Investigating the Effects of Meteorological Data Rainfall and Temperature on GNSS-R Soil Moisture Inversion

Soil moisture is one of the critical variables in maintaining the global water cycle balance. Moreover, it plays an essential role in climate change, crop growth, and environmental disaster event monitoring, and continuous monitoring of soil moisture is of great significance. The inversion of soil moisture detection technique using Global Navigation Satellite System Satellite-based Reflectance Signal (GNSS-R) to obtain high accuracy soil moisture is a hot topic of current research. To address the recent study, it only considers a limited number of variables related to the Cyclone Global Navigation Satellite System (CYGNSS). Still, it does not consider the effect of real-time variables rainfall and temperature on CYGNSS. To this end, this paper uses the GA-BP neural network model to obtain soil moisture by combining soil moisture data from ground stations with CYGNSS data-related variables, surface environmental data, rainfall, and air temperature. Analysis of the effect of meteorological data rainfall and temperature on the inversion of soil moisture. The experimental results show that the GA-BP neural network model with rainfall and temperature can better describe the correlation between multi-source variables and soil moisture with R of 0.9821 and RMSE of 0.0206 cm
³
/cm
³
. Rainfall and temperature have contributed benefits to GNSS-R soil moisture inversion.