SMAP-HydroBlocks, a 30-m satellite-based soil moisture dataset for the conterminous US
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 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.
Vergopolan, Noemi
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Chaney, Nathaniel W.
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Pan, Ming
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Sheffield, Justin
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Beck, Hylke E.
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Ferguson, Craig R.
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Torres-Rojas, Laura
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Sadri, Sara
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Wood, Eric F.
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Vergopolan, Noemi
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Chaney, Nathaniel W.
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Pan, Ming
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Sheffield, Justin
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Beck, Hylke E.
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Ferguson, Craig R.
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Torres-Rojas, Laura
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Sadri, Sara
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Wood, Eric F.
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Vergopolan, Noemi, Chaney, Nathaniel W., Pan, Ming, Sheffield, Justin, Beck, Hylke E., Ferguson, Craig R., Torres-Rojas, Laura, Sadri, Sara and Wood, Eric F.
(2021)
SMAP-HydroBlocks, a 30-m satellite-based soil moisture dataset for the conterminous US.
Scientific Data, 8 (1), [264].
(doi:10.1038/s41597-021-01050-2).
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.
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s41597-021-01050-2
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e-pub ahead of print date: 11 October 2021
Additional Information:
Funding Information:
We thank the researchers and funding agencies that maintain the open-source in-situ soil moisture networks, and the researchers from the SMAP Science Team that shared data from the SMAP core calibration/validation sites. New York State Mesonet data access was made possible through funding from NOAA grant NA19OAR4310368. This work was supported by NASA Soil Moisture Cal/Val Activities as a SMAP Mission Science Team Member (grant number NNX14AH92G); by the “Modernizing Observation Operator and Error Assessment for Assimilating In-situ and Remotely Sensed Snow/Soil Moisture Measurements into NWM” project from NOAA (grant number NA19OAR4590199); and the High Meadows Environmental Institute at Princeton University through the Mary and Randall Hack ‘69 Research Fund Award.
Publisher Copyright:
© 2021, The Author(s).
Identifiers
Local EPrints ID: 470143
URI: http://eprints.soton.ac.uk/id/eprint/470143
ISSN: 2052-4463
PURE UUID: 2f24df0f-bb0e-47c0-8767-28a841201088
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Date deposited: 04 Oct 2022 16:35
Last modified: 18 Mar 2024 03:33
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Contributors
Author:
Noemi Vergopolan
Author:
Nathaniel W. Chaney
Author:
Ming Pan
Author:
Hylke E. Beck
Author:
Craig R. Ferguson
Author:
Laura Torres-Rojas
Author:
Sara Sadri
Author:
Eric F. Wood
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