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

文献类型： 外文期刊

第一作者： Yang, Haoxuan

作者： Yang, Haoxuan;Yang, Jia;Zou, Chris B.;Ochsner, Tyson E.;Krueger, Erik S.;Xu, Mengyuan

作者机构：

期刊名称：EARTH SYSTEM SCIENCE DATA （ 影响因子：11.6； 五年影响因子：13.9 ）

ISSN： 1866-3508

年卷期： 2025 年 17 卷 7 期

页码：

收录情况： SCI

摘要： 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 and yet are essential for improving weather forecasting, optimizing precision irrigation, and enhancing fire risk assessment. In this study, we developed a new 3 h, 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 h 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 and 1 km characteristics and each scene of the SMAP in turn. Finally, the 3 h, 1 km SSM data (named 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 a mean correlation coefficient (CC) of 0.716 at the daily scale and 0.689 at the 3 h 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 h 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/10.5066/P13CCN69 (Yang et al., 2025a).

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