ArticleScientific data2021
SMAP-HydroBlocks, a 30-m satellite-based soil moisture dataset for the conterminous US.
Article in Scientific data, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 86 citations in OpenAlex.
- A global 400-m high-resolution soil moisture dataset derived from multi-sensor remote sensing observations.Scientific data · 2025Article
- Functional Traits From Imaging Spectroscopy Inform Patterns of Forest Mortality During Sierra Nevada Drought.Global change biology · 2025Article
- A unified ensemble soil moisture dataset across the continental United States.Scientific data · 2025Article
- Open Soil Spectral Library (OSSL): Building reproducible soil calibration models through open development and community engagement.PloS one · 2025Article
- Field Testing of Gamma-Spectroscopy Method for Soil Water Content Estimation in an Agricultural Field.Sensors (Basel, Switzerland) · 2024Article
- Dynamic geospatial modeling of mycotoxin contamination of corn in Illinois: unveiling critical factors and predictive insights with machine learning.Frontiers in microbiology · 2023Article
- Estimating soil moisture content under grassland with hyperspectral data using radiative transfer modelling and machine learning.International journal of applied earth observation and geoinformation : ITC journal · 2022Article
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Authors and funding
9 authors at 7 institutions in 4 countries.
Funding
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.