Abstract
This Soil moisture is an important part of the surface water cycle. Effective monitoring of soil moisture is of great significance for weather forecasting, flood forecasting and crop growth. Existing soil moisture monitoring methods (such as drying weighing, remote sensing observation, hygrometer measurement, etc.) have high cost, low spatial and temporal resolution, damage to observation objects, time-consuming and laborious, and long repeated observation periods. With the rapid development of GNSS remote sensing, the GPS signal based on microwave L-band is used for soil moisture monitoring with the advantages of low cost, high time resolution, strong real-time and high automation, which has attracted the attention of many scholars. This paper intends to study the soil moisture inversion algorithm based on GPS signal-to-noise ratio. Firstly, based on the basic principle, the inversion process is given. The fitting phase is zero-processed and the fusion is performed according to the correlation coefficient. Finally, a linear model of humidity is established. The inversion of the PBO plan station data was carried out, and the soil moisture data provided by PBO was compared. The results confirmed that the accuracy and stability of soil moisture inversion using GPS signal-to-noise ratio were improved by zero processing and weighted fusion.
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