Estimating Vegetation Surface Parameters From Simulated SMAP Microwave Data Using LS-SVM

In this paper, we explore the use of the Least Squares Support Vector Machines (LS-SVM) technique for modeling vegetation surface parameters inversion. The methodology has been applied to two sets of data to retrieve soil moisture content and vegetation water content from simulated microwave data. Based on the SMAP mission, we simulated the backscattering coefficients and the brightness temperature of vegetation surface by combining active and passive sensor at the frequencies of 1.26 and 1.41GHz, respectively, at incidence angles of 30, 40 degree. To assess the effectiveness of the method, the Gaussian distributed noise are added. A set of preliminary numerical results is provided in order to assess the performance of the proposed technique. This study demonstrates the great potential of LS-SVM in the estimation of soil moisture from microwave remotely sensed data.