29 Dec 2020

29 Dec 2020

Review status: this preprint is currently under review for the journal ESSD.

Long time series of daily evapotranspiration in China based on the SEBAL model and multisource images and validation

Minghan Cheng1,2,3, Xiyun Jiao1,3, Binbin Li4, Xun Yu2, Mingchao Shao2, and Xiuliang Jin2 Minghan Cheng et al.
  • 1Hohai University, College of Agricultural Science and Engineering, Nanjing, Jiangsu Province, 210048, PR China
  • 2Institute of Crop Sciences, Chinese Academy of Agricultural Sciences/Key Laboratory of Crop Physiology and Ecology, Ministry of Agriculture, Beijing 100081, PR China
  • 3State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing, Jiangsu Province, 210048, PR China
  • 4Monitoring Center of Soil and Water Conservation, Ministry of Water Resources of the People's Republic of China, Beijing, 100053, PR China

Abstract. Satellite observations of evapotranspiration (ET) have been widely used for water resources management in China. An accurate ET product with a high spatiotemporal resolution is required for research on drought stress and water resources management. However, such a product is currently lacking. Moreover, the performances of different ET estimation algorithms for China have not been clearly studied, especially under different environmental conditions. Therefore, the aims of this study were as follows: (1) to use multisource images to generate a long time series (2001–2018) daily ET product with a spatial resolution of 1 km × 1 km based on the Surface Energy Balance Algorithm for Land (SEBAL); (2) to comprehensively evaluate the performance of the SEBAL ET in China using flux observational data and hydrological observational data; (3) to compare the performance of the SEBAL ET with the MOD16 ET product at the point-scale and basin-scale under different environmental conditions in China. At the point-scale, both the models performed best in the conditions of forest cover, subtropical zones, hilly terrain, or summer, respectively, and SEBAL performed better in most conditions. In general, the accuracy of the SEBAL ET (rRMSE = 44.91 %) was slightly higher than that of the MOD16 ET (rRMSE = 48.72 %). In the basin-scale validation, both the models performed better than in the point-scale validation, with SEBAL obtaining superior results (rRMSE = 19.15 %) to MOD16 (rRMSE = 33.62 %). Additionally, both the models showed a negative bias, with the bias of the MOD16 ET being higher than that of the SEBAL ET. In the daily-scale validation, the SEBAL ET product showed an RMSE of 0.92 mm/d and an r-value of 0.79. In general, the SEBAL ET product can be used for the qualitative analysis and most quantitative analysis of regional ET. SEBAL ET product is freely available at (Cheng, 2020). The results of this study can provide a reference for the application of remotely sensed ET products and the improvement of satellite ET observation algorithms.

Minghan Cheng et al.

Status: open (until 23 Feb 2021)
Status: open (until 23 Feb 2021)
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Minghan Cheng et al.

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Long time series (2001-2018) of daily evapotranspiration in China generated based on SEBAL Minghan Cheng

Minghan Cheng et al.


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Short summary
Evapotranspiration (ET) is a key node linking surface water and energy balance. Satellite observations of ET have been widely used for water resources management in China. In this study, a ET product with high spatiotemporal resolution was generated using surface energy balance algorithm and multisource remote sensing data. The generated ET product can be used for several geoscience studies, especially for global change, water resources mangement and agricultural drought monitoring, etc.