Intracity Temperature Estimation by Physics Informed Neural Network Using Modeled Forcing Meteorology and Multispectral Satellite Imagery

Estimating urban surface temperature at high resolution is crucial for effective urban planning for climate-driven risks. This high-resolution surface temperature over broader scales can usually be obtained via satellite remote sensing for historical period. However, it can be hard for future predictions. This article presents a physics informed hierarchical perception (PIHP) network, a novel approach for accurate, high-resolution, and generalizable urban surface temperature estimation. The key to our approach is leveraging the implied temperature-related physics information of the land surface structure from high-resolution multispectral satellite images, thus achieving precise estimation or prediction for high spatial resolution urban surface temperature. Specifically, a semantic category histogram is first designed to describe the land surface structures. Based on this, a hierarchical urban surface perception network is proposed to capture the complex relationship between the underlying land surface features, upper atmosphere conditions, and the intracity temperature. The proposed PIHP-Net makes it possible to generate models that can generalize across different cities, thus estimating or predicting high-resolution urban surface temperature when the satellite land surface temperature (LST) observation is not available. Experiments over various cities in different climate regions in China show, for the first time, errors less than 2 K (for most of the cases) at the high resolution (60-by-60 meters grids), thus making it possible to predict future intracity temperature from forcing meteorology and multispectral satellite imagery.