Vegetation indices (VIs), which describe remotely sensed vegetation properties such as
photosynthetic activity and canopy structure, are widely used to study vegetation dynamics …

Over the last few years, Deep learning (DL) approaches have been shown to outperform
state-of-the-art machine learning (ML) techniques in many applications such as vegetation …

In the era of water scarcity and severe droughts, the accurate estimation of
evapotranspiration (ET) is crucial for the efficient management of water resources …

Actual evapotranspiration (AET) is one of the decisive factors controlling the water balance
at the catchment level, particularly in arid and semi-arid regions, but measured data for …

Terrestrial evapotranspiration (ET) refers to a key process in the hydrological cycle by which
water is transferred from the Earth's surface to lower atmosphere. With spatiotemporal …

Evapotranspiration (ET) is sensitive to environmental factors. Accurate assessment of ET
and its response to changing environments have important scientific and practical …

Evapotranspiration (ET) estimation models can be broadly classified as statistical or physical
process based models. However, assuming the limitation of individual approaches, the …

Abstract In recent years, Machine Learning (ML) techniques have gained the attention of the
hydrological community for their better predictive skills. Specifically, ML models are widely …

Accurate quantification of land surface evapotranspiration (ET) is desperately crucial for
agricultural irrigation strategy, drought monitoring, and water resource management in …

Almost fifty years have passed since the idea to retrieve a value for Evapotranspiration (ET)
using remote sensing techniques was first considered. Numerous ET models have been …