Abstract:Land Surface Temperature (LST) is a key variable for various applications, such as urban climate and ecology studies. Yet, existing satellite-derived LST products provide either high spatial or high temporal resolution, resulting in a fundamental trade-off between the two. To address this trade-off, we combine observations from a geostationary and a polar orbiting satellite and provide LST fields at high spatial and high temporal resolution (1 km at 15-min intervals). We demonstrate their application for intraday forecasting of LSTs. To estimate LST fields at high spatiotemporal resolution, a U-Net model is trained to map LST fields from SEVIRI/MSG (3 km and 15 min resolution) to LST fields from Terra/Aqua MODIS (1 km, 4 overpasses per day) that are collocated in space and time. The presented model has been trained on LSTs across large European cities with a population exceeding 1 million inhabitants, and achieves an RMSE = $1.92$°C and near-zero bias MBE = $0.01$°C on the hold-out test set. As a second step, we present an LST nowcasting model based on ConvLSTM architecture, trained across downscaled LST fields with forecast lead times of 15 to 75 minutes. The nowcasting model outperforms a persistence and a Climatological Rolling Median benchmarks, with RMSEs of $0.57$ to $1.15$°C for the considered lead times and biases ranging from $-0.1$ to $0.14$°C. An additional validation conducted against independent MODIS overpasses confirms robust performance. Our LST forecast model at high spatiotemporal resolution is directly applicable to operational satellite-based LST monitoring.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.13566 [cs.LG] |
| (or arXiv:2605.13566v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.13566 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Solomiia Kurchaba [view email]
[v1]
Wed, 13 May 2026 14:05:24 UTC (4,369 KB)