Articles | Volume 19, issue 14
https://doi.org/10.5194/gmd-19-6967-2026
https://doi.org/10.5194/gmd-19-6967-2026
Model description paper
 | 
30 Jul 2026
Model description paper |  | 30 Jul 2026

GEE-DisALEXI: cloud-based implementation of the DisALEXI model for evapotranspiration monitoring using Google Earth Engine

Yun Yang, Martha Anderson, Charles Morton, Yanghui Kang, Feng Gao, Weina Duan, Hui Liu, John Volk, and Christopher Hain

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Cited articles

Agrawal, Y., Kumar, M., Ananthakrishnan, S., and Kumarapuram, G.: Evapotranspiration Modeling Using Different Tree Based Ensembled Machine Learning Algorithm, Water Resour. Manage., 36, https://doi.org/10.1007/s11269-022-03067-7, 2022. 
Ahmad, S. K., Holmes, T. R., Kumar, S. V., Lahmers, T. M., Liu, P. W., Nie, W., Getirana, A., Orland, E., Bindlish, R., Guzman, A., Hain, C. R., Melton, F. S., Locke, K. A., and Yang, Y.: Droughts impede water balance recovery from fires in the Western United States, Nat. Ecol. Evol., 8, https://doi.org/10.1038/s41559-023-02266-8, 2024. 
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Anderson, M., Neale, C., Li, F., Norman, J., Kustas, W., Jayanthi, H., and Chavez, J.: Upscaling ground observations of vegetation water content, canopy height, and leaf area index during SMEX02 using aircraft and Landsat imagery, Remote Sens. Environ., 92, 447–464, https://doi.org/10.1016/j.rse.2004.03.019, 2004a. 
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Short summary
Evapotranspiration (ET) describes the transfer of water from land to the atmosphere and is fundamental to understanding agriculture, ecosystems, and drought. We implemented the established DisALEXI model on Google Earth Engine (GEE-DisALEXI), enabling scalable, high-resolution ET mapping over large regions. This paper evaluates this version's accuracy, highlights example applications, discusses limitations, and outlines opportunities for future improvements.
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