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 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.
1
Soil Horizons
The standard horizons in a
typical soil profile situated
above the bedrock include
O, A, E, B, C, R, and W
that 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 a mixture of inorganic
soil particles and decaying organic matter.
O horizon soils typically have a dark brown
or even black surface layer 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”.
2
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
3
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 the soil 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.
National Soil Survey Soil Organic Carbon Calculations
(Total soil organic carbon content value in unit of g/m2, > 20, 000 pedon points)
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0 - 9604.29 (g/m2)
9604.30 - 25782.89
25782.30 - 75874.14
75874.15 - 209662.70
209662.71-454392.19
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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.
4
State Soil Geographic (STATSGO2) and Soil Survey Geographic
(SSURGO) data are digital, geo-referenced soil surveys that are
supported by USDA/NRCS.
The Digital General Soil Map of the United States or STATSGO2 is a
broad-based inventory of soils and non-soil areas that occur in a
repeatable pattern on the landscape and that can be cartographically
shown at the scale mapped of 1:250,000 in the continental U.S.,
Hawaii, Puerto Rico, and the Virgin Islands (minimum size of a
mapping unit is approximately 600 hectares) and 1:1,000,000 in
Alaska.
The level of mapping is designed for broad planning and
management uses covering state, regional, and multi-state areas. The
U.S. General Soil Map is comprised of general soil association units
and is maintained and distributed as a spatial and tabular dataset.
http://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/ref/?cid=nrcs142p2_053631
http://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/ref/?cid=nrcs142p2_053631
5
The SSURGO database contains information about soil as
collected by the National Cooperative Soil Survey over the course
of a century. The information can be displayed in tables or as
maps and is available for most areas in the United States.
The information was gathered by walking over the land and
observing the soil. Many soil samples were analyzed in
laboratories. The maps outline areas called map units. The map
units describe soils and other components that have unique
properties, interpretations, and productivity.
The information was collected at scales ranging from 1:12,000 to
1:63,360. More details were gathered at a scale of 1:12,000 than
at a scale of 1:63,360. The mapping is intended for natural
resource planning and management by landowners, townships,
and counties. Some knowledge of soils data and map scale is
necessary to avoid misunderstandings.
http://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/ref/?cid=nrcs142p2_053627
6
Theoretically, the total upwelling radiance (Lt) from an exposed
soil recorded by the sensor onboard the aircraft or satellite is a
function of the EMR from all 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 m (i.e., ½ of 0.55 m).
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 spectral
response of the surface and subsurface radiance, i.e., Ls and Lv:
L s  Lv  Lt  L p
This involves careful radiometric correction of the remote sensor
data to remove atmospheric attenuation (Lp).
9
Ideally, we could disentangle the individual contribution of Ls
and 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 sensing 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.
Percent Reflectance
100
90
80
Silt
70
60
50
Sand
40
30
20
10
0
0.5 0.7
0.9
1.1
1.3 1.5 1.7 1.9
Wavelength (m)
2.1
2.3
2.5
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
Moisture
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.
13
Iron Oxide
The existence of iron oxides
generally causes an increase in
reflectance in the red portion of
the spectrum (0.6-0.7 m), 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 m region
when compared with a sandy
loam soil with no iron oxide.
14
15
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 silica
or 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
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
Derived from NASA’s
Airborne Visible Infrared
Imaging Spectrometer
(AVIRIS) Measured Using A
Laboratory Spectroradiometer
(after Van der Meer, 1994)
16
Absorption Process
It is noticed that key absorption
features associated with
kaolinite are typically found at
2.17, 2,21, and 2.32 m.
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.
Percent Reflectance (offset for clarity)
Alunite Laboratory Spectra, Simulated Landsat Thematic Mapper
Spectra, and Spectra from a 63-Channel GERIS Instrument over
Cuprite, Nevada
90
80
Laboratory
Spectra
Alunite
70
60
50
1 2 3
5
4
Landsat Thematic Mapper
40
23
29
30 31
7
30
20
10
GERIS
hyperspectral
0
0.4 0.6
0.8
28
32
1.0 1.2 1.4 1.6 1.8 2.0
Wavelength, m
2.2
2.4
17
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.
JPL Spectral Library: Alunite SO-4A
(in ERDAS Imagine Software System)
18
JPL Spectral Library: Kaolinite CM3
(in ERDAS Imagine Software System)
USGS Spectral Library: Aspen Leaf
(in ERDAS Imagine Software System)
19
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
spectral
reflectance
curves in the
region
0.4 – 0.9 m.
20