Reconstruction of a spatially seamless, daily SMAP (SSD_SMAP) surface soil moisture dataset from 2015 to 2021

Surface soil moisture (SSM) is a vital component in terrestrial hydrological processes. As a type of important microwave remote sensing-based SSM dataset, the Soil Moisture Active Passive (SMAP) SSM data were available since 2015 and have been applied to many studies. However, the spatial gaps in the SMAP SSM data affect greatly its applicability. To address this issue, this study developed a method to fill the spatial gaps in the SMAP SSM data, generating a spatially seamless, daily SMAP (SSD_SMAP) SSM dataset from 2015 to 2021. The method makes full use of the long SMAP SSM time-series data with inherent spatial gaps. To deal with the continuously changed spatial gaps in the daily SMAP data, we used a highly comparative time-series analysis (HCTSA) to dig the temporal profiles of the SMAP SSM time-series, producing 17 seamless HCTSA-based time-series characteristics (HTCs). Both spatially seamless topography characteristics (TCs) and location characteristics (LCs) were also considered. Moreover, a random forest (RF) model was trained to construct the relation between the valid daily SMAP data at the prediction time and the seamless auxiliary data (i.e., 17 HTCs coupled with TCs and LCs). Subsequently, the constructed RF model was migrated to fill the spatial gaps of the SMAP data at the corresponding time. The proposed method was validated in both simulated gaps (real data as reference) and real gaps (in-situ SSM data as reference). The produced SSD_SMAP dataset was also compared with the model-based Global Land Evaporation Amsterdam Model (GLEAM) dataset. The results indicated that the SSD_SMAP data are closer to the in-situ data, with an unbiased root mean square error (ubRMSE) of ∼ 0.040 m³/m³. The SSD_SMAP dataset can provide data support for global and regional research in related fields.