This session focuses on methodological advancements in the use of remote sensing (RS) to improve the quantification of evapotranspiration (ET) across a range of climates and environments. We invite contributions that demonstrate how RS techniques can enhance ET assessment and prediction in diverse settings including agricultural landscapes, riparian zones, urban areas, and forest ecosystems. The session aims to provide an overview of recent developments in RS-based ET estimation, at different geographical scales-from regional to global. By applying innovative approaches, we seek to address the challenges of ET quantification and deepen our understanding of water dynamics and vegetation conditions across different landscapes.

With growing societal attention to climate change, drought and flood early warning systems, ecosystem monitoring, biodiversity conservation, and the pursuit of a sustainable future, the demand for accurate estimation, modelling, mapping, and forecasting of ET has expanded. We welcome contributions that leverage cutting-edge techniques such as artificial intelligence (AI), data fusion, sharpening algorithms, hybrid physical and process-based models, empirical and statistical methods, and machine learning (ML). The expanding variety of space and airborne sensors from current and upcoming missions opens new horizons for quantifying ET across different spatial scales and land cover types. In parallel, cloud computing platforms offer researchers powerful tools, data, and computational resources to estimate and analyse hydrological variables like ET while enabling scalable, efficient, and collaborative workflows. Remote sensing (RS) of ET supports evidence-based decision-making, contributes to sustainable water management practices, and better informs managers, end-users, and the scientific community working to improve the quantification of water cycle components.

In our session of RS of ET, we welcome your research findings, commentary pieces and debates on:
A. recent developments in RS of ET
B. application of AI, cloud computing, and technology advancement;
C. fusion of RS, modelling, and ground-based methods;
D. validation, calibration, and upscaling challenges and solutions;
E. future directions in RS of ET.