ERS186: Environmental Remote Sensing Lecture 9: Soils.
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Transcript of ERS186: Environmental Remote Sensing Lecture 9: Soils.
ERS186:Environmental Remote Sensing
Lecture 9:
Soils
Overview
• Applications– Soil Science
• Physical Principles– Reflectance (specular and diffuse scattering)– Absorption bands– Dielectric constants
• Sensors– RADAR– Thermal– Hyperspectral
Definitions
• Soil: the weathered material between the surface of the Earth and the bedrock.
• Soils are composed of different composition and sizes of particles of inorganic mineral and organic matter
• Particles are about 50% of the soil volume, pores occupy the rest of the space. Pores can contain air or water (or ice!)
• Soils have vertical zonation (soil horizons) created by biological, chemical and physical processes
Soil Horizons• O horizon: > 20% partially decayed
organic matter (“humus”)• A horizon: zone of
eluviation/leaching; water leaches many minerals; often pale and sandy
• E horizon: mineral layer with loss of some combination of silicate clay, iron, aluminum
• B horizon: zone of illuviation; materials leached from other zones end up here; often lots of clay and iron oxides
• C horizon: weathered parent material; mostly mineral
• W horizon: water layer; Wf if permenantly frozen
• R horizon: bedrock
Soil Grain Size
0.002 0.05 0.1 0.25 0.5 1 2 mm
Particle size relative to a grain of sand 0.15 mm in diameter
Sand
SiltClay
Clay SiltSand
v. fine fine medium coarse v. coarseGravel
0.002 0.06 0.2 2 mm
Clay Silt SandGravel Stones
fine medium coarse
0.006 0.02 0.6
0.15 mm
fine medium coarse
76.2
0.002 0.2 2 mm
Clay SiltSand
Gravel
0.02
fine coarse
a. Soil Science Society of America and U.S. Department of Agriculture Soil Particle Size Scale
b. MIT and British Standards Institute
c. International Society of Soil Science
Soil Grain Size
• Different size particles play different roles in soil:– Sand (0.05 to 2.0 mm): large air spaces, rapid drainage of
water
– Silt (0.002 to 0.05 mm): enhance movement and retention of soil capillary water
– Clay (< 0.002 mm): enhance movement and retention of soil capillary water; carry electrical charges which hold ions of dissolved minerals (e.g. potassium and calcium)
Soil Texture
• Proportion of sand, silt and clay in a soil (or horizon), usually calculated as % weight for each type of particle.
• These %s can be broken up into different soil-texture classes.
100
90
80
70
60
50
40
30
20
10
100
100 90 80 70 60 50 40 30 20 10
90
80
70
60
50
40
30
20
10
Cla
y (%
)
Sand (%)
Silt (%)
read
read
read
Clay
SiltSand
loamysand
sandy loam
Loam
sandy clay loam
silty clay
silty clay loam
clay loam
sandy clay
silt loam
Soil Taxonomy
• Similar to biological taxonomy -- dichotomous keys based on soil profiles, soil color, soil-texture class, moisture content, bulk density, porosity, and chemistry are used to ID different types of soils.
The Question
• What are the important properties of a soil in an RS image?– Soil texture– Soil moisture content– Organic matter content– Mineral contents, including iron-oxide and
carbonates– Surface roughness
Exposed Soil Radiance
• Lt = Lp + Ls + Lv
• Lt = at-sensor radiance of a pixel of exposed soil• Lp = atmospheric path radiance, usually needs to be removed through
atmospheric correction• Ls = radiance reflected off the air-soil interface (boundary layer)
– Soil organic matter and soil moisture content significantly impact Ls; typically characterize the O horizon, the A horizon (if no O), or lower levels if A and O are nonexistant.
• Lv = volume scattering, EMR which penetrates a few mm to cm.– penetrates approximate 1/2 the wavelength– Function of the wavelength (so RADAR may penetrate farther), type and
amount of organic/inorganic constituents, shape and density of minerals, degree of mineral compaction, and the amount of soil moisture present.
Exposed Soil Radiance
Exposed Soil Radiance
SIR-C Color Composite:• Red = C-band HV• Green = L-band HV• Blue = L-band HH
SIRSIR--C Color Composite:C Color Composite:•• Red = CRed = C--band HVband HV•• Green = LGreen = L--band HVband HV•• Blue = LBlue = L--band HHband HH
Space Shuttle Color-Infrared
Photograph
Space Shuttle Space Shuttle ColorColor--Infrared Infrared
PhotographPhotograph
Basic Dry Soil Spectrum
20
60
100
Perc
ent R
efle
ctan
ce
0.5 0.7 1.1 1.30
Wavelength ( m)
80
40
0.9 1.5 1.7 1.9 2.1 2.3 2.5
Silt
Sand
10
30
50
70
90
Key characteristic of soil spectrum: increasing reflectance with increasing wavelength through the visible, near and mid infrared portions of the spectrum
Soil Moisture
• Water is a strong absorber, so soils with more moisture will be darker over most of the VNIR and SWIR portions of the spectrum than drier soils.
• The depths of the water absorption bands at 1.4, 1.9 and 2.7 m can be used to determine soil moisture.
specular reflectance
incident energy
interstitial air space
specular reflectance
soil water
a.
b.
dry soil
wet soil
volume reflectance
specular reflectance
incident energy
Soil Moisture and Texture
• Since clayey soil holds water more tightly than sandy soil, the water absorption features will be more prominent in clayey soils given the same amount of time since the last precipitation or watering.
• AVIRIS can be useful for quantifying these absorption features.
20
60
Per
cent
Ref
lect
ance
0.5 0.7 1.1 1.30
40
0.9 1.5 1.7 1.9 2.1 2.3 2.5
22 – 32%
10
30
50
Sand
20
60
0.5 0.7 1.1 1.30
Wavelength (m)
40
0.9 1.5 1.7 1.9 2.1 2.3 2.5
35 – 40% 10
30
50 2 – 6%
0 – 4% moisture content
5 – 12%
Clay
a.
b.
Per
cent
Ref
lect
ance
SandSandSand
ClayClayClay
Soil Moisture from RADAR
• Different materials conduct electricity better than others (different complex dielectric constant).
• Higher dielectric constants (more moisture) yields higher RADAR backscatter. Melfort, Saskatchewan, Canada,
ERS-1: Rainfall was incident on the lower half of the image but not on the upper half.
Soil Moisture from Thermal Sensors
• Water has a higher thermal capacity than soil and rock.
• Moist soils will change in temperature more slowly than dry soils.
Soil Moisture from Thermal Sensors
Daedalus thermal image (night time). If we had a daytime image to compare it to, we could see the amount of change in temperature and make inferences on the soil moisture content (less change = more moisture).
Identifying Clayey Soils
Soils with a large amount of clay exhibit hydroxyl absorption bands at 1.4 and 2.2 m. 2.2 m is more useful since it doesn’t overlap the water absorption feature.
20
60
Per
cent
Ref
lect
ance
0.5 0.7 1.1 1.30
40
0.9 1.5 1.7 1.9 2.1 2.3 2.5
22 – 32%
10
30
50
Sand
20
60
0.5 0.7 1.1 1.30
Wavelength (m)
40
0.9 1.5 1.7 1.9 2.1 2.3 2.5
35 – 40% 10
30
50 2 – 6%
0 – 4% moisture content
5 – 12%
Clay
a.
b.
Per
cent
Ref
lect
ance
SandSandSand
ClayClayClay
Soil Organic Matter
Organic matter is a strong absorber of EMR, so more organic matter leads to darker soils (lower reflectance curves).
Soil Organic Matter
Organic matter content in the Santa Monica mountains mapped using AVIRIS (Palacios-Orueta et al. 1999).
Iron Oxide
Recall that iron oxide causes a charge transfer absorption in the UV, blue and green wavelengths, and a crystal field absorption in the NIR (850 to 900 nm). Also, scattering in the red is higher than soils without iron oxide, leading to a red color.
Iron Oxide
Iron content in the Santa Monica mountains mapped using AVIRIS (Palacios-Orueta et al. 1999).
Surface Roughness• If a surface is smooth (particle
size is small relative to wavelength), we expect a lot of specular reflection.– Only sensors positioned at the
correct angle will see the bright reflectance. All other angles will see a dark surface (including all RADAR imagery).
– Smooth surfaces are clayey or silty and often contain strong absorbers such as moisture, organic content, and iron oxide.
• A rough surface generates a lot of diffuse reflection.– Conversely, well drained sands
are often very bright, regardless of angle.
Surface Roughness
• C/X-SAR (C-band) image of Oxford County, Ontario, Canada: Conservation tillage (the retention of crop residue on the soil surface) can diminish soil erosion. Conventional tillage produces a much rougher surface, and therefore brighter backscatter. The goal of this study was to determine if tillage practices could be identified using SAR imagery.