, land use, digital elevation data, and sediment trans-port. At the finest spatial resolutions, remote sensing can provide detailed infor-mation on linear erosion features such as gullies and sand dune formations (Alamand Harris, 1987; Bocco et al., 1990). Remote sensing data thus have good potentialfor providing an objective and rapid surveying technique to aid in the assessmentof spatial variations in soil loss in ecosystems.

Soil erosion is commonly grouped into three phases: (1) physical detachment ofsoil particles, (2) transportation of soil material by wind and water, and (3) depo-


Figure 1.21 MODIS image showing sediment transport at the mouth of the Yellow River on February 28, 2000. Soilerosion from the Loess Plateau is proceeding at a very high rate. See CD-ROM for color image. (Courtesy of JacquesDescloitres, MODIS Land Rapid Response Team, NASA / GSFC.)

sition of soil material, including their accumulation as sand dunes. Both water andwind erosion are rate processes that depend on the speed of wind or overland flow,and erosion occurs when the efflux of sediment exceeds that which enters. Theerodibility of a site depends on several factors, including the inherent erodibility ofthe soil, the extent of protective ground cover, topography, climate, and land use.


1 .4 .5 .1 WIND EROS ION

There are three categories of wind-driven particle motion, which in order of de-creasing particle size result in creep, saltation, and dust. Although wind speed is thedriving factor for erosion, it is modified by soil moisture, soil texture, structure,stoniness, and vegetation cover and land use practices. Saltation generally involvessand particle sizes and can result in sand dune activity and formations. Dune for-mations can be associated with past climates, and space imagery can be used tostudy land–climate relationships (Forman et al., 1992). The reactivation of stabledunes is an important indicator of desertification, and there is much interest incharacterizing sand dunes for this purpose. Blumberg (1998) found synthetic aper-ture radar (SAR) data highly useful in mapping dunes, and types of dunes, due tothe ability to control illumination parameters and characterize surface roughness. Instudying desert dune fields in North America, Bolivia, Australia, and Namibia, hefound the longer wavelengths, P- and L-band at 0.68 and 24 cm, respectively, toprovide the best contrast for dune mapping, and cross-polarized channels werefound best in discriminating active from inactive dune forms.

Dust can rise to an indefinite height in the atmosphere and exert strong environ-mental impacts on Earth’s climate as well as having strong effects on ocean sedi-mentation, soil formation, groundwater quality, and the transport of airbornediseases. Most of this dust is produced in arid areas, the Saharan desert being theworld’s largest supplier of dust, releasing an estimated 25 to 50 million metric tonsof dust over the Atlantic each year (Goudie, 1978) (Figure 1.20). Grigoryev andKondratyev (1981) used satellite observations to map westward and northward duststorms generated from North Africa. The extent of dust production is related tovarious external factors, such as wind speed and turbidity, and surface propertiesthemselves, such as particle size, roughness, and mineralogical composition (Gilletteet al., 1980). The Saharan desert exhibits great variability in the composition andamount of dust produced, due to the high variability of the geologic and geomorphicbackgrounds (see Figure 1.4). Escadafal and Callot (1991) investigated the compo-sition of dust sources in the Sahara with simple band ratios from TM satellite im-agery. They were able to group the soils into five main surface types based on theirmineralogy and particle size potential for dust production. Husar et al. (2001) usedSeaWiFS imagery and an aerosol index from the Total Ozone Mapping Spectrom-eter (TOMS) sensor to analyze the formation and transport patterns of dust cloudsfrom the Gobi desert, also known as Asian dust. The Asian dust clouds affected airquality as far away as the United States and were observed to increase the albedoof the land and ocean surfaces by 10 to 20%.

1 .4 .5 .2 WATER EROS ION

Water erosion is caused by soil dislodged by raindrop impact and sediment transportby thin overland flow and is usually measured using small field plots approximately100 m2 in size. Overland flow or runoff begins when the rainfall rate exceeds infil-tration into a soil. The prediction of runoff requires knowledge of soil characteristicsrelated to infiltration and knowledge of time variations of precipitation. Soil mois-ture and water-holding capacity information are needed to help determine runoffand erosion rates. Quantitative estimation of soil losses due to water erosion has


been confined to the plot level or small subwatersheds using empirical models suchas the universal soil loss equation (USLE) and the revised USLE (RUSLE) (Renardet al.,1991):

A � R � K � L � S � C � P (1.5)

where A is the total soil loss, R the rainfall erosivity index, K the soil erodibilityfactor, L the slope length factor, S the slope gradient factor, C a soil protective coverfactor, and P the land use and management factor. Some of these factors are readilyobtainable with remote sensing data. Price (1993) demonstrated the usefulness ofTM spectral data in assigning reliable protective cover C factors to the USLE modelwithin the pinon–juniper woodlands of Utah. The slope length and slope gradientfactors could potentially be derived from stereoscopic SPOT or ASTER data. Theland use and management factors are more difficult to assess and are needed toestimate runoff coefficients. High-spatial-resolution sensors such as those of Ikonosand QuickBird can potentially provide this information.

The results of small-plot studies are difficult to extrapolate to the catchment andwatershed levels, due to the necessity of sampling temporal and spatial variability.Processes of soil erosion and sediment transport differ with scale. At the watershedscale of hundreds of hectares, suspended sediment yield in waterways is used toassess soil erosion, while denudation rates are normally calculated for the large riverbasin scales (thousands of square kilometers). Examples of sedimentation studieswith remote sensing are provided in Chapter 7.

1 .4 .5 .3 EXPOSED SUBSURFACE SO I L

Soil erosion is strikingly evident when subsoil characteristics are exposed and exertan influence on the surface soil reflectance properties. Many have investigated thegenetic relationships and spectral associations expected between surface and sub-surface soil properties with the goal of vertical extension of remotely sensed surfaceproperties. Such relationships enable the use of satellite imagery for detection oferoded soils following wind- or water-induced exposure of subsurface layers (Agbuet al., 1990). As erosion proceeds, more of the parent material mineralogies andspectral properties become evident, while the optical properties of the organic-richupper layers become less pronounced. The undisturbed well-developed soil and un-derlying parent material represent the two endpoints from which the various degreesof soil erosion and land degradation can be assessed (De Jong et al., 1999; Hill etal., 1995). These characteristics can be monitored with satellite imagery using spec-tral indices and mixture models.

Seubert et al. (1979) and Latz et al. (1984) mapped soil erosion severity classesin Alfisol topographic sequences with MSS imagery. They were able to detectchanges in iron content from the broad iron absorption band at 0.87 �m and theresulting changes in the slopes of soil spectral signatures with depth related to thedecrease in organic matter and increase in iron oxide contents. Frazier and Cheng(1989) relied similarly on differences in organic matter and iron contents for soilerosion mapping in Washington and utilized TM band ratios. Pickup and Nelson(1984) used MSS band ratios to map soil erosion and deposition in central Australia


and model changes in soil landscape as affected by soil erosion (Pickup and Chew-ings, 1988). Other studies have demonstrated how changes in surface soil color,brightness values, and NDVI were useful in soil erosion studies (Dubucq et al., 1991;Escadafal, 1993). Galvao et al. (1997) utilized principal components analysis andvarious radiometric indexes, such as a redness index, to distinguished hematite-richsoils from goethite-rich soils and to relate spectral reflectance data between surfaceand subsurface soil horizons in Oxisols.

1 .4 .5 .4 PROTECT IVE COVER FACTOR

Soil erosion is reduced significantly by the presence of a ground protective cover,which includes standing (aerial) vegetation, and closer to the ground, contact cover,consisting of litter and stones. Soil cover protects the soil from water erosion byintercepting raindrops, thus dissipating their kinetic energy before they reach thesoil. Contact cover is much more effective in impeding overland flow, while aerialcover is effective in providing protection against wind erosion as well as rainfallimpact. In general, any land surface change that leads to more exposed soil withless cover will contribute to increased soil erosion and the removal of valuabletopsoil. The vegetative and litter protective cover further vary over the growingseason, and the degree of erosion will depend on their amounts during periods ofmaximum rainfall and eolian activity.

Satellite data offer quick and repetitive estimates of vegetative cover. De Jong etal. (1999) generated regional maps of erosion utilizing a computed vegetation coverinterception factor, P, by inverting the exponential relationship between interceptionand spectral vegetation index (VI):

VI � a[1 � exp(�bP)] � c (1.6)

where a, b, and c are area-specific model coefficients. The resulting erosion mapswere found more useful than the simple extrapolations from smaller erosion plotstudies. However, spectral vegetation indexes may misrepresent the vegetation coverfactor because they are responsive only to the amount of green vegetation over asoil surface, not to the senescent or woody vegetation cover (NPV), which providea soil protective cover equal to that of green vegetation (De Jong, 1994). Spectralmixture models that separate remote sensing data into green vegetation, soil, NPV,and shadow are better suited for this purpose (Smith et al., 1990; Roberts et al.,1993). Adams et al. (1995), Drake et al. (1999), and Asner and Lobell (2000) haveshown that it is possible to map both green and NPV cover, as well as exposed soil,using mixture modeling (see Figures 1.6 and 1.7). The green vegetation map is usefulas inputs to models of evapotranspiration and water balance, and the total vegetation(green and NPV) cover map is useful in predicting overland flow and assessingraindrop impact and wind turbulence (Drake et al., 1995).

Similarly, gravels and large rocks are important in protecting soil from erosion,and no soil is available for detachment and transport over rock outcrop areas. Mix-ture model techniques may provide useful information in estimating the amount ofrock, gravel, and stone material on the surface through either spectral differencesbetween soil and rock or by use of the shade endmember, which can be related to


the roughness of the surface. Optical–geometric methods that incorporate BRDFrelationships, texture, and roughness may be useful in characterizing the amountand size of rock materials on a soil surface.

1 .4 .5 .5 SO I L CRUSTS

There are three general types of surface crusts: (1) soil structural crusts, (2) desertvarnish, and (3) biogenic crusts. The first is formed rapidly, whereas the other twotake long periods and special environmental conditions. Assessment of the structuralstatus of the soil is essential for monitoring soil degradation processes, as it controlsmany soil degradation parameters, such as reduced infiltration rates and increasedrunoff and soil erosion. In their studies on crusting, Ben-Dor et al. (1999), foundobservable spectral changes during structural crust generation. The optical propertiesof the crust were significantly different from the bulk soil, providing a means ofcrust detection.

The spectral signal of biogenic crusts is also distinct and can be remotely sensed(O’Neill, 1994; Karnieli and Sarafis, 1996) (Figure 1.22). Biogenic soil crusts consistof soil cyanobacteria, lichens, and mosses. They play an important ecological rolein arid and semiarid lands by providing stability to otherwise easily eroded soils,increasing water retention, and stimulating plant biomass through nitrogen fixation(Harper and Marble, 1988; Metting, 1991; Johansen, 1993). The cyanobacterialfilaments form an intricate webbing of fibers that bind soil particles together, makingthem resistant to both wind and water erosion. Unlike vascular plant cover, thesecrusts provide erosion protection year-round and over adverse conditions (e.g.,drought). In the Colorado Plateau region of the southwestern United States, biogeniccrusts are well developed and may represent over 70% of the living ground cover(Belnap and Gardner, 1993). Unfortunately, these crusts are being disturbed increas-ingly over vast areas in the western United States as a result of rising recreationaland commercial uses, resulting in significant increases in regional wind erosion rates(Belnap, 1995; Williams et al., 1995). Prolonged drought conditions also weakenbiogenic crusts. As most crustal biomass is concentrated in the top 3 mm of the soil,very little erosion can have profound consequences for ecosystem dynamics.

Biogenic soils have been mapped successfully with remote sensing informationutilizing imaging spectroscopy and mixture modeling. Using AVIRIS data, Kokalyet al. (1994) successfully discriminated crusted soils from mineral soils and vascularvegetation with spectral mixture modeling, based on reference laboratory spectra.Karnieli et al. (1999) found that biogenic soil crusts contribute significantly to thehuge contrast observed with satellite data across the Sinai–Negev deserts. The darkerNegev side is covered with biogenic crusts which upon wetting from rainfall, becomephotosynthetic and produce a high NDVI signal (Figure 1.22).

1 .4 .5 .6 TOPOGRAPHY

Topography is of fundamental importance to soil erosion studies. Pilesjo (1992)demonstrated the effective integration of remote sensing with GIS for soil erosionstudies in semiarid and arid ecosystems. Digital elevation models (DEMs) were usedto generate various morphometric characteristics of relief, including slope steepness,aspect, profile, and tangential curvatures, all useful in modeling overland flow andthe prediction of hydrologic behavior. Similarly, Connors et al. (1987) mapped


Figure 1.22 Biogenic surface crusts, their spectral signatures, and their high-contrast appearance between degraded andprotected areas at the Negev–Sinai border. See CD-ROM for color image. (Courtesy of Arnon Karnieli.)

potential soil erosion rates by classifying multispectral SPOT imagery with DEMinformation about slope gradients. Remote sensing can provide topographic infor-mation directly, such as stereo SPOT imagery, which has been used to develop aDEM with 10 m of horizontal resolution and 5 m of vertical resolution (Case, 1989).Bocco et al. (1990) demonstrated the capability of SPOT stereo panchromatic imagesto map the distribution of gullies and drainage patterns in Mexico. Remote sensingsystems with stereoscopic capabilities, such as SPOT and ASTER, can meet thestrong need to develop global DEM data sets of uniform quality and with improvedresolutions. The new GTOPO30 DEM database, as well as improved global topo-graphic data bases currently under construction from space-based observations, willalso be of considerable assistance in soil erosion models and mapping.


1 .4 .5 .7 EROS ION MODELS

Models to predict soil erosion require spatial and temporal information of theprocess-controlling variables, some of which are readily provided by remote sensingimagery. Examples of erosion models are the Water Erosion Prediction Program(WEPP) (Nearing et al., 1989), KINEROS (Woolhiser et al., 1990), and EUROSEM(Morgan et al., 1998). As these models are becoming increasingly distributed, re-mote sensing is being explored as a valuable source of spatial and temporal infor-mation, such as soil moisture balance and infiltration, vegetation growth and decay,vegetation amount and physiognomy, and land use. These models predict runofffrom rainfall inputs provided by a climate generator. In general, model inputs canbe categorized into a climate file (wind, rainfall amount, rainfall duration), a slopefile (slope geometry), a soil file (texture, erodibility, hydraulic properties), a vege-tation file (cover, height, geometry), and a land use file (plant-dependent parame-ters).

De Jong et al. (1999) developed the Soil Erosion Model for MediterraneanRegions (SEMMED) to produce regional maps of soil erosion. The model integratesmultitemporal TM images to account for dynamic vegetation properties, a DEM toaccount for topographic variables, GIS layers depicting the spatial distribution ofsoil properties, and limited amounts of soil physical field data. For each cell, soilmoisture storage and infiltration capacities are determined through the soil maps,overland flow and drainage directions are assessed through the DEM, and vegetationcover and interception factors are derived from the satellite imagery. Wind erosionmodels include the Wind Erosion Production System (WEPS; Hagan, 1991) and theCSIRO/CaLM Model of Wind Erosion (Shao et al., 1996). These models incor-porate spatial variation in soil properties, surface roughness, surface cover, and top-ographic features. The CaLM model has been linked to a GIS and used to assessand predict patterns of wind erosion in Australia.

Soil erosion processes are extremely scale dependent, and there is a need todevelop methodologies for extrapolating plot-scale experiments to the landscape,watershed, and river basin scales. Similarly, data obtained from coarser-scale riverbasins need to be interpolated to the field scale. Remote sensing and GIS offer anopportunity for the development of dynamic distributed modeling and simulationtechniques to study and compare soil erosion across heterogeneous scales. GIS alsoprovides a method to incorporate land use and socioeconomic factors, such as dem-ographic pressures, into erosion assessments (Lal, 1994). As noted in Dazhong’s(1993) study, the alarming increase in soil loss over time across the Loess Plateauin China followed the increase in population of the region.

1 . 5 S O I L R E S O U R C E D ATA B A S E S

As seen in this chapter, spatially referenced soils data are needed to address a widerange of global environmental and global change issues (Figure 1.2). The availabilityof globally consistent soil databases is particularly urgent given the Kyoto Protocolon Climate Change, which requires up-to-date, reliable, and accessible informationon land conditions. Such information is also needed to understand and predict soilresponses to climate change and land use management and practices. Spatially ref-

1.6 CONCLUSIONS 51

erenced soils data also provide initialization values for many process-based models,which require values for soil texture, depth, nutrient, and water-holding capacity.

There are several limitations in currently available soil survey data. Soil data setsare usually country-specific, and there are no standardized criteria concerning theanalysis of soils or in determining the parameters needed to characterize soils. Re-mote sensing offers the opportunity to extrapolate existing soil databases to inac-cessible or less accessible areas, and to aggregate and disaggregate soil data across arange of scales, as required by GCM, hydrology, and ecosystem models.

At the international level, there are two major classification systems, the UnitedNations Food and Agriculture Organization (FAO) ‘‘Legend’’ of the Soil Map of theWorld (FAO, 1974) and the U.S. Department of Agriculture Soil Taxonomy (SoilSurvey Staff, 1975). These surveys contain soil descriptions, laboratory analyses, soilclassification maps, and interpretations on land use and management under prevail-ing climates and land capability interpretations. The FAO developed a soil map ofthe world at a scale of 1 : 5,000,000, but this map is fairly qualitative and becomingout of date because of recent soil surveys and new techniques for measurement. TheNational Aeronautics and Space Administration (NASA) developed a database of soilcharacteristics at a grid cell size of 1� by 1� with classes of soil texture, soil phase,and slope (Zobler, 1986). The World Soils and Terrain Digital Data Base (SOTER)project, initiated by the International Soil Reference and Information Center (ISRC),aims to georeference information on soil profile data and soil patterns at differentscales and spatial resolution (Batjes, 1990).

Much work remains, however, in the process of intercalibration between thesesoil data systems. A NASA EOS soils data set is under development as part of anIGBP-led project to compile and unify soils data sets with globally applicable pe-dotransfer functions which allow translation of a global pedon database into soilphysical and geochemical properties (Scholes et al., 1995). Pedotransfer functionsenable the derivation of secondary data products from the raw soil profile data: forexample, the translation of fundamental observed soil properties such as particlesize distribution, into inferred properties, such as water retention curve, or calcu-lated quantities, such as carbon density.

1 . 6 C O N C L U S I O N S

Many studies have shown the strong potential and feasibility of remote sensing inproviding high-quality spatially distributed data on a repetitive basis to monitor soilresources. Multispectral remote sensing, with its synoptic capability, is shown to bea useful tool to enhance interpretation of landscape patterns and estimate soil prop-erties. However, despite the demonstrated utility of remote sensing techniques forpedologic studies, satellite data are not used routinely for soil mapping in most ofthe world (Irons et al., 1989). This is partly due to several limitations inherent inremote sensing measurements, such as sensitivity to only the uppermost soil surfacelayer and extensive vegetation masking of the soil surface. There are also access,cost, and computational difficulties in the operational use of remote sensing datafor soil resource management. This is expected to change as environmental studiesbecome more integrative and access to satellite technology becomes user friendlyand less costly.


Remote sensing may only be sensitive to the immediate surface, but this is themost dynamic biologic and hydrologic interface, as it responds immediately to cli-matic changes and human forcings. Numerous sensor systems have recently beenlaunched and show great promise in providing much needed spatial and temporalmultiscale observations of the soil surface. For example, recently launched multian-gle sensors are expected to make major contributions to remote soil studies throughreliable morphometric measurements, including elevation measurements at the pixelscale and textural measurements at the subpixel scale. New capabilities are alsobecoming available for soil process studies enabling integration of environmentalprocess models, climate data, human interactions, and GIS with remote sensing data.As a result, much effort is being made to bridge the community gap between remotesensing and soil and water conservation communities in terms of the capabilities ofthe former and the needs of the latter. In view of the heterogeneity of soil landscapesand the need to infer soil properties at depth from surface-derived data, the chal-lenge to soil scientists is to determine the most effective data analysis proceduresfor effective use of remote sensing data.

To advance our understanding of the role of soils in the Earth system, remotesensing data must be better integrated into studies of soil and land surface processes.Large-scale monitoring is critical in assessing landscape processes involving ecolog-ical change and land degradation. There is an urgent need to characterize and quan-tify soil physical, chemical, and biologic responses to climate change, land use, andhuman disturbance. Remote sensing and GIS will play a major role in furtheringour understanding of the complex time and spatial scales that characterize environ-mental problems and in coupling ecosystem models with physical models and so-cioeconomic models to produce integrative Earth system science models with soilsas a key component (Schimel et al., 1991).

remote sensing of terrestrial ecosystems

Documents