Satellite-Based Spatiotemporal Trends of Canopy Urban Heat Islands and Associated Drivers in China’s 32 Major Cities

Abstract

The urban heat island (UHI) effect, in which urbanized areas tend to have warmer conditions compared to their rural surroundings, has drawn increasing attention in recent years. Using ground-based and satellite remote sensing data, we present a method to quantify the spatial pattern and diurnal and seasonal variations in canopy layer heat islands (CLHIs) in China’s 32 major cities during 2009 and investigate their relationships with built-up intensity (BI), nighttime lights, vegetation activity, surface albedo, and surface urban heat island intensity (SUHII). The results show that both the annual daytime and nighttime CLHI intensities (CLHIIs) were positive ranging from 0.2 °C to 2.2 °C and from 0.3 °C to 2.4 °C for these major cities, respectively. Higher CLHIIs were observed in the night, especially for northern parts of China. Along urban–rural gradients, the CLHI effect had an exponential decay shape and differed greatly by season. The CLHII distribution correlated positively and significantly to BI and nighttime lights. Vegetation activity was negatively correlated with the CLHII and more strongly in summer. Surface albedo showed an extremely weak correlation with the CLHII. In addition, CLHII had a strong correlation with SUHII. The annual daytime SUHII was 1.2 ± 1.1 °C (mean ± standard deviation) with 0.40 °C (95% confidence interval 0.36 to 0.44 °C) of annual daytime CLHII. The annual nighttime SUHII was 2.0 ± 0.8 °C with 1.04 °C (0.99 to 1.09 °C) of annual nighttime CLHII. Our results suggest that, reducing built-up intensity and anthropogenic heat emissions and increasing urban vegetation provide a co-benefit of mitigating SUHI and CLHI effects.

The Impact of the Land Cover Dynamics on Surface Urban Heat Island Variations in Semi-Arid Cities: A Case Study in Ahmedabad City, India, Using Multi-Sensor/Source Data

“…The second aim of the work was to explain the potential variables affecting the diurnal and seasonal variations of SUHII. The SUHII is driven by various factors that can be grouped in LULC distribution and changes over the study area, urban site characteristics, and landscape configuration [12]; the more-investigated drivers are related to the vegetation activity, surface albedo, agricultural pattern, and population density [22,60,61]. In the present study, different potential variables were analyzed, i.e., cropland area, built-up area, ΔNDVI, ΔET, ΔWSA, population, and depth of water table.…”

Effects of Climate and Land Use changes on Vegetation Dynamics in the Yangtze River Delta, China Based on Abrupt Change Analysis

“…Vegetation growth under different land uses depends on certain hydrothermal conditions. Therefore, the effects of temperature and precipitation on cropland, woodland, grassland and built-up land have different effects on vegetation [71][72][73][74][75][76][77][78][79].…”

“…Although the increase of temperature can promote the growth of vegetation, the cropland in the study area is mainly man-made paddy field and irrigation makes the effects of temperature smaller than woodland and grassland. For urban land, there are heat island effect and impervious layer and thus the increase of temperature will increase the evaporation and inhibit the growth of vegetation [79]. For grassland research, the increase in temperature is conducive to the growth of grassland [80].…”

Spatial Configuration and Extent Explains the Urban Heat Mitigation Potential due to Green Spaces: Analysis over Addis Ababa, Ethiopia

“…Broadly, two major categories of UHI have been identified depending upon the substrate of their influences: air (atmospheric) and surface. The atmospheric UHI and the surface temperature are highly correlated to health, though the air UHI impact is reflected to be intimately connected to human health [38]. The in-situ measurement techniques used for atmospheric UHI monitoring [37][38][39][40], have the advantages of better temporal resolution but lack spatial resolution.…”

“…The atmospheric UHI and the surface temperature are highly correlated to health, though the air UHI impact is reflected to be intimately connected to human health [38]. The in-situ measurement techniques used for atmospheric UHI monitoring [37][38][39][40], have the advantages of better temporal resolution but lack spatial resolution. Whereas the surface land surface temperature (LST) is typically mapped using satellite data from thermal sensors [41][42][43].…”