1) The document proposes a linear model to estimate soil water content using short wave infrared (SWIR) remote sensing. 2) It finds that there exists a linear relationship between transformed reflectance and soil water content in SWIR bands, due to strong water absorption in these wavelengths. 3) Evaluation of the model using data from multiple field studies shows it can accurately estimate soil water content, with root mean square errors generally below 0.08.

1. Morteza Sadeghi
Dept. Plants, Soils, and Climate, Utah State University
Scott B. Jones
Dept. Plants, Soils, and Climate, Utah State University
Stephen Bialkowski
Dept. 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
1

2. Motivation
2
Surface soil moisture is a fundamental state variable controlling:
 water infiltration and runoff,
 evaporation,
 heat and gas exchange,
 solute infiltration,
 soil erosion,
 etc.

3. 3
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]

4. 4
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.

5. 5
Physical Practical
Most of the optical models are empirical with no physical origin, while the
physically-based methods require difficult-to-determine input information.

6. 6
Theoretical considerations
Soil reflectance depends on soil
water content.
R = Reflected/Incident
Reflectance:

7. 7
z kI
kJ
I J
0
( , )
( ) ( , ) ( , )
dI z
k s I z sJ z
dz

    
( , )
( ) ( , ) ( , )
dJ z
k s J z sI z
dz

   
Kubelka & Munk [1931] Radiative Transfer Theory

8. 8
Proposed Model
 
 
d
s s d
  
     


  
d d
s d water s
s s
s s s


 

 
2
1
2
R
R




9. 9
Proposed Model in SWIR bands
d d
s d water s
s s
s s s


 

Strong water absorption
1 
d
s s
 
  



swater << sd

10. 10
 
 
d
s s d
  
     


  
d
s s
 
  



Non-linear model
(All optical bands)
Linear model
[SWIR bands]

11. 11
500 1000 1500 2000 2500 3000
Reflectance
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
Reflectance
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

12. 12
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)

13. 13
0.0 0.1 0.2 0.3 0.4
Transformedreflectance
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
Transformedreflectance
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

14. 14
Soil water content
0.0 0.2 0.4 0.6 0.8
Transformedreflectance
0
1
2
3
4
5
6
Aridisol
Andisol
Mollisol
Entisol
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

15. 15
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
Transformedreflectance
0.0
0.5
1.0
1.5
2.0
2.5
Lemoore
Tomelloso
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

16. 16
Measured water content
0.0 0.2 0.4 0.6 0.8
Estimatedwatercontent
0.0
0.2
0.4
0.6
0.8
Lemoore, RMSE = 0.067
Tomelloso, RMSE = 0.077
Measured water content
0.0 0.2 0.4 0.6 0.8
Estimatedwatercontent
0.0
0.2
0.4
0.6
0.8
Aridisol, RMSE = 0.005
Andisol, RMSE = 0.036
Mollisol, RMSE = 0.030
Entisol, RMSE = 0.012
Linear model performance at SWIR (Band 7]
Lobell and Asner (2002) Whiting et al. (2004)

17. 17
Conclusions:
 There exists a linear relationship between the transformed
reflectance and soil water content in the SWIR bands.

18. 18
Conclusions:

19. 19
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
…

20. 20
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.

21. 21
Thanks
for your attention