The important role of reliable land surface model simulation in high-resolution multi-source soil moisture data fusion by machine learning

While machine learning (ML) is used to correct gridded soil moisture (SM) products by fusing in-situ observations, the contribution of land surface model (LSM)- and satellite-based SM products are yet to be validated over a large area, leading to imprudent adoption of SM product(s) for data fusion. In this study, single or multiple SM products from CSSPv2 LSM simulation, ERA5 and GLDASv2.1 reanalysis, and ESA-CCI satellite data with different resolutions are used to train ML models and generate daily SM estimates at 0.0625° resolution with in-situ measurements as target and relevant variables as auxiliary. Three widely used ML methods, namely Random Forest (RF), LightGBM, and XGBoost, were compared. Validations over independent in-situ stations during 2012–2017 showed the improvement of fusion products against their corresponding raw products, with KGE and CC increased by 87 % and 6 %, and RMSE decreased by 22 % for SM at surface and rootzone layers. Regionally, ML-based SM estimates improve mainly in southeast China. Merging three coarse-resolution SM datasets (i.e., ERA5, GLDASv2.1 and ESA-CCI) together with in-situ observations further increases KGE and CC by 15 % and 5 % against individual fusion products, but it still fails to outperform the individual high-resolution fusion product of ML/CSSPv2. Merging all four gridded SM products with in-situ data shows advantage against the ML/CSSPv2, with KGE and CC increased by 9 % and 7 %. The results are consistent by using different ML methods. This study suggests the importance of high-resolution LSM for SM data fusion, even with the emergence of ML approaches.