Articles | Volume 18, issue 9
https://doi.org/10.5194/essd-18-6707-2026
© Author(s) 2026. 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-18-6707-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
11 Sep 2026
Data description article |
| 11 Sep 2026
| 11 Sep 2026
A benchmark dataset for half-hourly evapotranspiration estimation in China from 2000 to 2024
Long Qian
School of Soil and Water Conservation, Jiangxi University of Water Resources and Electric Power, Nanchang, 330099, China
College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, 712100, China
Xingjiao Yu
School of Soil and Water Conservation, Jiangxi University of Water Resources and Electric Power, Nanchang, 330099, China
College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, 712100, China
School of Soil and Water Conservation, Jiangxi University of Water Resources and Electric Power, Nanchang, 330099, China
Yaokui Cui
Institute of RS and GIS, School of Earth and Space Sciences, Peking University, Beijing, 100871, China
Zhitao Zhang
College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, 712100, China
Junying Chen
College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, 712100, China
Sumeng Ye
College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, 712100, China
Xuqian Bai
College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, 712100, China
Xiaogang Liu
Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming, 650500, China
Sien Li
Center for Agricultural Water Research in China, China Agricultural University, 100083, Beijing, China
Rangjian Qiu
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, 430072, Wuhan, China
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Accurately quantifying the blue and green water footprints of crops is essential for addressing unsustainable agricultural water use. However, long-term, high spatiotemporal resolution data have been lacking. Hence, multi-source remote sensing was integrated with high-resolution crop distribution and phenology within a detailed water footprint framework. ChinaCropWF provides daily, 1-km resolution blue and green water footprints for China's five major food crops from 2001 to 2020.
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Due to shortcomings such as extensive data gaps and limited observation durations in current ground-based latent heat flux (LE) datasets, we developed a novel gap-filling and prolongation framework for ground-based LE observations, establishing a benchmark dataset for global evapotranspiration (ET) estimation from 2000 to 2022 across 64 sites at various timescales. This comprehensive dataset can strongly support ET modeling, water–carbon cycle monitoring, and long-term climate change analysis.
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We created a highly precise dataset for daily water needs in China from 1951–2021, using machine learning to fill data gaps at 2419 weather stations. Independent models were trained for minor gaps, and LSTM models addressed severe gaps. Our research also examined the relationships between various weather parameters affecting water needs.
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Earth Syst. Sci. Data, 16, 1811–1846, https://doi.org/10.5194/essd-16-1811-2024, https://doi.org/10.5194/essd-16-1811-2024, 2024
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Using a collocation-based approach, we developed a reliable global land evapotranspiration product (CAMELE) by merging multi-source datasets. The CAMELE product outperformed individual input datasets and showed satisfactory performance compared to reference data. It also demonstrated superiority for different plant functional types. Our study provides a promising solution for data fusion. The CAMELE dataset allows for detailed research and a better understanding of land–atmosphere interactions.
Changming Li, Hanbo Yang, Wencong Yang, Ziwei Liu, Yao Jia, Sien Li, and Dawen Yang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-456, https://doi.org/10.5194/essd-2021-456, 2022
Revised manuscript not accepted
Short summary
Short summary
A long-term (1980–2020) global ET product is generated based on a collocation-based merging method. The produced Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration Data (CAMELE) performed well over different vegetation coverage against in-situ data. For global comparison, the spatial distribution of multi-year average and annual variation were in consistent with inputs.The CAMELE products is freely available at https://doi.org/10.5281/zenodo.6283239 (Li et al., 2021).
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Short summary
Understanding how water moves from land to the atmosphere is essential for studying climate and water resources. However, ground observations in China are often incomplete and short. We created the first seamless half-hourly dataset covering China from 2000 to 2024 by carefully filling data gaps and extending observations in time. The dataset closely matches measurements and provides a reliable foundation for climate, water, and environmental research.
Understanding how water moves from land to the atmosphere is essential for studying climate and...
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