Soil Moisture Retrieval Based on Satellite-Borne GNSS-R Technology

Abstract

The global water cycle is affected by Soil moisture which plays an important role in the fields of hydrology and agriculture. Therefore, it is of great significance to study the theory and method of soil moisture detection. Currently, soil moisture detection methods are very rich. As an emerging remote sensing technology, satellite-borne GNSS-R has the advantages of abundant signal sources, low cost and Global coverage, etc. It has attracted people's attention to use satellite-borne GNSS-R technology to retrieve soil moisture. In this paper, a method of soil moisture retrieval based on satellite-borne GNSS-R is proposed. Soil moisture was constructed as a function of surface reflectivity, vegetation optical depth, roughness coefficient and temperature which are extracted from CYGNSS data and SMAP data. Then we use neural network model training data to determine the mathematical model of soil moisture retrieval. African soil moisture status was obtained using CYGNSS data from July to December 2018. Finally, the soil moisture retrieval method proposed in this paper was evaluated by comparing with the soil moisture data provided by SMAP. The results show that the soil moisture retrieval method proposed in this paper has a good consistency with SMAP soil moisture. This shows that GNSS reflection signals collected by CYGNSS project can be used to retrieve soil moisture and satellite-borne GNSS-R technology has great potential to obtain soil moisture with high precision and high spatial and temporal resolution. And it also provides a new method for soil moisture retrieval.