## 1 Introduction
[30] observed a relative difference in temperature for each land cover type in urban thermal environments, even between day and night [30]. [PERSON] and [PERSON] [28] and [PERSON] et al. [31] revealed a relationship between the SVF and urban thermal environment, as well as differences in the heat environment according to meteorological conditions [28; 31]. Previous studies have shown urban thermal environments exhibit various characteristics according to temporal and SD conditions. Therefore, when using HICS to improve the urban thermal environment, comprehensive considerations of the spatial and temporal aspects of urban heating are needed. Accordingly, to analyze HICS effectiveness when considering actual urban environments and including the aspects referred to above, [PERSON] et al. [12] used measurement sensors mounted on a mobile platform [12]. However, their research had limitations in the determination of overall urban thermal environment characteristics because it only included meteorological observations along a specific travel route. Although observation-based methods can obtain accurate measurements, the observations are one-dimensional and collected at a single time; thus, HICS effectiveness can only be determined in the dimension of a plane surface [32; 33]. In contrast, using unmanned aerial vehicle (UAV)-based remote sensing methods with thermal infrared (TIR) spectral sensors mounted on a UAV, the temperature of an observed area can be rapidly captured in a two-dimensional space for a continuous period, and high-spatial-resolution data can be obtained [34; 35; 36]. The purpose of this study was to determine the practical effectiveness of HICS for reducing UHI intensity in a study area where concepts specified by the WHO and IPCC can be spatially implemented. Because urban thermal environments exhibit various LST distributions according to temporal and SD characteristics, the difference in HICS effectiveness was also examined over time and according to the level of SD in the area. A study area was selected that featured the three representative cooling mechanisms of HICS. First, it contained zones that were expected to have cooling effects on the ground surface via the evapotranspiration of trees, vegetation, and green roofs within UGSs, as suggested by the WHO [3; 6]. Second, it had zones where overheating was expected to be suppressed by high-albedo materials coated on the surfaces of cool roofs and cool pavements, consistent with the GBAM proposed by the IPCC [7; 8; 9]. Third, it had shaded zones formed by the leaves and stems of trees and vegetation, as well as green roofs, in UGSs [10; 11]. To determine HICS effectiveness, TIR sensors were installed on a UAV to measure LSTs in the study area. The cooling effect was defined as a relative value, in which the temperatures of the experimental groups were compared with those of the control groups. The experimental groups included zones affected by the abovementioned three cooling mechanisms; the control groups were general urban zones not affected by HICS. The detailed workflow of this study was as follows: First, analysis targets were selected, and the HICS effect values to be estimated were defined. Second, a scenario for analyzing HICS effectiveness was established. The analysis scenario was designed as a matrix, with the SVF representing SD and the temporal conditions regarded as the two axes. The temporal conditions included changes in HICS effectiveness with the passage of time during the day and under different meteorological conditions. Third, data for determining HICS effectiveness based on an established scenario were constructed. Data were obtained for zones selected as experimental and control groups; LSTs and SVF distribution maps were constructed for these zones. Fourth, HICS effectiveness was estimated using these data. The findings of this study provide information that can assist decision-making when urban planners and policymakers apply HICS to urban environments (Figure 1).