Support Vector Regression for Retrieval of Soil Moisture Using Bistatic Scatterometer Data at X-Band
An approach was evaluated for the retrieval of soil
moisture of bare soil surface using bistatic scatterometer data in the
angular range of 200 to 700 at VV- and HH- polarization. The
microwave data was acquired by specially designed X-band (10
GHz) bistatic scatterometer. The linear regression analysis was done
between scattering coefficients and soil moisture content to select the
suitable incidence angle for retrieval of soil moisture content. The 250
incidence angle was found more suitable. The support vector
regression analysis was used to approximate the function described
by the input output relationship between the scattering coefficient and
corresponding measured values of the soil moisture content. The
performance of support vector regression algorithm was evaluated by
comparing the observed and the estimated soil moisture content by
statistical performance indices %Bias, root mean squared error
(RMSE) and Nash-Sutcliffe Efficiency (NSE). The values of %Bias,
root mean squared error (RMSE) and Nash-Sutcliffe Efficiency
(NSE) were found 2.9451, 1.0986 and 0.9214 respectively at HHpolarization.
At VV- polarization, the values of %Bias, root mean
squared error (RMSE) and Nash-Sutcliffe Efficiency (NSE) were
found 3.6186, 0.9373 and 0.9428 respectively.
[1] W. Cramer, A. Bondeau, F. I. Woodward, I. C. Prentice, R. A. Betts, V.
Brovkin, P. M. Cox, V. Fisher, J. A. Foley, and A. D. Friend, "Global
response of terrestrial ecosystem structure and function to CO2 and
climate change: results from six dynamic global vegetation models,"
Global change biology, vol. 7, pp. 357-373, 2001.
[2] P. K. Srivastava, D. Han, M. A. Rico-Ramirez, D. Al-Shrafany, and T.
Islam, "Data fusion techniques for improving soil moisture deficit using
SMOS satellite and WRF-NOAH land surface model," Water resources
management, vol. 27, pp. 5069-5087, 2013.
[3] B. Merz and E. J. Plate, "An analysis of the effects of spatial variability
of soil and soil moisture on runoff," Water Resources Research, vol. 33,
pp. 2909-2922, 1997.
[4] E. F. Wood, D. P. Lettenmaier, and V. G. Zartarian, "A land-surface
hydrology parameterization with subgrid variability for general
circulation models," Journal of Geophysical Research: Atmospheres,
vol. 97, pp. 2717-2728, 1992.
[5] C. Fitzjohn, J. Ternan, and A. Williams, "Soil moisture variability in a
semi-arid gully catchment: implications for runoff and erosion control,"
Catena, vol. 32, pp. 55-70, 1998.
[6] L. Wang and J. J. Qu, "Satellite remote sensing applications for surface
soil moisture monitoring: A review," Frontiers of Earth Science in
China, vol. 3, pp. 237-247, 2009.
[7] P. C. Dubois, J. Van Zyl, and T. Engman, "Measuring soil moisture with
imaging radars," IEEE Transactions on Geoscience and Remote Sensing,
vol. 33, pp. 915-926, 1995.
[8] E. G. Njoku and L. Li, "Retrieval of land surface parameters using
passive microwave measurements at 6-18 GHz," IEEE Transactions on
Geoscience and Remote Sensing, vol. 37, pp. 79-93, 1999.
[9] Y. Oh, K. Sarabandi, and F. T. Ulaby, "An empirical model and an
inversion technique for radar scattering from bare soil surfaces," IEEE
Transactions on Geoscience and Remote Sensing, vol. 30, pp. 370-381,
1992.
[10] T. Schmugge, P. E. O'Neill, and J. R. Wang, "Passive Microwave Soil
Moisture Research," IEEE Transactions on Geoscience and Remote
Sensing, vol. GE-24, pp. 12-22, 1986.
[11] J. R. Wang, P. E. O'Neill, T. J. Jackson, and E. T. Engman,
"Multifrequency measurements of the effects of soil moisture, soil
texture, and surface roughness," IEEE Transactions on Geoscience and
Remote Sensing, pp. 44-51, 1983.
[12] E. Ceraldi, G. Franceschetti, A. Iodice, and D. Riccio, "Estimating the
soil dielectric constant via scattering measurements along the specular
direction," IEEE Transactions on Geoscience and Remote Sensing vol.
43, pp. 295-305, 2005.
[13] Y. Du, F. T. Ulaby, and M. C. Dobson, "Sensitivity to soil moisture by
active and passive microwave sensors," IEEE Transactions on
Geoscience and Remote Sensing, vol. 38, pp. 105-114, 2000.
[14] K. B. Khadhra, T. Boerner, D. Hounam, and M. Chandra, "Surface
parameter estimation using bistatic polarimetric X-band measurements,"
Progress In Electromagnetics Research B, vol. 39, pp. 197-223, 2012.
[15] D. Singh, P. Mukherjee, S. Sharma, and K. Singh, "Effect of soil
moisture and crop cover in remote sensing," Advances in Space
Research, vol. 18, pp. 63-66, 1996.
[16] D. Tuia, J. Verrelst, L. Alonso, F. Pérez-Cruz, and G. Camps-Valls,
"Multioutput support vector regression for remote sensing biophysical
parameter estimation," Geoscience and Remote Sensing Letters, IEEE,
vol. 8, pp. 804-808, 2011.
[17] G. Camps-Valls, L. Bruzzone, J. L. Rojo-Álvarez, and F. Melgani,
"Robust support vector regression for biophysical variable estimation
from remotely sensed images," Geoscience and Remote Sensing Letters,
IEEE, vol. 3, pp. 339-343, 2006.
[18] V. Vapnik, S. E. Golowich, and A. J. Smola, "Support Vector Method
for Function Approximation, Regression Estimation and Signal
Processing," in Advances in Neural Information Processing Systems,
1997, pp. 281-287.
[19] A. J. Smola and B. Schölkopf, "A tutorial on support vector regression,"
Statistics and computing, vol. 14, pp. 199-222, 2004.
[20] A. Karatzoglou, A. Smola, K. Hornik, and A. Zeileis, "kernlab-an S4
package for kernel methods in R," 2004.
[1] W. Cramer, A. Bondeau, F. I. Woodward, I. C. Prentice, R. A. Betts, V.
Brovkin, P. M. Cox, V. Fisher, J. A. Foley, and A. D. Friend, "Global
response of terrestrial ecosystem structure and function to CO2 and
climate change: results from six dynamic global vegetation models,"
Global change biology, vol. 7, pp. 357-373, 2001.
[2] P. K. Srivastava, D. Han, M. A. Rico-Ramirez, D. Al-Shrafany, and T.
Islam, "Data fusion techniques for improving soil moisture deficit using
SMOS satellite and WRF-NOAH land surface model," Water resources
management, vol. 27, pp. 5069-5087, 2013.
[3] B. Merz and E. J. Plate, "An analysis of the effects of spatial variability
of soil and soil moisture on runoff," Water Resources Research, vol. 33,
pp. 2909-2922, 1997.
[4] E. F. Wood, D. P. Lettenmaier, and V. G. Zartarian, "A land-surface
hydrology parameterization with subgrid variability for general
circulation models," Journal of Geophysical Research: Atmospheres,
vol. 97, pp. 2717-2728, 1992.
[5] C. Fitzjohn, J. Ternan, and A. Williams, "Soil moisture variability in a
semi-arid gully catchment: implications for runoff and erosion control,"
Catena, vol. 32, pp. 55-70, 1998.
[6] L. Wang and J. J. Qu, "Satellite remote sensing applications for surface
soil moisture monitoring: A review," Frontiers of Earth Science in
China, vol. 3, pp. 237-247, 2009.
[7] P. C. Dubois, J. Van Zyl, and T. Engman, "Measuring soil moisture with
imaging radars," IEEE Transactions on Geoscience and Remote Sensing,
vol. 33, pp. 915-926, 1995.
[8] E. G. Njoku and L. Li, "Retrieval of land surface parameters using
passive microwave measurements at 6-18 GHz," IEEE Transactions on
Geoscience and Remote Sensing, vol. 37, pp. 79-93, 1999.
[9] Y. Oh, K. Sarabandi, and F. T. Ulaby, "An empirical model and an
inversion technique for radar scattering from bare soil surfaces," IEEE
Transactions on Geoscience and Remote Sensing, vol. 30, pp. 370-381,
1992.
[10] T. Schmugge, P. E. O'Neill, and J. R. Wang, "Passive Microwave Soil
Moisture Research," IEEE Transactions on Geoscience and Remote
Sensing, vol. GE-24, pp. 12-22, 1986.
[11] J. R. Wang, P. E. O'Neill, T. J. Jackson, and E. T. Engman,
"Multifrequency measurements of the effects of soil moisture, soil
texture, and surface roughness," IEEE Transactions on Geoscience and
Remote Sensing, pp. 44-51, 1983.
[12] E. Ceraldi, G. Franceschetti, A. Iodice, and D. Riccio, "Estimating the
soil dielectric constant via scattering measurements along the specular
direction," IEEE Transactions on Geoscience and Remote Sensing vol.
43, pp. 295-305, 2005.
[13] Y. Du, F. T. Ulaby, and M. C. Dobson, "Sensitivity to soil moisture by
active and passive microwave sensors," IEEE Transactions on
Geoscience and Remote Sensing, vol. 38, pp. 105-114, 2000.
[14] K. B. Khadhra, T. Boerner, D. Hounam, and M. Chandra, "Surface
parameter estimation using bistatic polarimetric X-band measurements,"
Progress In Electromagnetics Research B, vol. 39, pp. 197-223, 2012.
[15] D. Singh, P. Mukherjee, S. Sharma, and K. Singh, "Effect of soil
moisture and crop cover in remote sensing," Advances in Space
Research, vol. 18, pp. 63-66, 1996.
[16] D. Tuia, J. Verrelst, L. Alonso, F. Pérez-Cruz, and G. Camps-Valls,
"Multioutput support vector regression for remote sensing biophysical
parameter estimation," Geoscience and Remote Sensing Letters, IEEE,
vol. 8, pp. 804-808, 2011.
[17] G. Camps-Valls, L. Bruzzone, J. L. Rojo-Álvarez, and F. Melgani,
"Robust support vector regression for biophysical variable estimation
from remotely sensed images," Geoscience and Remote Sensing Letters,
IEEE, vol. 3, pp. 339-343, 2006.
[18] V. Vapnik, S. E. Golowich, and A. J. Smola, "Support Vector Method
for Function Approximation, Regression Estimation and Signal
Processing," in Advances in Neural Information Processing Systems,
1997, pp. 281-287.
[19] A. J. Smola and B. Schölkopf, "A tutorial on support vector regression,"
Statistics and computing, vol. 14, pp. 199-222, 2004.
[20] A. Karatzoglou, A. Smola, K. Hornik, and A. Zeileis, "kernlab-an S4
package for kernel methods in R," 2004.
@article{"International Journal of Earth, Energy and Environmental Sciences:70733", author = "Dileep Kumar Gupta and Rajendra Prasad and Pradeep Kumar and Varun Narayan Mishra and Ajeet Kumar Vishwakarma and Prashant Kumar Srivastava", title = "Support Vector Regression for Retrieval of Soil Moisture Using Bistatic Scatterometer Data at X-Band", abstract = "An approach was evaluated for the retrieval of soil
moisture of bare soil surface using bistatic scatterometer data in the
angular range of 200 to 700 at VV- and HH- polarization. The
microwave data was acquired by specially designed X-band (10
GHz) bistatic scatterometer. The linear regression analysis was done
between scattering coefficients and soil moisture content to select the
suitable incidence angle for retrieval of soil moisture content. The 250
incidence angle was found more suitable. The support vector
regression analysis was used to approximate the function described
by the input output relationship between the scattering coefficient and
corresponding measured values of the soil moisture content. The
performance of support vector regression algorithm was evaluated by
comparing the observed and the estimated soil moisture content by
statistical performance indices %Bias, root mean squared error
(RMSE) and Nash-Sutcliffe Efficiency (NSE). The values of %Bias,
root mean squared error (RMSE) and Nash-Sutcliffe Efficiency
(NSE) were found 2.9451, 1.0986 and 0.9214 respectively at HHpolarization.
At VV- polarization, the values of %Bias, root mean
squared error (RMSE) and Nash-Sutcliffe Efficiency (NSE) were
found 3.6186, 0.9373 and 0.9428 respectively.", keywords = "Bistatic scatterometer, soil moisture, support vector
regression, RMSE, %Bias, NSE.", volume = "9", number = "10", pages = "1193-4", }
