This session will focus on using remote sensing to advance evapotranspiration (ET) quantification across a range of climates and environments. We invite contributions that explore how remote sensing can enhance ET assessments and predictions in diverse settings such as agriculture, riparian zones, urban areas, and forests. By applying these methods from regional to global scales, we aim to tackle the challenges of ET estimation and improve our understanding of water dynamics in various landscapes.

With the increased attention of society to climate change, drought and flood early warning systems, ecosystem monitoring, and biodiversity conservation, and reaching a sustainable future, the demand for estimating, modelling, mapping, and forecasting evapotranspiration (ET) has expanded. New techniques such as artificial intelligence (AI) and other techniques, such as data fusion, sharpening algorithms, and the combination of physical- and process-based models with empirical/statistical methods and machine learning are cutting-edge. These techniques and the variety of space/airborne sensors introduce new horizons to quantify ET at different scales over various land covers. Cloud computing platforms provide scientists and researchers with the pivotal tool, data, and computing resources to model and analyze hydrological parameters like ET while offering scalability, efficiency, and collaboration opportunities. Remote sensing (RS) of ET supports evidence-based decision-making, helps in addressing water-related challenges, contributes to sustainable water management practices, and better informs managers, end-users, and the community.

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 improvements;
e. future directions in RS of ET.