This paper explores the evolution of geoscientific inquiry, tracing the progression from
traditional physics-based models to modern data-driven approaches facilitated by significant …

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 …

Random forests (RF) is a supervised machine learning algorithm, which has recently started
to gain prominence in water resources applications. However, existing applications are …

The hydrologic community has experienced a surge in interest in machine learning in recent
years. This interest is primarily driven by rapidly growing hydrologic data repositories, as …

Core Ideas Heihe was the first basin‐scale integrated observatory network established in
China. An intensive flux observation matrix experiment was conducted. New techniques, eg …

The constant need to increase agricultural production, together with the more and more
frequent drought events in many areas of the world, requires a more careful assessment of …

Abstract Machine learning (ML) models are increasingly used to study complex
environmental phenomena with high variability in time and space. In this study, the potential …

Accurate ahead evapotranspiration forecasting is crucial for irrigation planning, for wetlands,
agricultural and forest habitats preservation, and for water resource management. Deep …

Evapotranspiration (ET) is critical in linking global water, carbon and energy cycles.
However, direct measurement of global terrestrial ET is not feasible. Here, we first reviewed …

Having recognized the limitations in spatial representativeness and/or temporal coverage of
(i) current ground ETa observations and (ii) land surface model‐and remote sensing‐based …