SMAP-HydroBlocks, a 30-m satellite-based soil moisture dataset for the conterminous US

Soil moisture plays a key role in controlling land-atmosphere interactions, with implications for water resources, agriculture, climate, and ecosystem dynamics. Although soil moisture varies strongly across the landscape, current monitoring capabilities are limited to coarse-scale satellite retrieva...

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Bibliographic Details
Published in:Scientific data 2021-10, Vol.8 (1), p.264-264, Article 264
Main Authors: Vergopolan, Noemi, Chaney, Nathaniel W., Pan, Ming, Sheffield, Justin, Beck, Hylke E., Ferguson, Craig R., Torres-Rojas, Laura, Sadri, Sara, Wood, Eric F.
Format: Article
Language:English
Subjects:
704/172
704/242
Agricultural ecosystems
Computer applications
Data Descriptor
Datasets
Ecosystem dynamics
Humanities and Social Sciences
Learning algorithms
Machine learning
multidisciplinary
Radiative transfer
Science
Science (multidisciplinary)
Soil moisture
Transfer learning
Water resources
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Summary:Soil moisture plays a key role in controlling land-atmosphere interactions, with implications for water resources, agriculture, climate, and ecosystem dynamics. Although soil moisture varies strongly across the landscape, current monitoring capabilities are limited to coarse-scale satellite retrievals and a few regional in-situ networks. Here, we introduce SMAP-HydroBlocks (SMAP-HB), a high-resolution satellite-based surface soil moisture dataset at an unprecedented 30-m resolution (2015–2019) across the conterminous United States. SMAP-HB was produced by using a scalable cluster-based merging scheme that combines high-resolution land surface modeling, radiative transfer modeling, machine learning, SMAP satellite microwave data, and in-situ observations. We evaluated the resulting dataset over 1,192 observational sites. SMAP-HB performed substantially better than the current state-of-the-art SMAP products, showing a median temporal correlation of 0.73 ± 0.13 and a median Kling-Gupta Efficiency of 0.52 ± 0.20. The largest benefit of SMAP-HB is, however, the high spatial detail and improved representation of the soil moisture spatial variability and spatial accuracy with respect to SMAP products. The SMAP-HB dataset is available via zenodo and at https://waterai.earth/smaphb . Measurement(s) wetness of soil Technology Type(s) computational modeling technique Factor Type(s) geographic location • temporal interval Sample Characteristic - Environment land • surface soil Sample Characteristic - Location contiguous United States of America Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.14582265
ISSN:2052-4463
2052-4463
DOI:10.1038/s41597-021-01050-2