A Hybrid Biophysical‐Machine Learning Framework for Diurnal Surface Energy Flux Estimation Using Proximal Sensing

Abstract Thermal infrared‐based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics‐based analytical surface energy balance model for predicting latent energ...

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Hlavní autoři:	James F. Cross, Kanishka Mallick, Guler Aslan‐Sungur (Rojda), Andy VanLoocke, Darren T. Drewry
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	Wiley 2026-05-01
Edice:	Water Resources Research
Témata:	evapotranspiration surface energy balance thermal infrared sensing proximal sensing explainable machine learning vegetation biophysics
On-line přístup:	https://doi.org/10.1029/2025WR041510
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