By 2050, 68% of the world’s population will live in urban areas, with 255 million living in China (
https://www.un.org/development/desa/en/news/population/2018-revision-of-world-urbanization-prospects.html). Urbanization and population growth have brought a series of urban ecosystem problems, particularly in the urban thermal environment where urban heat islands (UHIs) have an important impact on public health and energy security [
1]. This phenomenon is more significant in China, which has a greater urban population than many other counties and more rapid urban growth [
2]. Previous studies have shown that the three-dimensional (3D) urban infrastructure affected land surface temperature (LST) more strongly than the 2D structure [
3,
4]. The 3D urban surface decreases surface albedo, storing more energy in the urban canopy, which further boosts the thermal environment [
5]. In addition, the 3D urban surface affects urban ventilation by increasing surface roughness, which also affects the thermal environment [
6]. Moreover, the impact of the 3D structure on UHIs varies depending on temporal scale, e.g., day and night [
7]. Obtaining regional 3D surface information is usually difficult: for example, the building height, building density, etc. are mostly derived from digital surface models (DSMs), these in turn are based on airborne LiDAR data which are often limited [
3,
4].
The local climate zone (LCZ) scheme provided by Stewart and Oke [
8] categorizes urban surfaces into 10 building types and 7 natural types based on their surface morphology. LCZ enriches the normal 2D land use/land cover classification and provides more focus on the 3D spatial distribution. Moreover, the LCZ can be mapped using available satellite remote sensing data. Future 3D urban growth is an inevitable trend, and the LCZ will help to investigate urban thermal environmental processes and then to develop mitigation measures. LCZ has been popularly used in studies of urban LST features [
9,
10,
11,
12], UHI [
13,
14], urban ventilation [
6], outdoor thermal comfort [
15], and urban energy budgets [
16,
17]. WUDAPT (the World Urban Database and Access Portal Tools) provide a standard workflow for LCZ classification from free satellite remote sensing data (e.g., Landsat), using Google Earth images for sampling selection and SAGA software for classification [
18]. The advantage of this approach is that it does not depend on city DSMs and can be applied to any city, using satellite images. An LCZ product of not less than 120 cities in different continents, established using the WUDAPT method as a supervised image classification, is being uploaded to the WUDAPT portal [
19]. This method is also widely used in China [
10,
11]. LST varies among different LCZs with higher LST over built-type LCZs and lower LST over natural-type LCZs [
9,
20,
21]. However, the spatiotemporal variations of LST based on LCZs are different across different cities [
22,
23,
24]. Du et al. [
25] showed that remote-sensed LST varied with LCZ and seasons in Nanjing, China, with the largest LST difference in summer. However, LST is negatively correlated with building height in all seasons, with the highest mean LST for large low-rise buildings (LCZ 8). Chen et al. [
10] showed that the remote-sensed LST distribution was quite different during day and night in Guangzhou and Hongkong. The highest daytime LST was usually obtained in high-density and low-rise areas (e.g., LCZ 3 and 8), while the highest night time LST was observed in high-density and high- or mid-rise urban areas (e.g., LCZ 1 and 2). However, Yang et al. [
6] showed high-density high-rise buildings had higher LSTs than other building types in Shanghai, China. Yang et al. [
9] reported that LCZ 7 (industry) had the highest LST derived from Landsat 8, in a study of Pearl River Delta cities; they also showed that cities with larger areas usually had higher LSTs. To enhance outdoor thermal comfort, cool cities and cool communities should be promoted with increasing water and vegetation area [
26]. While, for developing countries with densely population, there should have enough built area to accommodate population. Therefore, the reasonable configuration of built and natural surfaces with current urban area and population is essential for mitigating urban heat island and urban planning [
27].
Previous studies in China mainly focused on a particular city or an urban agglomeration. However, the thermal distribution of LCZs differs with different cities in different climate zones and also differs temporally, there is a lack of comprehensive understanding it. This study comprehensively investigated diurnal and seasonal variations in MODIS LST between LCZ types among cities in three urban agglomerations in China. In addition, we also discussed the optimal LCZ combination for better urban cooling by altering urban area and population of each LCZ type. Firstly, we categorize LCZs across 63 major cities in China, mostly covered with provincial capital cities with superior economy and larger population, using Landsat 8 data. Secondly, we study the diurnal and seasonal relationships between LCZs and LST in the three most important urban agglomerations in China (Jing-Jin-Ji, Yangtze River Delta, Pearl River Delta). In addition, MODIS quality control (QC) is important for LST temporal aggregation. Lai et al. [
28] showed that seasonal surface urban heat island (SUHI) differences with and without QC accounted for 29.9% of the total SUHI in China during night time and exceeded 5.0 K in northern cities. Therefore, QC is employed for seasonal LST calculations in this study. Finally, we explore the optimal combination of LCZs with minimum LST value for cool urban. This study will help us to comprehensively understand the urban surface thermal configuration of various LCZs in Chinese cities. Furthermore, it will be beneficial for planners and policy makers devising appropriate strategies to mitigate the urban thermal environment.