Articles | Volume 17, issue 10
https://doi.org/10.5194/essd-17-5181-2025
https://doi.org/10.5194/essd-17-5181-2025
Data description paper
 | 
07 Oct 2025
Data description paper |  | 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, Shunlin Liang, Han Ma, Tao He, Feng Tian, Guodong Zhang, and Jianglei Xu

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Cited articles

Amanambu, A. C., Mossa, J., and Chen, Y.-H.: Hydrological Drought Forecasting Using a Deep Transformer Model, Water, 14, 3611, https://doi.org/10.3390/w14223611, 2022. 
Bahdanau, D., Cho, K., and Bengio, Y.: Neural Machine Translation by Jointly Learning to Align and Translate, arXiv [preprint], https://doi.org/10.48550/arXiv.1409.0473, 2014. 
Bartalis, Z., Wagner, W., Naeimi, V., Hasenauer, S., Scipal, K., Bonekamp, H., Figa, J., and Anderson, C.: Initial soil moisture retrievals from the METOP-A Advanced Scatterometer (ASCAT), Geophys. Res. Lett., 34, L20401, https://doi.org/10.1029/2007GL031088, 2007. 
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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.
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