Assessing Scaling Effect in Downscaling Land Surface Temperature in a Heterogenous Urban Environment

Keywords: Downscaling LST, Thermal remote sensing, Scaling effect, Scaling factor, Urban environment, ASTER, MODIS, AISA, TABI
Session Type: Virtual Paper
Day: Friday
Session Start / End Time: 4/9/2021 08:00 AM (Pacific Time (US & Canada)) - 4/9/2021 09:15 AM (Pacific Time (US & Canada))
Room: Virtual 33

Abstract

A literature review indicates that quantifying and fully understanding scaling effect in DLST processing remains unclear. In this study, a main goal is to quantify, assess and understand scaling effect in downscaling LST product processes at different higher spatial resolutions and spatial extents. A machine leaning model and a traditional multivariate regression model were adopted with corresponding scaling factors extracted from ASTER 15 – 30 m optical multispectral data and Airborne Imaging Spectrometer for Different Applications (AISA) 2 m hyperspectral visible-near infrared data. MODIS 990 m LST and ASTER 90 m LST products were downscaled to high and very high resolution LST maps. In addition, ETM+ 60 m retrieved LST and Thermal Airborne Broadband Imager (TABI) 2 m retrieved LST and its upscaled LSTs were used to verify higher resolution DLST maps. The experimental results demonstrate that scaling effect in downscaling LST processes is significant, especially downscaling LST to high and very high resolution LST maps. One innovation point derived from findings by assessing the scaling effect in DLST processing is that when DLST processes are at spatial resolutions beyond a range (20 – 30 m in this study) measured from semivariograms, the processes are safe and their results are reasonable and reliable, and thus their scaling effect may be ignored, but when spatial resolutions and spatial extent lag distance within the range, the DLST processes are not safe, and their results are not reliable and thus the scaling effect has to be considered.