Articles | Volume 13, issue 4
https://doi.org/10.5194/essd-13-1711-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-13-1711-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Gap-free global annual soil moisture: 15 km grids for 1991–2018
Mario Guevara
Department of Plant and Soil Sciences, University of Delaware, Newark, DE, USA
present address: University of California Riverside, Environmental Sciences|USDA-ARS, U.S. Salinity Laboratory, Riverside, CA, USA
Michela Taufer
Department of Electrical Engineering and Computer Science, The
University of Tennessee, Knoxville, TN, USA
Department of Plant and Soil Sciences, University of Delaware, Newark, DE, USA
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15 citations as recorded by crossref.
- ChinaCropSM1 km: a fine 1 km daily soil moisture dataset for dryland wheat and maize across China during 1993–2018 F. Cheng et al. 10.5194/essd-15-395-2023
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- Spatiotemporal Seamless Estimation of Global Surface Soil Moisture Using Triple Collocation, Machine Learning, and Data Assimilation L. Xu et al. 10.1109/TGRS.2025.3568034
- Urban and agricultural areas under threat of the termite pest genus Heterotermes: insights from species distribution modelling and phylogeny E. Duquesne & D. Fournier 10.1007/s10340-025-01866-6
- Reconstructing long-term global satellite-based soil moisture data using deep learning method Y. Hu et al. 10.3389/feart.2023.1130853
- A methodological framework for assessing pastoral socio-ecological system vulnerability: A case study of Altay Prefecture in Central Asia Z. Yang et al. 10.1016/j.scitotenv.2022.160828
- High-resolution European daily soil moisture derived with machine learning (2003–2020) S. O et al. 10.1038/s41597-022-01785-6
- The Carbon Transfer From Plant to Soil Is More Efficient in Less Productive Ecosystems X. Fan et al. 10.1029/2023GB007727
- Reconstruction of a spatially seamless, daily SMAP (SSD_SMAP) surface soil moisture dataset from 2015 to 2021 H. Yang & Q. Wang 10.1016/j.jhydrol.2023.129579
- The International Soil Moisture Network: serving Earth system science for over a decade W. Dorigo et al. 10.5194/hess-25-5749-2021
- SGD-SM 2.0: an improved seamless global daily soil moisture long-term dataset from 2002 to 2022 Q. Zhang et al. 10.5194/essd-14-4473-2022
- The Relationships between Biomass and Soil Respiration across Different Forest Management Practices C. Hu et al. 10.3390/f15040712
- A stepwise method for downscaling SMAP soil moisture dataset in the CONUS during 2015–2019 H. Yang et al. 10.1016/j.jag.2024.103912
- Building Trust in Earth Science Findings through Data Traceability and Results Explainability P. Olaya et al. 10.1109/TPDS.2022.3220539
- Downscaling satellite soil moisture for landscape applications: A case study in Delaware, USA D. Warner et al. 10.1016/j.ejrh.2021.100946
15 citations as recorded by crossref.
- ChinaCropSM1 km: a fine 1 km daily soil moisture dataset for dryland wheat and maize across China during 1993–2018 F. Cheng et al. 10.5194/essd-15-395-2023
- Artificial intelligence achieves easy-to-adapt nonlinear global temperature reconstructions using minimal local data M. Wegmann & F. Jaume-Santero 10.1038/s43247-023-00872-9
- Spatiotemporal Seamless Estimation of Global Surface Soil Moisture Using Triple Collocation, Machine Learning, and Data Assimilation L. Xu et al. 10.1109/TGRS.2025.3568034
- Urban and agricultural areas under threat of the termite pest genus Heterotermes: insights from species distribution modelling and phylogeny E. Duquesne & D. Fournier 10.1007/s10340-025-01866-6
- Reconstructing long-term global satellite-based soil moisture data using deep learning method Y. Hu et al. 10.3389/feart.2023.1130853
- A methodological framework for assessing pastoral socio-ecological system vulnerability: A case study of Altay Prefecture in Central Asia Z. Yang et al. 10.1016/j.scitotenv.2022.160828
- High-resolution European daily soil moisture derived with machine learning (2003–2020) S. O et al. 10.1038/s41597-022-01785-6
- The Carbon Transfer From Plant to Soil Is More Efficient in Less Productive Ecosystems X. Fan et al. 10.1029/2023GB007727
- Reconstruction of a spatially seamless, daily SMAP (SSD_SMAP) surface soil moisture dataset from 2015 to 2021 H. Yang & Q. Wang 10.1016/j.jhydrol.2023.129579
- The International Soil Moisture Network: serving Earth system science for over a decade W. Dorigo et al. 10.5194/hess-25-5749-2021
- SGD-SM 2.0: an improved seamless global daily soil moisture long-term dataset from 2002 to 2022 Q. Zhang et al. 10.5194/essd-14-4473-2022
- The Relationships between Biomass and Soil Respiration across Different Forest Management Practices C. Hu et al. 10.3390/f15040712
- A stepwise method for downscaling SMAP soil moisture dataset in the CONUS during 2015–2019 H. Yang et al. 10.1016/j.jag.2024.103912
- Building Trust in Earth Science Findings through Data Traceability and Results Explainability P. Olaya et al. 10.1109/TPDS.2022.3220539
- Downscaling satellite soil moisture for landscape applications: A case study in Delaware, USA D. Warner et al. 10.1016/j.ejrh.2021.100946
Latest update: 30 May 2025
Short summary
Soil moisture is key for understanding soil–plant–atmosphere interactions. We provide a machine learning approach to increase the spatial resolution of satellite-derived soil moisture information. The outcome is a dataset of gap-free global mean annual soil moisture predictions and associated uncertainty for 28 years (1991–2018) across 15 km grids. This dataset has higher agreement with in situ soil moisture and precipitation measurements. Results show a decline of global annual soil moisture.
Soil moisture is key for understanding soil–plant–atmosphere interactions. We provide a machine...
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