A Time-Constrained and Spatially Explicit AI Model for Soil Moisture Inversion Using CYGNSS Data

Current research often improves the accuracy of global navigation satellite system-reflectometry soil moisture (SM) inversion by incorporating auxiliary data, which somewhat limits its potential for practical application. To reduce the reliance on auxiliary data, this article presents a cyclone global navigation satellite system SM inversion method based on the time-constrained and spatially explicit artificial intelligence (TCSE-AI) model. The method initially segments data into multiple subsets through time constraints, thus limiting irrelevant factors to a relatively stable state and endowing the data with temporal attributes. Then, it incorporates raster data spatial information, integrating the potential spatiotemporal distribution characteristics of the data into the SM inversion model. Finally, it constructs SM inversion models using machine learning methods. The experimental results indicate that the TCSE-AI SM inversion model based on the XGBoost and random forest model architectures achieved favorable results. Their monthly SM inversion results for 2022 were compared with the soil moisture active passive (SMAP) products, with Pearson's correlation coefficients (R) all greater than 0.91 and root-mean-square errors (RMSEs) less than 0.05 cm³/cm³. Subsequently, this study used the XGBoost method as an example for validation with in situ data and conducted an interannual SM cross-inversion experiment. From January to June 2022, the R between SM inversion results in the study area and in situ SM was 0.788, with an RMSE of 0.063 cm³/cm³. The interannual cross-inversion experimental results, except for cases of missing data over multiple days, indicate that the TCSE-AI model generally achieved the accurate estimates of SM. Compared with SMAP SM, the R was all greater than 0.8, with a maximum RMSE of 0.072 cm³/cm³, and they showed satisfactory consistency with the in situ data.