Soil moisture retrieval from SMAPVEX12 and SMAPVEX16-MB data

Ramata Magagi, Kalifa Goïta

Honqguan Wang (PDF)

Vincent Beauregard (M. Sc. student)

With the collaboration of Andreas Colliander, Alexandre Roy and Tom Jackson

Outline

• Context

• Results from recent investigations with SMAPVEX12 data

- Empirical soil moisture retrievals over wheat fields;

- Polarimetric soil moisture retrievals;

• Current works on SMAPVEX12 and SMAPVEX16-MB data

2

ContextProject designed in the context of the Canadian science plan for SMAP mission

Financial partners:

Canadian Space Agency (CSA)

Natural Sciences and Engineering Research Council of Canada (NSERC)

Collaborators:

Environment Canada (EC)

Agriculture and Agri-Food Canada (AAFC)

University (Sherbrooke, Manitoba, Guelph)

National Aeronautics and Space Administration (NASA), USDA, JPL

CESBIO, France

DLR Microwaves and Radar Institute, Germany3

Study area and data

4

PALS Low altitude Flight lines

X

X

X Ground radiometers

SMAPVEX12 and SMAPVEX16-MB fields

Collection of ground (soil and vegetation) data

network data (SM and temp.)

L-band radiometers

airborne PALS data

airborne UAVSAR data (2012 only)

satellite data

Empirical soil moisture retrievals over wheat fields

from RADARSAT-2 data

5

Empirical Soil moisture estimation over wheat fields

from RADARSAT-2

• Multiple linear regressions

𝒎𝒗 = 𝜷𝟏𝑿𝟏 +⋯+ 𝜷𝒊𝑿𝒊 +⋯+ 𝜷𝒑𝑿𝒑 + 𝜷𝟎 + 𝝐

1) Consider all the variables 𝑿𝒊 (incoherent + coherent)

2) Reduce the dimension by using only selected non-correlated variables

3) Use stepwise regression

6

Empirical Soil moisture estimation over wheat fields (cont.)

• Results from SMAPVEX12

Model Nb of fields Nb of

Observations R2

RMSE

(m³/m³) Variables β p-value

4 8 60 0.598 0.078 𝝈𝑯𝑯𝟎 0.107 0.000

𝝈𝑯𝑽𝟎 -0.117 0.000

𝝈𝑽𝑽𝟎 0.057 0.005

𝝓𝑯𝑯−𝑽𝑽 -0.003 0.000

Hs 2.072 0.000

A -1.637 0.008

5 8 60 0.510 0.084 (Intercepte) 0.662 0.000

𝝈𝑯𝑯𝟎 0.036 0.000

𝝓𝑯𝑯−𝑽𝑽 -0.004 0.000

7

Polarimetric soil moisture retrievals

8

Polarimetric soil moisture retrievals

• Compare 3 model-based polarimetric decompositions

(Freeman-Durden, Hajnsek and An) for soil moisture

retrievals from L-band UAVSAR data;

• Apply the best approach to Multi-angular UAVSAR

9

Freeman-Durden Hajnsek An Classified Image 2012-06-17

0

10

20

30

40

50

60

Surface Dihedral Volume

Perc

ent (%

)

Scattering mechanisms

Freeman

Hajnsek

An

Comparison of 3 polarimetric methods : RGB composite of scattering powers

Dihedral

SurfaceVolume Wang et al. 2017 RSE (under review)

More surface scattering power in An decomposition10

Surface

Dihedral

Volume

Wang et al. 2016 Remote sensing

Wang et al. 2016 IEEE GRSL

Comparison of 3 polarimetric methods (cont.)

Results of soil moisture retrieval from the dominant surface or dihedral scattering component

Best retrieval with Hajnsek method 11

Wang et al. 2017 RSE (under review)

Wang et al. 2016 Remote sensing

Wang et al. 2016 IEEE GRSL

Polarimetric decomposition of Multi-angular UAVSAR data

Surface scattering component

#3160312

#31604 #31605 #31606

Flight lines

(2012-06-17)

14 acquisition days

Wang et al. 2017 IEEE TGRS (under review)Wang et al. 2016 IEEE GRSL

Polarimetric decomposition of Multi-angular UAVSAR data (cont.)

• Results

Retrieval rate RMSE

Multi-incidence angle increases the retrieval rate and decreases the RMSE

165 170 175 180 185 190 195 200 2050

10

20

30

40

50

60

70

80

90

100

DOY 2012

Retr

ieval ra

te (

%)

-1

-2

-3

-4

Multi-

13

Wang et al. 2017 IEEE TGRS (under review)Wang et al. 2016 IEEE GRSL

Current works with SMAPVEX12 and SMAPVEX16-MB

data

14

Preliminary analyses

Field campaigns Data analyses

SMAPVEX16-MB L-band Radiometers :

Ground vs soil moisture

Ground vs low altitude PALS

Low altitude PALS vs soil moisture

SMAPVEX12 and SMAPVEX16-MB Comparison of low altitude PALS

Modelling results

15

SMAPVEX 12 & 16-MB PALS TB (Low altitude) to soil moisture

Objective : Understand the effects of soil and vegetation conditions

on TB for soil moisture retrieval

Ground radiometer TB versus soil moisture• Field #105: Wheat Field #202: Canola (Low vegetation)

16

TBH

TBV

TBH

TBV

Less vegetation over Field #202 Higher sensitivity to soil moisture

Ground radiometer versus PALS

• Field #105: Wheat

17

TBH

TBV

TB of radiometer over wheat field Higher than PALS

Ground radiometer versus PALS (cont.)

• Field #202: Canola

18

TBH

TBV

TB of radiometer over canola field Lower than PALS

Preliminary analysesSMAPVEX 12 & 16-MB PALS TB (Low altitude) to soil moisture

0 0.1 0.2 0.3 0.4 0.5 0.6100

150

200

250

300

June:R=-0.67

July: R=-0.24

Ground measured soil moisture (m3/m3)

PA

LS

passiv

e T

BV (

K)

June

July

0 0.1 0.2 0.3 0.4 0.5 0.6100

140

180

220

260

300

June:R=-0.62

July: R=-0.44

Ground measured soil moisture (m3/m3)

PA

LS

pa

ssiv

e T

BH (

K)

June

July

24 days observation gap from June to July(very different vegetation status )

24 days observation gap from June to July(very different vegetation status )

Low vegetation

High vegetation (24 days later on)

Low vegetation

High vegetation (24 days later on)

More vegetation effect in July 14-22than in June 08-20

TBH TBV

Sensitivity of SMAPVEX16 PALS passive signal to soil moistureCanola

0 0.1 0.2 0.3 0.4 0.5 0.6100

140

180

220

260

300

Ground measured soil moisture (m3/m

3)

PA

LS

passiv

e T

B H (

K)

43

202

203

204

224

234

235

237

June

8 FieldsDifferent color & shape represent different fields

Field #

0 0.1 0.2 0.3 0.4 0.5 0.6100

140

180

220

260

300

Ground measured soil moisture (m3/m3)

PA

LS

passiv

e T

BH (

K)

43

202

203

204

224

234

235

237

July

8 Fields

Field #

20

0 0.1 0.2 0.3 0.4 0.5 0.6100

140

180

220

260

300

June:R=-0.063

July: R=-0.46

Ground measured soil moisture (m3/m3)

PA

LS

pa

ssiv

e T

BH (

K)

June

July

0 0.1 0.2 0.3 0.4 0.5 0.6100

150

200

250

300

June:R=-0.16

July: R=-0.43

Ground measured soil moisture (m3/m3)

PA

LS

pa

ssiv

e T

BV (

K)

June

July

Sensitivity of SMAPVEX16 PALS passive signal to soil moisture

Diversity in soil conditions particularly for soybean;

For June and July, clusters of points.

Soybean

TBH TBV

0 0.1 0.2 0.3 0.4 0.5 0.6100

140

180

220

260

300

Ground measured soil moisture (m3/m

3)

PA

LS

passiv

e T

B H (

K)

41

71

72

101

113

210

223

231

232

236

June

10 FieldsDifferent color & shape represent different fields

Field #

0 0.1 0.2 0.3 0.4 0.5 0.6100

150

200

250

300

Ground measured soil moisture (m3/m3)

PA

LS

passiv

e T

BH (

K)

41

71

72

101

113

210

223

231

232

236

July

10 Fields

Field #

21

0 0.1 0.2 0.3 0.4 0.5 0.6100

140

180

220

260

300

June:R=-0.36

July: R=-0.36

Ground measured soil moisture (m3/m3)

PA

LS

pa

ssiv

e T

BV (

K)

June

July

0 0.1 0.2 0.3 0.4 0.5 0.6100

140

180

220

260

300

June:R=-0.47

July: R=-0.36

Ground measured soil moisture (m3/m3)

PA

LS

pa

ssiv

e T

BH (

K)

June

July

Less sensitivity to soil conditions

TBH TBV

Sensitivity of SMAPVEX16 PALS passive signal to soil moistureWheat

DTB over wheat fields not large as for other crops

0 0.1 0.2 0.3 0.4 0.5 0.6100

140

180

220

260

300

Ground measured soil moisture (m3/m

3)

PA

LS

passiv

e T

B H (

K)

31

32

62

91

104

105

233

June

7 Fields

Different color & shape represent different fields

Field #

0 0.1 0.2 0.3 0.4 0.5 0.6100

140

180

220

260

300

Ground measured soil moisture (m3/m3)

PA

LS

passiv

e T

BH (

K)

31

32

62

91

104

105

233

JulyField #

7 Fields

Preliminary analyses

Field campaigns Data analyses

SMAPVEX16-MB L-band Radiometers : Ground vs soil moistureGround vs low altitude PALSLow altitude PALS vs soil moisture

SMAPVEX12 and SMAPVEX16-MB Comparison of low altitude PALS

Modelling results

23

SMAPVEX 12 & 16-MB PALS TB (Low altitude) to soil moisture

Objective : Understand the effects of soil and vegetation conditions on TB for soil moisture retrieval

Soil moisture conditions in 2012 and 2016

155 160 165 170 175 180 185 190 195 200 2050

0.1

0.2

0.3

0.4

0.5

0.6

DOY 2012

Gro

und m

easure

d m

v (

m3/m

3)

155 160 165 170 175 180 185 190 195 200 2050

0.1

0.2

0.3

0.4

0.5

0.6

DOY 2016

Gro

und m

easure

d m

v (

m3/m

3)

SMAPVEX12

SMAPVEX16-MB

24

• Ground measurements mv

• Ground measurements VWC

160 165 170 175 180 185 190 195 200 2050

1

2

3

4

5

DOY

VW

C (

kg/m

2)

Canola 2012

Corn 2012

Soybean 2012

Wheat 2012

Canola 2016

Corn 2016

Soybean 2016

Wheat 2016

25

Vegetation conditions in 2012 and 2016

Lower VWC in SMAPVEX16-MB than SMAPVEX12

26

Canola 2012

Corn 2012

Soybean 2012

Wheat 2012

Canola 2016

Corn 2016

Soybean 2016

Wheat 2016

Temperature conditions in 2012 and 2016

Canola 2012

Corn 2012

Soybean 2012

Wheat 2012

Canola 2016

Corn 2016

Soybean 2016

Wheat 2016

Vegetation temperature

Soil temperature

Lower in 2016

Lower in 2016

0 0.1 0.2 0.3 0.4 0.5 0.6100

140

180

220

260

300

Ground measured soil moisture (m3/m3)

PA

LS

pa

ssiv

e T

BH (

K)

June 2012

July 2012

June 2016

July 2016

0 0.1 0.2 0.3 0.4 0.5 0.6100

140

180

220

260

300

Ground measured soil moisture (m3/m3)

PA

LS

passiv

e T

BV (

K)

June 2012

July 2012

June 2016

July 2016

Canola

Comparison between 2012 and 2016 PALS data

27

TBH TBV

Low soil moisture during SMAPVEX12 Low contrast between June and July 2012

High soil moisture during SMAPVEX16 High contrast between June and July 2016

0 0.1 0.2 0.3 0.4 0.5 0.6100

140

180

220

260

300

Ground measured soil moisture (m3/m3)

PA

LS

pa

ssiv

e T

BH (

K)

June 2012

July 2012

June 2016

July 2016

0 0.1 0.2 0.3 0.4 0.5 0.6100

140

180

220

260

300

Ground measured soil moisture (m3/m3)

PA

LS

pa

ssiv

e T

BV (

K)

June 2012

July 2012

June 2016

July 2016

Soybean

Lower VWC & Surface and vegetation temperature for all crops during SMAPVEX16-MB

TB in 2016 should be lower than TB 2012: not always observed

28

Comparison between 2012 and 2016 PALS data

TBH TBV

DT in SMAPVEX12 > DT in SMAPVEX16-MB

Wheat

29

Comparison between 2012 and 2016 PALS data

TBH TBV

Lower VWC & Surface and vegetation temperature for all crops during SMAPVEX16-MB

TB in 2016 should be lower than TB 2012: not always observed

Preliminary analyses of modelling results obtained with

SMAPVEX12 and SMAPVEX16-MB data

Objective : Soil moisture mapping

• Use of SMAPVEX12 PALS data into L-MEB model to estimate bp

parameters (p is the polarization);

• Analyze the performance of bp parameters with SMAPVEX16-MB

PALS data;

• Develop the retrieval approaches (not yet done) 30

(1 R ) (1 )(1 )T (1 )(1 )T Rp p p soil p vege p vege p pTB T

/cos( ) * /cos( )p pb VWC

p e e

Preliminary analyses of modelling results with SMAPVEX12 data

(cont.)

• Optimal b parameters obtained from low altitude PALS TB in 2012

31

• Performance of optimization

Preliminary analyses of modelling results with SMAPVEX12 data (cont.)

Soybean (9 fields, 17 days)

Wheat (4 fields , 17 days)

Canola (1 field , 17 days)

32

TBH

TBH

TBH

TBV

TBV

TBV

The b parameter of canola based only one field

Preliminary analyses of modelling results with SMAPVEX16-MB data (cont.)

100 150 200 250 300100

150

200

250

300

Measured TBH (K)

Sim

ula

ted

TB

H (

K)

R=0.58

RMSE=21.96 K

100 150 200 250 300100

150

200

250

300

Measured TBV (K)

Sim

ula

ted

TB

V (

K)

R=0.58

RMSE=23.30 K

Soybean (10 fields , 12 days)

100 150 200 250 300100

150

200

250

300

Measured TBH (K)

Sim

ula

ted

TB

H (

K)

R=0.23

RMSE=20.36 K

100 150 200 250 300100

150

200

250

300

Measured TBV (K)

Sim

ula

ted

TB

V (

K)

R=0.19

RMSE=13.63 K

Wheat (7 fields , 12 days)

100 150 200 250 300100

150

200

250

300

Measured TBH (K)

Sim

ula

ted

TB

H (

K)

R=0.12

RMSE=34.90 K

100 150 200 250 300100

150

200

250

300

Measured TBV (K)

Sim

ula

ted

TB

V (

K)

R=0.044

RMSE=25.99 K

Canola (8 fields , 12 days)

Calibrated L-MEB underestimates PALS TB in 2016

Roughness or vegetation water content or both??33

TBH

TBH

TBH

TBV

TBV

TBV

• Performance of b parameter

Perspectives

• Refine the data analysis and model calibration;

• Develop the retrieval algorithms of soil moisture;

• Soil moisture disaggregation with RADARSAT-2.

34

Acknowledgments

Canadian Space Agency (CSA)

Natural Sciences and Engineering Research Council of Canada (NSERC)

Environment Canada (EC)

Agriculture and Agri-Food Canada (AAFC)

University (Sherbrooke, Manitoba, Guelph)

National Aeronautics and Space Administration (NASA)

United States Department of Agriculture (USDA)

Jet Propulsion Laboratory (JPL)

CSA-SOAR program

CESBIO, France

DLR Microwaves and Radar Institute, Germany35