A seamless global daily 5&thinsp;km soil moisture product from 1982 to 2021 using AVHRR satellite data and an attention-based deep learning model

Zhang, Yufang; Liang, Shunlin; Ma, Han; He, Tao; Tian, Feng; Zhang, Guodong; Xu, Jianglei

doi:10.5194/essd-17-5181-2025

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.

https://doi.org/10.5194/essd-17-5181-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 17, issue 10

Data description article

|

07 Oct 2025

Data description article |

| 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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3,077	1,053	135	4,265	113	117

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PDF: 1,053
XML: 135
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Views and downloads (calculated since 16 Jan 2025)

Month	HTML	PDF	XML	Total
Jan 2025	162	26	4	192
Feb 2025	152	20	3	175
Mar 2025	113	19	1	133
Apr 2025	75	16	3	94
May 2025	122	22	8	152
Jun 2025	76	17	13	106
Jul 2025	76	15	2	93
Aug 2025	84	20	0	104
Sep 2025	375	15	3	393
Oct 2025	493	81	11	585
Nov 2025	270	83	17	370
Dec 2025	287	115	10	412
Jan 2026	224	78	16	318
Feb 2026	188	121	13	322
Mar 2026	190	224	9	423
Apr 2026	188	175	22	385
May 2026	2	6	0	8

Cumulative views and downloads (calculated since 16 Jan 2025)

Month	HTML	PDF	XML	Total
Jan 2025	162	26	4	192
Feb 2025	152	20	3	175
Mar 2025	113	19	1	133
Apr 2025	75	16	3	94
May 2025	122	22	8	152
Jun 2025	76	17	13	106
Jul 2025	76	15	2	93
Aug 2025	84	20	0	104
Sep 2025	375	15	3	393
Oct 2025	493	81	11	585
Nov 2025	270	83	17	370
Dec 2025	287	115	10	412
Jan 2026	224	78	16	318
Feb 2026	188	121	13	322
Mar 2026	190	224	9	423
Apr 2026	188	175	22	385
May 2026	2	6	0	8

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Total article views: 4,265 (including HTML, PDF, and XML) Thereof 4,208 with geography defined and 57 with unknown origin.

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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.