Fully automated land surface temperature downscaling based on RGB very high spatial resolution images

Abstract

Downscaling is a particularly needed process in many environmental, social and governance applications at the fine scale. The need for an automated and reliable very high spatial resolution downscaling approach is then required. In this paper, a fully-automated open-access downscaling approach was proposed, named HSR-LST. It is based on the High Spatial Resolution (HSR) Red, Green and Blue (RGB) bands collected from commercial and free-to-access satellite images, generating LST values lower than 2-m spatial resolutions. This is based on the Landsat-8 thermal datasets and while implementing a fully-automated Ordinary Least Squares (OLS) approach. HSR-LST was implemented over Beirut, Boston and Dubai between 2016 and 2018. In comparison to an airborne LST image captured over ElKhorn River in Nebraska, USA, HSR-LST showed an AME of 0.88 °C and a R-squared value of 86.33%. Main results showed the variability of LST based on the sensed land features’ type. Different LST distribution footprints (i.e., irregular in Beirut, intermitted in Boston, systematic in Dubai) were highlighted depicting a characteristic urban configuration in each city. This latter along buildings’ material, density and height appear also to show a different effect on the local and surrounding LST values. By implementing the automated HSR-LST model in cities around the Globe, urban planners, policy makers and inhabitants can acquire improved information to assess urban heat islands, to propose more adequate planning policies, but more importantly to tackle urban heat and thermal comfort at the finest scales. HST-LST will effectively address the low spatial resolution of thermal bands. As HSR-LST is both automated and dynamic, it can be portable to other urban areas with diverse climatic regions.