Lake Environmental Monitoring

The project

Satellite-derived Lake Surface Water Temperature (LSWT) is a key indicator of lake health, heatwaves, and long-term climate trends, but the daily record is often fragmented by cloud cover and acquisition gaps, especially for small lakes, where entire scenes can be missing for several consecutive days.

MEEO’s role

Here MEEO adapts self-supervised deep learning (Masked Autoencoders) to reconstruct cloud-free daily LSWT fields, learning spatio-temporal patterns from Earth Observation time series (ESA Lakes CCI) and inferring missing observations in a physically consistent way, without manually labelled data.

The result is a consistent, gap-free time series that scales from single-lake pilots to global, multi-lake monitoring, turning fragmented satellite records into reliable evidence for environmental decision-making.