A 3-hour, 1-km surface soil moisture dataset in Continental United States

We simulated a 3-hour, 1-km spatially seamless SSM dataset (called STF_SSM) in the Continental United States (CONUS) using a virtual image pair-based spatio-temporal fusion method. This proposed approach effectively fuses the distinct advantages of two long-term SSM datasets, namely, the Soil Moisture Active Passive (SMAP) L4 SSM product and the Crop Condition and Soil Moisture Analytics (Crop-CASMA) dataset. The SMAP L4 product provides spatially seamless SSM observations with a 3-hour temporal resolution but at a 9-km spatial resolution, while the Crop-CASMA SSM dataset offers a finer spatial resolution of 1 km but has a daily temporal resolution and contains spatial gaps. By referring to the ground-based in-situ data, the performance of the STF_SSM dataset is better than the Crop-CASMA and is close to the SMAP L4 product, with mean correlation coefficients (CC) of 0.716 at the daily scale and 0.689 at the 3-hour scale. This dataset provides a critical data source for the calibration and validation of land surface models.