Land-Surface Temperature Super Resolution from Geostationary to Low Earth Orbit Satellite Products with a Masked Autoencoder

Remote sensing data from satellites offer real-world observations on large spatial scales without incorporating model biases and model simplifications such as contained in reanalysis datasets. Numerical weather prediction models benefit largely from data with high temporal and spatial resolution, as provided by Earth observation remote sensing missions. Yet, while geostationary (GEO) satellites provide data at high temporal, e.g., 15 minutes, but low spatial resolution, e.g., 5 km, low earth orbit (LEO) satellites deliver data at low temporal, e.g., 16 days, but high spatial resolution, e.g., 90 m. In this research study, we therefore train a combination of a masked autoencoder and a ResNet model to learn a mapping from GEO to LEO Land-Surface Temperature (LST) products. The model receives the coarse-resolution 5 km LST from the Copernicus Global Land Service (apart from other static inputs) to approximate the fine-grained 70 m LST product from NASA’s ECOSTRESS mission. We use the spatial domain extent over Europe defined by the Land Atmosphere Feedback Initiative (LAFI). In theory, our algorithm allows the generation of 70 m LST estimates at a temporal resolution of 15 minutes. However, missing or corrupted input patches, when covered by clouds or in the event of missing sensor coverage or outages, challenges this optimal resolution. Therefore, we aim at 70 m daily LST estimates across continental Europe. We will present examples of the super resolution results from different biome regions across Europe, highlighting the potential and limitations of our approach.