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Global Evapotranspiration Retrieval Using Fengyun-3D Passive Microwave Measurements With Genetic Algorithm Optimization
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AI
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P
王
J
DOI：10.1029/2025JD043823.png)
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Abstract
En 中文
Accurate estimation of terrestrial evapotranspiration (ET) is essential for understanding the global water cycle and energy balance. Satellite passive microwave (PMW) remote sensing offers a unique advantage for retrieving ET under cloudy conditions, where optical remote sensing fails. However, existing PMW-based ET retrieval methods face uncertainties at a global scale, particularly across diverse land types and climates. Additionally, observations from China's Fengyun-3D (FY-3D) satellite have not been utilized for global ET estimation. To address these challenges, this study combined in situ ET measurements from 207 flux towers across 13 vegetation types with FY-3D microwave radiation imager observations and reanalysis data sets. A machine-learning genetic algorithm (GA) was developed to optimize key parameters of the PMW-based ET retrieval method, generating a new global ET product (ETFY3D) with 0.25° resolution for 2020–2022. Evaluations at flux towers showed significantly improved accuracy compared to the original algorithm, reducing bias by 1.07 mm/day and root mean square error by 0.85 mm/day while increasing the Kling-Gupta efficiency by 0.67. The ETFY3D was further evaluated against three independent global ET products: the Moderate Resolution Imaging Spectroradiometer product, the Global Land Evaporation Amsterdam Model, and the Penman-Monteith-Leuning (PML) product. The comparison showed that ETFY3D performed consistently with these benchmark products at a global scale. Sensitivity analysis highlighted vegetation and humidity parameters' importance. This study demonstrates AI's potential for optimizing physical-based ET algorithms. And an independent new global ET product-based China's FY low orbit satellite was developed for further applications.
Keywords:
evapotranspiration
satellite microwave
Fengyun-3D
global product
Journal
J
IF:
3.4
Papers:
2.2W
Citations:
7.7W
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