Improvement of CYGNSS soil moisture retrieval model considering water and surface temperature

Abstract

The key to retrieving soil moisture (SM) with spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) data is to correct the effects of soil surface temperature (SST), roughness, water, and vegetation. In this study, Cyclone Global Navigation Satellite System (CYGNSS) data are used to calculate surface reflectivity and surface roughness is characterized based on the statistical moments of Delay-Doppler Maps (DDM). After removing open water, a multiple linear regression model is created to retrieve SM by combining the SST and vegetation optical depth (VOD) parameters provided by Soil Moisture Active Passive (SMAP) data. With a correlation coefficient of 0.815 and a root-mean-square error of 0.066 cm3cm−3, an experimental analysis reveals decent consistency between the CYGNSS SM and the referenced SMAP SM. In addition, a time-series analysis between the CYGNSS SM and the International Soil Moisture Network (ISMN) referenced SM data shows a good correlation. Since surface reflectivity is significantly affected by water and because there is a coupling relationship between SST and SM, the differences of CYGNSS SM when the two factors are simultaneously considered or ignored are also analyzed. The experimental results show that after removing water and incorporating SST into the linear regression model, the accuracy of CYGNSS SM has been improved significantly, with the root mean square error, mean absolute error, and Bias increasing by 6%, 7%, and 11.3%, respectively. This study demonstrates the necessity of considering water and SST in SM retrieval and provides a novel approach for SM retrieval using high accuracy and high spatial and temporal resolution data.