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Support Vector Regression for Retrieval of Soil Moisture Using Bistatic Scatterometer Data at X-Band

Authors: Dileep Kumar Gupta, Rajendra Prasad, Pradeep Kumar, Varun Narayan Mishra, Ajeet Kumar Vishwakarma, Prashant Kumar Srivastava


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.

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[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.