| Citation: | SUN Bo, LIANG Yong, HAN Mutian, et al. GNSS-IR soil moisture inversion method based on GA-SVM[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(3): 486-492. doi: 10.13700/j.bh.1001-5965.2018.0417(in Chinese)
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In order to improve the precision of soil moisture measurement in a wide range, in this paper, the global navigation satellite system interferometry and reflectometry (GNSS-IR) for soil moisture was studied and a soil moisture inversion model based on support vector machine (SVM) was proposed. In this model, the automatic optimizing function of genetic algorithm (GA) was applied to optimize the parameters of SVM. The results show that the mean absolute percentage error (MAPE), the maximum relative error (MRE) and the coefficient of determination for equation of linear regression are 0.69%, 1.22% and 0.9569 respectively between the soil moisture inverted by the proposed GA-SVM model and the ground measured values. In addition, the performance of GA-SVM model was also compared with the statistical regression, particle swarm optimization SVM model (PSO-SVM) and back propagation (BP) neural network. The comparison results show that the GA-SVM method is more suitable for the GNSS-IR soil moisture inversion than other machine learning algorithms in small training set scenario, and it has higher inversion precision and better generalization performance.
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