Cloud occlusion is a common problem in the field of remote sensing, particularly for thermal infrared imaging. Clouds degrade thermal signals emitted from the Earth’s surface and interfere with the retrieved Land Surface Temperature (LST). Such cloud contamination severely reduces the set of serviceable thermal images for downstream applications. We introduce a novel method to remove cloud occlusions from Landsat 8 LST images. We call our method ISLAND, an acronym for Informing Brightness and Surface Temperature Through a Land Cover-based Interpolator. ISLAND predicts occluded LST through a set of spatio-temporal filters that perform distance-weighted spatio-temporal interpolation. A critical feature of ISLAND is that the filters are land cover-class aware, making it particularly advantageous in complex urban settings with heterogeneous land cover types and distributions. In this thesis, (1) We show ISLAND achieves robust reconstruction performance with an accuracy of around 1 Kelvin. (2) We provide a public dataset of 20 U.S. cities with pre-computed ISLAND thermal infrared and LST outputs. (3) Using several case studies, we demonstrate that ISLAND opens the door to a multitude of high-impact urban and environmental applications across the continental United States.