Estimation of Soil Water Content Using Short Wave Infrared Remote Sensing
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Transcript of Estimation of Soil Water Content Using Short Wave Infrared Remote Sensing
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Morteza Sadeghi Dept. Plants, Soils, and Climate, Utah State University
Scott B. Jones Dept. Plants, Soils, and Climate, Utah State University
Stephen BialkowskiDept. Chemistry and Biochemistry, Utah State University
William Philpot School of Civil & Environmental Engineering, Cornell University
Estimation of Soil Water Content Using Short Wave Infrared Remote
Sensing
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Motivation
Surface soil moisture is a fundamental state variable controlling:
water infiltration and runoff, evaporation, heat and gas exchange, solute infiltration, soil erosion, etc.
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Satellite remote sensing provides large-scale estimates of soil water content.
Optical [0.4-2.5 μm]
Electromagnetic radiation of soils in various wavelengths is correlated with surface moisture content.
Thermal [3.5-14 μm]
Microwave [0.5-100 cm]
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Microwave RS techniques have demonstrated the most promising ability
for globally monitoring soil moisture.
Penetration depth of microwave is high.
Measurements are not impeded by clouds or darkness.
Spatial resolution of microwave satellites is inherently coarse.
Optical/thermal satellites provide favorable means for
downscaling of microwave estimates of soil moisture.
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Physical Practical
Most of the optical models are empirical with no physical origin, while the
physically-based methods require difficult-to-determine input information.
Our objective was to develop a physically-based
and also practical model.
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Theoretical considerations
Soil reflectance depends on soil water content.
R = Reflected/Incident
Reflectance:
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zkI sI
sJ
kJ
I J
0
( , )( ) ( , ) ( , )
dI zk s I z sJ z
dz
( , )
( ) ( , ) ( , )dJ z
k s J z sI zdz
Kubelka & Munk [1931] Radiative Transfer Theory
Absorbed
light
Scatteredlight
Forward flux
Backward
flux
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Proposed Model
d
s s d
Saturated
water content
Soil water
content
Transformed
reflectanceTransforme
d reflectance
of saturated
soil
Transforme
d
reflectance
of dry soil
d d
s d water s
s s
s s s
Relative
scattering
coefficient
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2
R
R
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Proposed Model in SWIR bands
d d
s d water s
s s
s s s
Strong water absorption
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d
s s
A linear τ-θ relationship in SWIR …
swater << sd
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d
s s d
d
s s
Non-linear model (All optical bands)
Linear model [SWIR bands]
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500 1000 1500 2000 2500 3000
Ref
lect
ance
0.00
0.05
0.10
0.15
0.20
0.25
0.30
= 0
500 1000 1500 2000 2500 3000
0.00
0.05
0.10
0.15
0.20
0.25
0.30
= 0
Wavelength (nm)
500 1000 1500 2000 2500 3000
Ref
lect
ance
0.0
0.1
0.2
0.3
0.4
0.5
= 0
Wavelength (nm)
500 1000 1500 2000 2500 3000
0.0
0.1
0.2
0.3
0.4
0.5
= 0
Aridisol Andisol
Mollisol Entisol
Validation
Lobell and Asner
(2002)
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Evaluations were performed at six bands corresponding
to the Landsat ETM bands:
band 1 (blue, 480 nm)
band 2 (green, 560 nm)
band 3 (red, 660 nm)
band 4 (near infrared, 830 nm)
band 5 (SWIR, 1650 nm)
band 7 (SWIR, 2210 nm)
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0.0 0.1 0.2 0.3 0.4
Tra
nsfo
rmed
re
flect
ance
0
2
4
6
8
10
12 480 nm (band 1)560 nm (band 2)660 nm (band 3)830 nm (band 4)
= 0.157
= 0.217
= 0.295
= 0.371
Aridisol
0.0 0.2 0.4 0.6 0.8
0
5
10
15
20
25
= 0.057
= 0.088
= 0.119
= 0.164
Soil water content
0.0 0.2 0.4 0.6 0.8 1.0
Tra
nsfo
rmed
re
flect
ance
0
2
4
6
8
10
12
14
Andisol
Mollisol
= 0.100
= 0.114
= 0.134
= 0.193
Soil water content
0.0 0.1 0.2 0.3 0.4 0.5 0.6
0
2
4
6
8
10
12
14
Entisol
= 0.109
= 0.162
= 0.289
= 0.431
Non-linear model at VIS/NIR
14Soil water content
0.0 0.2 0.4 0.6 0.8
Tra
nsfo
rme
d re
flect
anc
e
0
1
2
3
4
5
6
AridisolAndisolMollisolEntisol
Soil water content
0.0 0.2 0.4 0.6 0.8
0
1
2
3
4
5
6
1650 nm (band 5)
2210 nm (band 7)
= 0.331
= 0.528
Linear model at SWIR bands
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Soil water content
0.0 0.2 0.4 0.6
0
1
2
3
4
Soil water content
0.0 0.2 0.4 0.6
Tra
nsfo
rmed
re
flect
anc
e
0.0
0.5
1.0
1.5
2.0
2.5
LemooreTomelloso
1650 nm (band 5)
2210 nm (band 7)
Using a single calibration equation for a large area
Whiting et al. (2004) data
25 km2 (near Lemoore, CA)clay loam, sandy clay loam and silty clay loam
27 km2 (near Tomelloso, Spain)loam, sandy loam and silt loam
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Measured water content
0.0 0.2 0.4 0.6 0.8
Est
ima
ted
wa
ter
conte
nt
0.0
0.2
0.4
0.6
0.8
Lemoore, RMSE = 0.067Tomelloso, RMSE = 0.077
Measured water content
0.0 0.2 0.4 0.6 0.8
Est
ima
ted
wa
ter
conte
nt
0.0
0.2
0.4
0.6
0.8
Aridisol, RMSE = 0.005Andisol, RMSE = 0.036Mollisol, RMSE = 0.030Entisol, RMSE = 0.012
Linear model performance at SWIR (Band 7]
Lobell and Asner (2002)
Whiting et al. (2004)
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The models parameters are physically defined. So, linking the model
parameters to soil basic information such as texture, color or
taxonomic class would be feasible.
The link would enhance the model’s applicability using existing soil databases (e.g. USDA-NRCS soil maps).
Conclusions:
There exists a linear relationship between the transformed reflectance and soil water content in the SWIR bands.
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A simple method for its application, without the need for ground measurements, could be to collect time series of reflectance data at a given location, including the full range of saturation.
The linear model’s parameters could then be resolved as the maximum
and minimum values of the transformed reflectance.
Conclusions:
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Next step:
Testing the model for large-scale applications when facing satellite-scale
challenges such as:
high degrees of heterogeneity
vegetation
surface roughness
topographical features
…
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Reference:
Sadeghi, M., S. B. Jones, W. D. Philpot. 2015. A Linear Physically-Based Model for Remote Sensing of Soil Moisture using Short Wave Infrared Bands. Remote Sensing of Environment, 164, 66–76.
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Thanks
for your
attention