1

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

2

Motivation

Surface soil moisture is a fundamental state variable controlling:

water infiltration and runoff, evaporation, heat and gas exchange, solute infiltration, soil erosion, etc.

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

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

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.

6

Theoretical considerations

Soil reflectance depends on soil water content.

R = Reflected/Incident

Reflectance:

7

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

8

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

21

2

R

R

9

Proposed Model in SWIR bands

d d

s d water s

s s

s s s

Strong water absorption

1

d

s s

A linear τ-θ relationship in SWIR …

swater << sd

10

d

s s d

d

s s

Non-linear model (All optical bands)

Linear model [SWIR bands]

11

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)

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

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

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

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

16

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)

17

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.

18

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:

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

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

Thanks

for your

attention