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Volume 42 Issue 6
Jun.  2016
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Journal of Beijing University of Aeronautics and Astronautics  > 2016  >  42(6): 1134-1141.
YANG Lei, WU Qiulan, ZHANG Bo, et al. SVRM-assisted soil moisture retrieval method using reflected signal from BeiDou GEO satellites[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(6): 1134-1141. doi: 10.13700/j.bh.1001-5965.2015.0656(in Chinese)
Citation: YANG Lei, WU Qiulan, ZHANG Bo, et al. SVRM-assisted soil moisture retrieval method using reflected signal from BeiDou GEO satellites[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(6): 1134-1141. doi: 10.13700/j.bh.1001-5965.2015.0656(in Chinese)
shu

SVRM-assisted soil moisture retrieval method using reflected signal from BeiDou GEO satellites

doi: 10.13700/j.bh.1001-5965.2015.0656
  • 1.

    School of Information Science and Engineering, Shandong Agricultural University, Taian 271019, China

  • 2. School of Electronic and Information Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China

  • Received Date: 12 Oct 2015
  • Publish Date: 20 Jun 2016
    Abstract
  • We propose a support vector regression machine (SVRM)-assisted soil moisture retrieval method using the reflected signal from BeiDou geosynchronous orbit (GEO) satellites. This method uses a right hand circular polarization (RHCP) antenna and a left hand circular polarization (LHCP) antenna to gain the direct and reflected signal's power data from the BeiDou GEO satellites, respectively. Furthermore, it uses the direct and reflected signal power, BeiDou GEO satellites' elevation angle and azimuth angle as the input features and uses the soil moisture data which is obtained by oven-drying method as the output target of the ε-SVRM which uses a radial basis function (RBF) kernel function. The collected data is separated into two sets randomly:one as training set and the other as test set. The test results show that the error between retrieval model's prediction and the value of oven-drying method is less than 3%; the regression coefficient of determination is 0.897 9; the root mean square error (RMSE) is 1.492 6%, which proves that this method has good generalization ability and the practical results meet the application requirement.

     

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