Global Evapotranspiration Retrieval Using Fengyun-3D Passive Microwave Measurements With Genetic Algorithm Optimization

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 (ET_FY3D) 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 ET_FY3D 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 ET_FY3D 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.