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

Noemi Vergopolan, Nathaniel W. Chaney, Ming Pan, Justin Sheffield, Hylke E. Beck, Craig R. Ferguson, Laura Torres-Rojas, Sara Sadri, Eric F. Wood

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

Abstract

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.
Original languageEnglish (US)
JournalScientific data
Volume8
Issue number1
DOIs
StatePublished - Dec 1 2021
Externally publishedYes

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Generated from Scopus record by KAUST IRTS on 2023-02-14

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