Spatial Gap Filling in a Geostationary Land-Surface Temperature Product with a Masked Autoencoder

With the increasing availability and demand of remote sensing data from Earth observation satellites, the accuracy of weather prediction models can be improved substantially. Satellite products, such as Land-Surface Temperature (LST), however, suffer from missing data, either caused by clouds that cover the ground, by missing spatial coverage of the mission, or by outages of the sensors. Such spatial data gaps in LST products impose strict limitations when aiming to process the data further with, e.g., numerical weather prediction models assuming spatial continuity with gapless input data. We therefore propose a gap-filling algorithm based on a masked autoencoder that only receives a small percentage from a 32x32 LST snapshot and learns to reconstruct the missing patches. We use the spatial domain defined by the Land Atmosphere Feedback Initiative (LAFI) over central Europe and operate on geostationary LST data from the Copernicus Global Land Service in June 2023 at 5 km resolution. Our approach indicates considerable potential when filling spatial gaps in LST products, however, we emphasize one critical aspect. The LST estimates below clouds cannot be expected to be realistic and would require a sophisticated atmospheric correction. To mitigate this limitation, we aim to incorporate microwave data in future that penetrates clouds and therefore could help to estimate LST below clouds. In its current formulation, our algorithm can be used to fill gaps in LST products as if there were no clouds. We will show the potential and limitations of the autoencoder-based gap-filling algorithm for several showcases across Europe.