ISLAND: Informing Brightness and Surface Temperature Through a Land Cover based Interpolator

Cloud occlusion is a common problem in the field of remote sensing, particularly for thermal infrared imaging. While remote sensing thermal instruments onboard operational satellites enable frequent and high-resolution observations over land, clouds adversely affect thermal signals by blocking outgoing longwave radiation emission from Earth’s surface, interfering with the retrieved ground emission temperature. Such cloud contamination severely reduces the set of serviceable thermal images for downstream applications, making it impractical to perform intricate time-series analysis of land surface temperature (LST). In this paper, we introduce a novel method to Inform Brightness and Surface Temperature Through a Land cover-based Interpolator (ISLAND). Our approach uses thermal infrared images from Landsat 8 (at 30 m resolution with 16-day revisit cycles) and the NLCD land cover dataset. ISLAND predicts occluded brightness temperature and LST through a set of spatio-temporal filters. 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. Through qualitative and quantitative analysis, we show that ISLAND achieves robust reconstruction performance across a variety of cloud occlusion and topographical conditions, and with a high spatio-temporal resolution. Via several case studies, we demonstrate that ISLAND opens the door to a multitude of high-impact environmental applications across the continental United States.