Spatiotemporal patterns and driving forces of remotely sensed urban agglomeration heat islands in South China

Abstract

Rapid urbanization and increasing population have widely caused the urban heat island effect. Due to the decreasing distance between cities, there is an urgent need to reevaluate regional heat island intensity (RHII) in an urban agglomeration scale by considering all cities together instead of from conventional single city perspective. Using cropland land surface temperature as the reference temperature, we assessed the diurnal and seasonal RHII variations in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) urban agglomeration in South China. The boosted regression trees (BRT) method was then used to analyze the relative influence and marginal effect of possible drivers to disentangle their underlying driving mechanisms. Results showed that the daytime RHII spatial patterns averaged over the period 2003-2017 illustrated higher intensity and greater heterogeneity than their nighttime counterparts, especially for the stronger RHII in the central GBA around the estuary area. Seasonal dynamics of daytime RHII displayed a generally descending trend from summer to winter, but the opposite for night. BRT analyses indicated that at both annual and seasonal scales, vegetation fraction and background temperature had a dominant influence on RHII in daytime and nighttime, respectively. RHII variations were also considerably attributed to other drivers for different seasons. For daytime RHII, the other influential drivers included anthropogenic heat emissions and precipitation in summer, anthropogenic heat emissions and terrain in the transition season, and temperature and albedo in winter. For nighttime RHII, anthropogenic heat emissions for all seasons, vegetation activities for summer and the transition season, and precipitation for winter also had important contributions. The marginal effects detected the different nonlinear responses of diurnal and seasonal RHII to potential drivers, suggesting contrasting driving mechanisms. Results of this study highlight more targeted and informed strategies for RHII mitigation in the GBA and provide helpful insights into RHI evaluation in other urban agglomerations.

Keywords: Boosted regression trees; Driving forces; Land surface temperature; Regional heat island; Spatiotemporal patterns; Urban agglomeration.