Spatiotemporal downscaling and nowcasting of urban land surface temperatures with deep neural networks

ArXi:2605.13566v1 Announce Type: new 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 nstrate their application for intraday forecasting of LSTs.