Yang et al. (2026) GEE-DisALEXI: cloud-based implementation of the DisALEXI model for evapotranspiration monitoring using Google Earth Engine

Identification

• Journal: Geoscientific model development
• Year: 2026
• Date: 2026-07-30
• Authors: Yun Yang, Martha C. Anderson, Charles Morton, Yanghui Kang, Feng Gao, Weina Duan, Hui Liu, John Volk, Christopher Hain
• DOI: 10.5194/gmd-19-6967-2026

Research Groups

• School of Integrative Plant Science, Cornell University
• Hydrology and Remote Sensing Laboratory, USDA ARS
• Desert Research Institution
• Department of Biological Systems Engineering, Virginia Tech
• Marshall Space Flight Center, NASA

Short Summary

This paper presents the cloud-based implementation of the Disaggregation of the Atmosphere Land Exchange Inverse (DisALEXI) model on Google Earth Engine (GEE), enabling scalable and high-resolution evapotranspiration (ET) data production. The model exhibits excellent performance in croplands, particularly during the growing season.

Objective

• Investigate the feasibility of cloud-based ET modeling using GEE-DisALEXI.
• Evaluate the model's performance across different biomes and climate zones.
• Explore potential applications of GEE-DisALEXI for drought monitoring and other agricultural, forest, and water management contexts.

Study Configuration

• Spatial Scale: 30 m spatial resolution
• Temporal Scale: Daily to annual time steps

Methodology and Data

• Models used: Disaggregation of the Atmosphere Land Exchange Inverse (DisALEXI) model
• Data sources: Landsat Collection 2 Level 2 surface temperature and reflectance, ALEXI ET, CFSR meteorological inputs, National Land Cover Database (NLCD), USGS streamflow observations

Main Results

• GEE-DisALEXI exhibits excellent performance in croplands, particularly during the growing season.
• The model achieves a normalized Mean Absolute Error (MAE) of 16.8% at monthly time steps.
• Annual bias of DisALEXI ET comparing with water balance estimated ET is -0.36%.
• GEE-DisALEXI shows good correlation with US Drought Monitor drought signal and is the strongest over humid areas.

Contributions

• The paper contributes to a deeper understanding of GEE-DisALEXI data and builds a baseline for future evolution of the GEE version of the DisALEXI model.
• The study highlights the potential applications of cloud-based ET modeling using GEE-DisALEXI for drought monitoring and other agricultural, forest, and water management contexts.

Funding

• This research was supported by NASA's Terrestrial Hydrology Program (NNX15AD38G) and the USDA ARS.

Citation

@article{Yang2026GEEDisALEXI,
  author = {Yang, Yun and Anderson, Martha C. and Morton, Charles and Kang, Yanghui and Gao, Feng and Duan, Weina and Liu, Hui and Volk, John and Hain, Christopher},
  title = {GEE-DisALEXI: cloud-based implementation of the DisALEXI model for evapotranspiration monitoring using Google Earth Engine},
  journal = {Geoscientific model development},
  year = {2026},
  doi = {10.5194/gmd-19-6967-2026},
  url = {https://doi.org/10.5194/gmd-19-6967-2026}
}