remote sensing Article Combination of Sentinel-2 and PALSAR-2 for Local Climate Zone Classification: A Case Study of Nanchang, China Chaomin Chen 1 , Hasi Bagan 1,2, * , Xuan Xie 1 , Yune La 3,4 and Yoshiki Yamagata 2   Citation: Chen, C.; Bagan, H.; Xie, X.; La, Y.; Yamagata, Y. Combination of Sentinel-2 and PALSAR-2 for Local Climate Zone Classification: A Case Study of Nanchang, China. Remote Sens. 2021, 13, 1902. https://doi.org/ 10.3390/rs13101902 Academic Editor: Takeo Tadono Received: 16 March 2021 Accepted: 5 May 2021 Published: 13 May 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China; 1000480060@smail.shnu.edu.cn (C.C.); 1000480061@smail.shnu.edu.cn (X.X.) 2 Center for Global Environmental Research, National Institute for Environmental Studies, Ibaraki 305-8506, Japan; yamagata@nies.go.jp 3 Cryosphere Research Station on the Qinghai-Tibetan Plateau, State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resource, Chinese Academy of Sciences, Lanzhou 730000, China; layune20@mails.ucas.ac.cn 4 University of Chinese Academy Sciences, Beijing 100049, China * Correspondence: hasi.bagan@nies.go.jp Abstract: Local climate zone (LCZ) maps have been used widely to study urban structures and urban heat islands. Because remote sensing data enable automated LCZ mapping on a large scale, there is a need to evaluate how well remote sensing resources can produce fine LCZ maps to assess urban thermal environments. In this study, we combined Sentinel-2 multispectral imagery and dual- polarized (HH + HV) PALSAR-2 data to generate LCZ maps of Nanchang, China using a random forest classifier and a grid-cell-based method. We then used the classifier to evaluate the importance scores of different input features (Sentinel-2 bands, PALSAR-2 channels, and textural features) for the classification model and their contribution to each LCZ class. Finally, we investigated the relationship between LCZs and land surface temperatures (LSTs) derived from summer nighttime ASTER thermal imagery by spatial statistical analysis. The highest classification accuracy was 89.96% when all features were used, which highlighted the potential of Sentinel-2 and dual-polarized PALSAR-2 data. The most important input feature was the short-wave infrared-2 band of Sentinel-2. The spectral reflectance was more important than polarimetric and textural features in LCZ classification. PALSAR-2 data were beneficial for several land cover LCZ types when Sentinel-2 and PALSAR- 2 were combined. Summer nighttime LSTs in most LCZs differed significantly from each other. Results also demonstrated that grid-cell processing provided more homogeneous LCZ maps than the usual resampling methods. This study provided a promising reference to further improve LCZ classification and quantitative analysis of local climate. Keywords: local climate zone; random forest; feature importance; land surface temperature; grid cells; Sentinel-2; PALSAR-2; ASTER 1. Introduction With continuous urbanization and the increasing settlement in global cities, natural landscapes are constantly converted to impervious surfaces in urban areas, altering the natural surface energy and water balances, which often results in altered climatic conditions in urban areas and the formation of the urban heat island (UHI) phenomenon [1–3]. As a key topic in urban climate studies, the concept of a “local climate zone” (LCZ) was introduced in 2012 by Stewart and Oke [4] to quantify the relationship between urban morphology and the UHI phenomenon. LCZs provide a standardized framework to link land cover types and urban morphology with corresponding thermal properties, so LCZs have been the systematic criteria for UHI comparisons [5]. Notably, the World Urban Database and Access Portal Tools (WUDAPT) project was developed as a new global initiative to produce standardized LCZ maps [6–8]. Because remote sensing data are Remote Sens. 2021, 13, 1902. https://doi.org/10.3390/rs13101902 https://www.mdpi.com/journal/remotesensing