ESA CCI Land Evaporation: Towards a long-term consistent satellite-based global evaporation dataset

Land evaporation is an essential component of the terrestrial water, energy, and carbon cycles, yet its large-scale behaviour remains poorly understood. This limited understanding relates to the scarcity and uneven distribution of ground-based measurements (particularly across the Global South), the difficulties in modelling the complex interplay of vegetation processes, atmospheric turbulence, and soil–vegetation–atmosphere dynamics, and the inability to observe evaporation directly from space. This uncertainty limits our ability to quantify land–atmosphere feedbacks, monitor hydrological extremes such as droughts and heatwaves, understand the influence of climate change on water availability, and enhance the resilience of agricultural systems, emphasising the need for accurate, long-term, and observation-based global records.

Given the relevance of land evaporation for climate, it has recently been identified by the Global Climate Observing System (GCOS) and the European Space Agency (ESA) as an Essential Climate Variable (ECV). ESA through its Climate Change Initiative (CCI) has recently launched the CCI Land Evaporation initiative, which aims to provide an observationally constrained dataset for climatological research that is aligned with GCOS requirements and climate community needs.

To achieve this goal, the ESA CCI Land Evaporation initiative will integrate satellite observations with state-of-the-art process-based and machine learning modelling. Long-term (1980-present), spatially consistent daily estimates of land evaporation and its associated ECV products—transpiration, interception loss, bare soil evaporation, as well as latent and sensible heat—will be generated using a novel algorithm following a multi-physics strategy.  Processes will be constrained by satellite data and represented by multiple alternative formulations derived from an extensive literature review. Multi-physics permutations will be evaluated through perturbation experiments, and estimates will be compared against eddy-covariance observations to identify the algorithmic configuration that achieves the highest performance while maximising simplicity and reliance on satellite data. The modular design will also enable the quantification of epistemic uncertainty, which will be provided for each one of the variables comprising the Land Evaporation CCI dataset.

Overall, this presentation will summarise the objectives, methodological framework, algorithm development, and anticipated contributions of the ESA CCI Land Evaporation initiative to climate monitoring and long-term assessment of the terrestrial water cycle.