Exploring Sentinel-L Data for Local Climate Zone Classification

Abstract

Local climate zone (LCZ) is a categorical scheme describing the morphology of urban area, which is a valuable not only for the original purpose of temperature study, but also for other urban oriented studies like population density estimation and economical development monitoring. Standard LCZ production works only on individual cities using merely optical data, mostly LandSat-8 data. Our goal is to develop a framework that 1) can potentially work on a large number of cities, i.e., training on number of cities and testing on number of other cities; 2) exploits Synthetic Aperture Radar (SAR) data. In this paper, we investigated the potential of Sentinel-l Dual-Pol data on producing LCZ maps in general. It shows the Sentinel-1 data could improve the classification accuracy of several LCZ classes. Joint use of LandSat-8 data, Open Street Map (OSM) data and Sentinel-1 data provide 62.05% overall accuracy, which is higher than 51.20% achieved by using only LandSat-8 and OSM data.