Various forms of machine learning (ML) methods have historically played a valuable role in
environmental remote sensing research. With an increasing amount of “big data” from earth …

The recent advancement in data science coupled with the revolution in digital and satellite
technology has improved the potential for artificial intelligence (AI) applications in the …

Understanding the impact of global land surface temperature (LST) changes on gross
primary production (GPP) is crucial for addressing global sustainability challenges …

Drought is a common hydrometeorological phenomenon and a pervasive global hazard. As
our climate changes, it is likely that drought events will become more intense and frequent …

This review surveys the basic theories, observational methods, satellite algorithms, and land
surface models for terrestrial evapotranspiration, E (or λE, ie, latent heat flux), including a …

More accurate and process-based satellite evapotranspiration (ET) estimation for the
Tibetan Plateau (TP)—the Third Pole of the world—have long been of major interest in …

Remote sensing datasets are increasingly being used to provide spatially explicit large
scale evapotranspiration (ET) estimates. Extensive evaluation of such large scale estimates …

Evapotranspiration (ET) is a vital variable for land‐atmosphere interactions that links surface
energy balance, water, and carbon cycles. The in situ techniques can measure ET …

In the context of climate change, the occurrence of water stress in forest ecosystems, which
are solely dependent on precipitation, has exhibited a rising trend, even among species that …

Water use efficiency (WUE) measures the trade-off between carbon gain and water loss of
terrestrial ecosystems and better understanding its dynamics and controlling factors is …