A 3-hour, 1-km surface soil moisture dataset for the contiguous United States from 2015 to 2023

Abstract. Surface soil moisture (SSM) is a critical variable for understanding the terrestrial hydrologic cycle, and it influences ecosystem dynamics, agriculture productivity, and water resource management. Although SSM information is widely estimated through satellite-derived and model-assimilated methods, datasets with fine spatio-temporal resolutions remain unavailable at the continental scale, yet are essential for improving weather forecasting, optimizing precision irrigation, and enhancing fire risk assessment. In this study, we developed a new 3-hour, 1-km spatially seamless SSM dataset spanning 2015 to 2023, covering the entire contiguous United States (CONUS), using a spatio-temporal fusion model. This approach effectively combines 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 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. To overcome the spatio-temporal mismatch between the two products, we developed a time-series data mining approach known as the highly comparative time-series analysis (HCTSA) method to extract multiple spatially seamless characteristics (e.g., maximum and mean) from the two inter-annual SSM datasets (i.e., SMAP and Crop-CASMA). Then the fusion model was constructed using the extracted 9-km and 1-km characteristics and each scene of the SMAP, in turn. Finally, the 3-hour, 1-km SSM data (named as STF_SSM) were predicted from 2015 to 2023. The comparison with in-situ data from multiple SSM observation networks showed that the performance of our 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. The STF_SSM dataset in this study is the first long time-series, spatially seamless SSM dataset to realize continuous intra-day 1-km SSM observations every 3 hours across the CONUS, which provides a new insight into the fast changes in soil moisture along with drought and wet spell occurrences, and ecosystem responses. Additionally, this dataset provides a valuable data source for the calibration and validation of land surface models. The STF_SSM dataset is available at https://doi.org/10.6084/m9.figshare.28188011 (Yang et al., 2025).