A novel global grid model for soil moisture retrieval considering geographical disparity in spaceborne GNSS-R

Spaceborne global navigation satellite system-reflectometry has become an effective technique for Soil Moisture (SM) retrieval. However, the accuracy of global SM retrieval using a single model is limited due to the complexity of land surface. Introducing redundant ancillary data may also result in over-reliance problems. Therefore, we propose a method for SM retrieval that considers geographical disparities using the data from Cyclone GNSS (CYGNSS) observations and Soil Moisture Active and Passive (SMAP) product. Based on the CYGNSS effective reflectivity and ancillary datasets of SMAP, we establish five models for each grid with different parameters to achieve global SM retrieval. Subsequently, an optimal model, determined by the performance indicator, is used for SM retrieval. The results show that the root mean square error \(S_{\mathrm{RMSE}}\) $S_{\mathrm{RMSE}}$ with the improved method is decreased by 9.1% using SMAP SM as reference with the \(S_{\mathrm{RMSE}}\) $S_{\mathrm{RMSE}}$  = 0.040 cm³/cm³ compared with using single reflectivity-temperature-vegetation method. Additionally, using the in-situ SM of International Soil Moisture Network as reference, the overall correlation coefficient \(R\) $R$ and \(S_{\mathrm{RMSE}}\) $S_{\mathrm{RMSE}}$ values with the improved method are 0.80 and 0.064 cm³/cm³, respectively. The average \(R\) $R$ of the chosen sites is increased by 22.7%, and the average \(S_{\mathrm{RMSE}}\) $S_{\mathrm{RMSE}}$ is decreased by 8.7%. The results indicate that the improved method can better retrieve SM in both global and local scales without redundant auxiliary data.