A review of machine learning models and influential factors for estimating evapotranspiration using remote sensing and ground-based data

In the era of water scarcity and severe droughts, the accurate estimation of evapotranspiration (ET) is crucial for the efficient management of water resources, understanding hydrological and ecological processes, and comprehending the relationships between the atmosphere, hydrosphere, and biosphere. ET is a complex phenomenon influenced by a set of biophysical and environmental factors. Its estimation becomes more complicated in heterogeneous environments, demanding detailed data and accurate model calibration. Combining remote sensing imagery and machine learning (ML) models has provided a considerable capacity for estimating ET, which relaxes a number of assumptions and requires less data than traditional approaches. Satellite imagery provides influential variables for ET estimation using ML models. Nevertheless, a growing number of ML models and emerging satellite imagery has opened up a wide and complex potential before researchers. While previous studies have reviewed physical-based methods for ET estimation, this paper offers a recent decade review of the progress, challenges, and opportunities provided by the RS and ML models for the ET estimation and future outlook.

Automated summary

In the era of water scarcity and severe droughts, accurately estimating evapotranspiration (ET) is crucial for efficient water resource management and understanding hydrological and ecological processes. Combining remote sensing imagery and machine learning (ML) models provides a strong capability for ET estimation, which requires less data than traditional approaches. However, the increasing number of ML models and emerging satellite imagery presents both challenges and opportunities for researchers in this field.