Articles | Volume 17, issue 10
https://doi.org/10.5194/essd-17-5181-2025
© Author(s) 2025. 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-17-5181-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
07 Oct 2025
Data description paper |
| 07 Oct 2025
| 07 Oct 2025
A seamless global daily 5 km soil moisture product from 1982 to 2021 using AVHRR satellite data and an attention-based deep learning model
Yufang Zhang
School of Software, Northwestern Polytechnical University, Xi'an, 710072, China
Department of Geography, University of Hong Kong, Hong Kong SAR, 999077, China
Department of Geography, University of Hong Kong, Hong Kong SAR, 999077, China
Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China
Feng Tian
Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China
Guodong Zhang
Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, 610031, China
Jianglei Xu
Department of Geography, University of Hong Kong, Hong Kong SAR, 999077, China
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Short summary
Soil moisture (SM) plays a vital role in climate, agriculture, and hydrology, yet reliable long-term, seamless global datasets remain scarce. To fill this gap, we developed a four-decade seamless global daily 5 km SM product using multi-source datasets and deep learning models. This product has long-term coverage, spatial and temporal integrity, and high accuracy, making it a valuable resource for applications like SM trend analysis, drought monitoring, and assessment of vegetation responses.
Soil moisture (SM) plays a vital role in climate, agriculture, and hydrology, yet reliable...
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