INVERSION OF SOIL MOISTURE BY WAVELET ANALYSIS AND MULTI-STAR FUSION

Abstract

Soil moisture content is an important parameter in hydrology, meteorology and agriculture, and it is of great significance for meteorological forecast, flood disaster and water resource cycle. Global Positioning System Interferometric Reflectometry (GPS-IR) is a new remote sensing technique with low cost, high efficiency and high resolution.. Using GPS-IR to invert soil moisture, snow depth, sea level and other aspects compared with traditional measurements, greatly reducing the difficulty of measurement and improving the accuracy of estimation. In view of how to efficiently and accurately separate satellite reflection signals and invert soil moisture, this paper considers the time-rate characteristics of wavelet analysis and the advantages of multi-star fusion. This paper proposes a combination of wavelet analysis and multi-star fusion estimation model for the existing research and analysis. First, the GPS satellite straight and reflected signals are separated by wavelet analysis, and then the relative delay phase is obtained by the sine fitting model. The multi-satellite relative delay phase is effectively analyzed and selected by a linear regression model. Finally, the change in soil moisture is estimated by establishing a multi-star fusion model. The feasibility and effectiveness of using wavelet analysis and multiple GPS satellites to estimate soil moisture were compared and analyzed.Take the monitoring data provided by the PBO of the US Sector Boundary Observation Program as an example. Comparative analysis of the feasibility and effectiveness of soil moisture estimation using single or multiple GPS satellites.The results of the two data show that the linear regression equation can better describe the relationship between relative delay phase and soil moisture.Wavelet analysis fully exploits the performance of the identification trend term in the process of satellite reflection signal separation.Using a single satellite for soil moisture inversion, it is difficult to accurately grasp the variation law of soil moisture, and the error of inversion error fluctuates greatly, which is prone to jump phenomenon.Multiple satellites can effectively combine the advantages of each satellite and effectively suppress abnormal jumps.The method of this paper gives full play to the advantages of multiple linear regression models.This model effectively suppresses the transition phenomenon. It combines wavelet analysis and multi-star fusion to effectively suppress abnormal jump values. The estimation error is stable and the inversion accuracy is improved, which provides soil moisture monitoring. An accurate and efficient method.This not only ensures the stability of local errors during the separation of satellite reflected signals, but also effectively suppresses abnormal jumps when estimating a single satellite. A single satellite does not easily affect the inversion process. Therefore, soil moisture estimation can be feasible and effective as a linear event estimate.