A Novel Research for Radiometer Assisted Ground-based GNSS-R Soil Moisture Inversion
Soil moisture monitoring based on Global Navigation Satellite System-Reflection(GNSS-R)makes up for the shortcomings of traditional measurement methods,and has become a hot research topic in the field of remote sensing in recent years.In view of the influence of soil roughness and vegetation moisture content on the inversion accuracy,we studied the method of improving the accuracy by using radiometer data and proposed the soil moisture inversion model based on GNSS-R and radiometer data fusion with the neural network of nonlinear autoregressive model(NARX).The method is verified by field experiments through the general flow of signal processing.The results show that,compared with traditional GNSS-R method,the correlation coefficient of the proposed inversion method is increased by 77%,the root mean square error is decreased by 78%,and the correlation coefficient is increased by 47%compared with the radiometer method,the root mean square error decreased by 68%.We proves that the proposed method can achieve long-term continuous observation of soil moisture in a fixed area.
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