ImagingSpectroscopyforCharacterizingSoil ......cl ay silty cl ay sand y cl ay cl ay loamsilty sand y...

8
Imaging Spectroscopy for Characterizing Soil Proper7es over Large Areas Debsunder Dutta 1 , Praveen Kumar 1 and Jonathan Greenberg 2 1 Department of Civil and Environmental Engineering, 2 Department of Geography and Geographic Information Science University of Illinois at Urbana Champaign 2015 HyspIRI Science Symposium 1

Transcript of ImagingSpectroscopyforCharacterizingSoil ......cl ay silty cl ay sand y cl ay cl ay loamsilty sand y...

Page 1: ImagingSpectroscopyforCharacterizingSoil ......cl ay silty cl ay sand y cl ay cl ay loamsilty sand y cl ay loam loam silty loam sand y loam loam y sandsilt sand 10 20 30 40 50 60 70

Imaging  Spectroscopy  for  Characterizing  Soil  Proper7es  over  Large  Areas  

Debsunder Dutta1, Praveen Kumar1 and Jonathan Greenberg2

1Department of Civil and Environmental Engineering, 2Department of Geography and Geographic Information Science

University of Illinois at Urbana Champaign

2015 HyspIRI Science Symposium 1  

Page 2: ImagingSpectroscopyforCharacterizingSoil ......cl ay silty cl ay sand y cl ay cl ay loamsilty sand y cl ay loam loam silty loam sand y loam loam y sandsilt sand 10 20 30 40 50 60 70

Study  Area  and  Methods  

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Sources: Esri, DeLorme, NAVTEQ, TomTom, Intermap, increment P Corp., GEBCO,USGS, FAO, NPS, NRCAN, GeoBase, IGN, Kadaster NL, Ordnance Survey, EsriJapan, METI, Esri China (Hong Kong), swisstopo, and the GIS User Community

0 10 205 Kilometersµ

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100 data points76 data points with NDVI <0.7

training and test set split 80% and 20%

224 predictor set

10

Reduced set of 45 predictors

10 10 10 5

Observed Soil Texture Data

clay

silty clay

sandy clay

clay loam silty clay loam

sandy clay loam

loam

silty loam

sandy loam

siltloamy sandsand

102030405060708090

10

20

30

40

50

60

70

80

90

1020

3040

5060

7080

90

[%] Sand 50−2000 µm

[%] C

lay

0−2 µ

m

[%] S

ilt 2−50 µ

m

3 6

8

91011

12

13

14

16

1718

19

20

21

23

24

25

26

2728 29

30

31

3233

34

3536

37

38

39

40

42

4344

46

48

49

50

51

52

57

58

61

62

63

64

65

666768

71

72 75

76

80

81

8283

8485

86

87

8889

91

92

9394

96

98

99

100

Predicted Soil Texture Data

clay

silty clay

sandy clay

clay loam silty clay loam

sandy clay loam

loam

silty loam

sandy loam

siltloamy sandsand

102030405060708090

10

20

30

40

50

60

70

80

90

1020

3040

5060

7080

90

[%] Sand 50−2000 µm

[%] C

lay

0−2 µ

m

[%] S

ilt 2−50 µ

m

3

6

8

9

10

11

12

13

14

16

17

18

1920

21

23

24

2526 27

28

29

30

31

32

33

3435

36

37

38

39

40

42

4344

46

48 49

50

51

52

57

58

61

62

63

64

656667687172

75

768081

8283

84

85

86

87

8889

91

92

9394

96

98

99

100

Lasso 50 times

Bootstrapping

50 times for final model coefficients

Final Model Coefficients

clay

silty claysandy clay

clay loam silty clay loam

sandy clay loam

loamsilty loam

sandy loamsiltloamy sand

sand

102030405060708090

10

20

30

40

50

60

70

80

90

1020

3040

5060

7080

90

[%] Sand 50−2000 µm

[%] C

lay 0−

2 µm

[%] Silt 2−50 µm

3 6

8

91011

12

13

14

16

1718

19

20

21

23

24

25

26

2728 29

30

31

3233

34

3536

37

38

39

40

42

4344

46

48

49

50

51

52

57

58

61

62

63

64

65

666768

71

72 75

76

80

81

8283

8485

86

87

8889

91

92

9394

96

98

99

100

clay

silty claysandy clay

clay loam silty clay loam

sandy clay loam

loamsilty loam

sandy loamsiltloamy sand

sand

102030405060708090

10

20

30

40

50

60

70

80

90

1020

3040

5060

7080

90

[%] Sand 50−2000 µm

[%] C

lay 0−

2 µm

[%] Silt 2−50 µm

3

6

8

9

10

11

12

13

14

16

17

18

1920

21

23

24

2526 27

28

29

30

31

32

33

3435

36

37

38

39

40

42

4344

46

48 49

50

51

52

57

58

61

62

63

64

656667687172

75

768081

8283

84

85

86

87

8889

91

92

9394

96

98

99

100

Observed Soil Texture Data Predicted Soil Texture Data

Page 3: ImagingSpectroscopyforCharacterizingSoil ......cl ay silty cl ay sand y cl ay cl ay loamsilty sand y cl ay loam loam silty loam sand y loam loam y sandsilt sand 10 20 30 40 50 60 70

Spa2al  Correla2on  of  Soil  Cons2tuents  

Page 4: ImagingSpectroscopyforCharacterizingSoil ......cl ay silty cl ay sand y cl ay cl ay loamsilty sand y cl ay loam loam silty loam sand y loam loam y sandsilt sand 10 20 30 40 50 60 70

Spa2al  Correla2on  of  Soil  Cons2tuents  

0 10 205 Kms

Mg [mg/kg]0 - 200

200 - 400

400 - 600

600 - 800

800 - 1,000

1,000 - 1,200

1,200 - 1,400

1,400 - 1,600

1,600 - 1,800

1,800 - 2,000

2,000 - 5,000

Erroneous Values

Water

Vegetation

0 10 205 Kms

Al [mg/kg]0 - 200

200 - 400

400 - 600

600 - 800

800 - 1,000

1,000 - 5,000

Erroneous Pixels

Water

Vegetation

0 10 205 Kms

Ca [mg/kg]0 - 1,000

1,000 - 2,000

2,000 - 3,000

3,000 - 4,000

4,000 - 5,000

5,000 - 6,000

6,000 - 7,000

7,000 - 8,000

8,000 - 15,000

Erroneous Pixels

Water

Vegetation

0 10 205 Kms

K [mg/kg]0 - 200

200 - 400

400 - 600

600 - 800

800 - 1,000

1,000 - 5,000

Erroneous Pixels

Water

Vegetation

Historic meander paths of the Mississippi River

Historic meander paths of the Mississippi River

Historic meander paths of the Mississippi River

Historic meander paths of the Mississippi River

Page 5: ImagingSpectroscopyforCharacterizingSoil ......cl ay silty cl ay sand y cl ay cl ay loamsilty sand y cl ay loam loam silty loam sand y loam loam y sandsilt sand 10 20 30 40 50 60 70

Effects  of  Spa2al  Resolu2on  on  Predic2on  of  Soil  Cons2tuents  

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0 500 1000 1500 2000Mg [mg/kg]

Are

a [

sq

.m]

Resolution

7.6m

10 m

15.2m

20 m

30.4 m

45 m

60.8 m

90 m

2-­‐dimensional  Gaussian  func2on  was  used  as  the  PSF  and  was  used  to  design  the  kernel  used  for  convolu2on  of  the  images  for  upscaling.  

The  full  width  at  half  maximum  (FWHM)  of  the  kernel  is  taken  to  be  the  spa2al  resolu2on  of  upscaled  images  and  specially  of  HyspIRI  

Convolve  and  Resample  Resolu/on  

Kernel  Size  

15.2  m   5  x  5  

30.4  m   11  x  11  

60.8  m  (HyspIRI  resolu2on)  

21  x  21  

Page 6: ImagingSpectroscopyforCharacterizingSoil ......cl ay silty cl ay sand y cl ay cl ay loamsilty sand y cl ay loam loam silty loam sand y loam loam y sandsilt sand 10 20 30 40 50 60 70

Effects  of  Spa2al  Resolu2on  on  Predic2on  of  Soil  Cons2tuents  

0

40

80

120

15.2m (Gaussian) 30.4m (Gaussian) 60.8m (Gaussian)

Scale

Cla

y [%

]

PropertyModel Values

Spatial Median

2.5

5.0

7.5

10.0

15.2m (Gaussian) 30.4m (Gaussian) 60.8m (Gaussian)

Scale

∆[%

]

● ●

0

5

10

15

20

15.2m (Gaussian) 30.4m (Gaussian) 60.8m (Gaussian)

Scale

SO

M [%

]

PropertyModel Values

Spatial Median

2.0

2.5

3.0

3.5

15.2m (Gaussian) 30.4m (Gaussian) 60.8m (Gaussian)

Scale

∆[%

]

●●

●●

0

1000

2000

3000

4000

15.2m (Gaussian) 30.4m (Gaussian) 60.8m (Gaussian)

Scale

Mg [m

g/k

g]

PropertyModel Values

Spatial Median

7.5

10.0

12.5

15.0

17.5

15.2m (Gaussian) 30.4m (Gaussian) 60.8m (Gaussian)

Scale

∆[%

]

0

500

1000

1500

2000

2500

15.2m (Gaussian) 30.4m (Gaussian) 60.8m (Gaussian)

Scale

Fe [m

g/k

g]

PropertyModel Values

Spatial Median

5

6

7

8

15.2m (Gaussian) 30.4m (Gaussian) 60.8m (Gaussian)

Scale

∆[%

]

2 11 20 29 38 48 56 64 76 86 94

05

01

00

15

0

Gauss60.8m

Sample Number

Cla

y [%

]

●●

●●

●●

●●

●●

●●

●●

●●

●●●●

●●

●●

●●●

●●

●●

2 11 20 29 38 48 56 64 76 86 94

05

1015

2025

Gauss60.8m

Sample Number

SO

M [%

]

●●

●●

●●

●●

●●

●●

●●

●●

●●

●●

●●

●●

●●●●●

●●●●

●●

●●

●●●

2 11 20 29 38 48 56 64 76 86 94

0500

1000

1500

2000

2500

3000

Gauss60.8m

Sample Number

Mg [m

g/k

g]

●●

●●

●●●

●●

●●

●●

●●

●●

●●●●

●●

●●

●●

●●●

2 11 20 29 38 48 56 64 76 86 94

050

010

0015

0020

0025

0030

00

Gauss60.8m

Sample Number

Fe

[mg/

kg]

●●

●●●

●●

●●

●●

●●

●●

●●●●●

●●

●●●●

●●●●●

●●

●●●●

●●

●●●

●●●

●●

●●

●●

●●

●●●

158.76193.4 200.57

1e+03

1e+05

15.2m (Gaussian) 30.4m (Gaussian) 60.8m (Gaussian)Scale

With

in P

ixel

Var

ianc

e Cl

ay [

sq. %

]

●●

1.692.11

2.71

1

100

15.2m (Gaussian) 30.4m (Gaussian) 60.8m (Gaussian)Scale

With

in P

ixel

Var

ianc

e SO

M [

sq. %

]

●●

●●

38983.6353163.86 59270.66

1e+05

1e+07

15.2m (Gaussian) 30.4m (Gaussian) 60.8m (Gaussian)Scale

With

in P

ixel

Var

ianc

e M

g [ s

q. m

g/kg

]

●●

●●

49531.15 52872.2362898.28

1e+04

1e+06

15.2m (Gaussian) 30.4m (Gaussian) 60.8m (Gaussian)Scale

With

in P

ixel

Var

ianc

e Fe

[ sq

. mg/

kg]

Page 7: ImagingSpectroscopyforCharacterizingSoil ......cl ay silty cl ay sand y cl ay cl ay loamsilty sand y cl ay loam loam silty loam sand y loam loam y sandsilt sand 10 20 30 40 50 60 70

Effects  of  Spa2al  Resolu2on  on  Predic2on  of  Soil  Cons2tuents  

0 8 164 Kms

Clay (60.8m) [%]0 - 20

20 - 40

40 - 60

60 - 80

80 - 100

Erroneous Values

Water

Vegetation

0 8 164 Kms

Clay (7.6m) [%]0 - 20

20 - 40

40 - 60

60 - 80

80 - 100

Erroneous Pixels

Water

Vegetation

0 8 164 Kms

Mg (7.6m) [mg/kg]0 - 200

200 - 400

400 - 600

600 - 800

800 - 1,000

1,000 - 1,200

1,200 - 1,400

1,400 - 1,600

1,600 - 1,800

1,800 - 2,000

2,000 - 5,000

Erroneous Values

Water

Vegetation

0 8 164 Kms

Mg (60.8m) [mg/kg]0 - 200

200 - 400

400 - 600

600 - 800

800 - 1000

1000 - 1200

1200 - 1400

1400 - 1600

1600 - 1800

1800 - 2000

2000 - 5000

Erroneous Values

Water

Vegetation

0 1 20.5 Kmsµ 0 1 20.5 Kmsµ

0 1 20.5 Kmsµ 0 1 20.5 Kmsµ

(a) (b)

(c) (d)

Page 8: ImagingSpectroscopyforCharacterizingSoil ......cl ay silty cl ay sand y cl ay cl ay loamsilty sand y cl ay loam loam silty loam sand y loam loam y sandsilt sand 10 20 30 40 50 60 70

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