1

Remote Sensing of Soil and Minerals

Soil is a mixture of inorganic mineral particles and organic matters of varying size and composition. Remote sensing can play a limited role in the identification, inventory, and mapping of soils, especially when surface soils are not covered with dense vegetation.

Remote sensing can provide information about the chemical composition of rocks and minerals that are on the Earth's surface, and not completely covered by vegetation.

Soil Horizons

Biological, chemical, and physical processes create vertical zonation within the upper 200 cm or so of soils in which there is comparatively free movement of gravity water and groundwater capillary moisture. This results in the creation of relatively horizontal layers, or soil horizons.

2

Soil Horizons

The standard horizons in a typical soil profile situated above the bedrock include O, A, E, B, C, R, and Wthat may be distinguishable from one another based on their color, texture, and chemical properties.

The Humus-rich Topsoil, or O Horizon –

Contains more than 20% partially decayed organic matter. It is amixture of inorganic soil particles and decaying organic matter.

O horizon soils typically have a dark brown or even black surfacelayer ranging in thickness from a few centimeters to several meters in areas where dense plant cover exists.

This horizon is created by the interaction of water, other chemicals, heat, organic materials, and air among the soil particles.

Plant root systems extract much of their water and nutrients from within this “zone of life”.

3

Soil Grain Size and Texture

The average diameter of grains of soil in a soil horizon is one of the major variables used to identify the taxonomy of a soil. There are three universally recognized soil grain size classes: sand, silt, and clay.• Sand: a soil particle between 0.05 and 2.0 mm in diameter• Silt: a soil particle between 0.002 and 0.05 mm in diameter• Clay: a soil particle < 0.002 mm in equivalent diameter

Soil Particle Size Scales

4

Remote Sensing of Soil Properties

• Most of the information used by soil scientists to map soil series is obtained by direct observation in the field.

• It is essential that subsurface soil profiles be examined and careful biological, chemical, and physical measurements be obtained within each soil horizon.

• It is not realistic to expect of using remote sensing to map thesoil taxa without “in situ” data collection.

• Soil scientists find that remotely sensed images of the terrain are essential to the soil mapping process.

• Some soil property characteristics may be measured remotely under ideal conditions.

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0 - 9604.29 (g/m2)9604.30 - 25782.8925782.30 - 75874.1475874.15 - 209662.70209662.71-454392.19

National Soil Survey Soil Organic Carbon Calculations(Total soil organic carbon content value in unit of g/m2, 13, 000 pedon points)

Data Source: ftp://nssc.nrcs.usda.gov/sww/uofri/ nsslpt48soc.e00

The National Soil Survey (NSS) characterization database is the largest single source of soils data. Over 20,000 geo-referenced pedons (a pedon is defined as a 1 to 10 m2 area of similar soils) across the U.S. have been characterized for multiple purposes as a part of the USDA-NRCS Progressive Soil Survey.

5

State Soil Geographic (STATSGO) and Soil Survey Geographic (SSURGO) data are digital, geo-referenced soil surveys that are supported by USDA/NRCS.

Soil maps in the STATSGO database are organized at 1:250,000 scale by generalizing detailed soil survey data. The minimum size of a mapping unit in STASGO is approximately 600 hectares.

Each STATSGO map is linked to a database that gives the proportionate extent of the various soils for each map unit.

There are STATSGO maps available for every state in the U.S. andthe maps are designed to be used in regional GIS-based research, planning, and management decisions.

http://soils.usda.gov/survey/geography/statsgo/

• The Soil Survey Geographic (SSURGO) soil survey maps range in scale from 1:15,840 to 1:31,680.

• The minimum mapping unit size in SSURGO ranges from 5 to 10 hectares.

• http://soils.usda.gov/survey/geography/ssurgo/

6

Theoretically, the total upwelling radiance ((LtLt) ) from an exposed soil recorded by the sensor onboard the aircraft or satellite is a function of the EMR from the all the sources (Lp, Ls, and Lv).

7

Lp: is the portion of the recorded radiance resulting from the downwelling solar (Esun) and sky (Esky) radiation that never actually reaches the soil surface. This is unwanted atmospheric path radiance noise and should ideally be removed from the imagery prior to trying to extract information about surficial soils or materials.

Ls: the radiation reaches the air-soil interface (boundary layer) and penetrates it approximately ½ wavelength (l) deep into the soil. For example, if the major wavelength of light being investigated is green light, the depth of penetration into the soil column would be approximately 0.275 µm (i.e., ½ of 0.55 µm).

The amount of radiant flux exiting the soil column based on the reflection and scattering taking place at this depth is Ls. The characteristics of the soil organic matter and inorganic constituents and the amount of soil moisture have a significant impact on the amount of this portion of the energy.

8

Lv: Some of the incident downwelling solar and sky radiation may be able to penetrate perhaps a few millimeters or even a centimeter or two into the soil column. This may be referred to as volume scattering, Lv.

The goal of most soil and mineral remote sensing is to extract the radiance of interest from all the other radiance components being recorded by the sensor system.

For example, the scientist interested in identifying the organic and inorganic (mineral) constituents in the very top layer of the soil profile is most concerned with measuring the integrated spectralresponse of the surface and subsurface radiance, i.e., Ls and Lv:

ptvs LLLL −=+

This involves careful radiometric correction of the remote sensor data to remove atmospheric attenuation (Lp).

9

Ideally, we could disentangle the individual contribution of Lsand Lv. However, it is very difficult to distinguish between them.

Nevertheless, it is possible to make some general observations about how surficial soils appears in remote sensor data based on their spectral reflectance properties.

The spectral reflectance characteristics of soils are a function of several important characteristics, including:

• Soil texture (percentage of sand, silt, and clay)• Soil moisture content (e.g., dry, moist, saturated)• Organic matter content• Iron-oxide content, and • Surface roughness

10

One of the most consistent characteristics of dry soil is: increasing reflectance with increasing wavelength, especially in the visible, near- and middle IR portions of the spectrum.

20

60

100

Perc

ent R

efle

ctan

ce

0.5 0.7 1.1 1.30

Wavelength (µm)

80

40

0.9 1.5 1.7 1.9 2.1 2.3 2.5

Silt

Sand

10

30

50

70

90

Soil Texture and Moisture Content

There is relationship between the size of the soil particles found in a mass of soil (e.g., m3) and the amount of moisture that the soil can store.

The finer clay soils have particles that are packed very closely to one another. The interstitial air spaces between the soil particles are very small.

11

Soil Texture and Moisture Content

There is relationship between the size of the soil particles found in a mass of soil (e.g., m3) and the amount of moisture that the soil can store.

The finer clay soils have particles that are packed very closely to one another. The interstitial air spaces between the soil particles are very small.

The amount of moisture held in the surficial soil layer is a function of the soil texture. The finer the soil texture, the greater the soil’s ability to maintain a high moisture content in the presence of precipitation.

The greater the soil moisture, the more incident radiant energy absorbed and the less reflected energy.

12

Soil Organic Matter

The amount of organic matter in the soil has a significant impact on the spectral reflectance characteristics of exposed soils.

Generally, the greater the amount of organic contents in the upper portions of the soil, the greater the absorption of incident energy and the lower the spectral reflectance.

Iron OxideThe existence of iron oxides generally causes an increase in reflectance in the red portion of the spectrum (0.6-0.7 µm), and hence its reddish color.

There is also a noticeable decrease in the blue and green reflectance in the iron-oxide soil. The iron-oxide soil also exhibits an absorption band in the 0.85 – 0.90 µm region when compared with a sandy loam soil with no iron oxide.

13

14

Remote Sensing of Rock and Minerals

Rocks are assemblages of minerals that have interlocking grains or are bound together by various types of cement (usually silicaor calcium carbonate). When there is minimal vegetation and soil present and the rock material is visible directly by the remote sensing system, it may be possible to differentiate between several rock types and obtain information about their characteristics.

Black Dikes

15

Absorption Process

A typical spectral reflectance curve obtained by an imaging spectrometer exhibits various maxima and minima. The minima are caused by strong absorption bands.

Spectra of Three Minerals Spectra of Three Minerals Derived from NASADerived from NASA’’s s Airborne Visible Infrared Airborne Visible Infrared Imaging Spectrometer Imaging Spectrometer (AVIRIS) Measured Using A (AVIRIS) Measured Using A Laboratory Laboratory SpectroradiometerSpectroradiometer(after Van (after Van derder Meer, 1994)Meer, 1994)

Absorption Process

It is noticed that key absorption features associated with kaolinite are typically found at 2.17, 2,21, and 2.32 µm.

It is important to point out that only a hyperspectral sensor with a spectral bandwidth resolution of approximately 10 nm could capture such information.

Spectroradiometers with 20 nm bandwidth might miss the important minima or maxima entirely.

16

Alunite Laboratory Spectra, Simulated Landsat Thematic MapperSpectra, and Spectra from a 63-Channel GERIS Instrument over Cuprite, Nevada

Landsat Thematic Mapper

Laboratory Spectra

GERIS hyperspectral

Wavelength, µm

Per

cent

Ref

lect

ance

(off

set f

or c

larit

y)

0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4

20

30

40

50

60

70

80

90

100

1 2 3 4 5

723

28

29 30 31

32

Alunite

Creating Mineral Maps Using Hyperspectral Data

If we obtain high spectral resolution remote sensing spectra for an unknown surficial rock material, remove the atmospheric effects and convert the brightness values to percent reflectance, then it may be possible to search a spectral library and identify the type of mineral that has an identical or very similar spectra.

17

JPL Spectral Library: Kaolinite CM3(in ERDAS Imagine Software System)

USGS Spectral Library: Aspen Leaf (in ERDAS Imagine Software System)

18

The ASTER spectral library, a compilation of almost 2000 spectra of natural and man made materials.

The ASTER spectral library includes data from three other spectral libraries: the Johns Hopkins University (JHU) Spectral Library the Jet Propulsion Laboratory (JPL) Spectral Library, and the United States Geological Survey (USGS - Reston) Spectral Library.

Typical Typical spectral spectral reflectance reflectance curves in the curves in the region region 0.4 0.4 –– 0.9 0.9 µµm.m.