A Novel SUHI Referenced Estimation Method for Multicenters Urban Agglomeration using DMSP/OLS Nighttime Light Data

Abstract

The surface urban heat island (SUHI) of urban agglomeration has always been an important topic in the studies of urban heat island, especially with the development of satellitebased land surface temperature (LST) products. However, most studies are limited to the perspective of a single city, ignoring the impact of urban agglomeration and the changes of LST at day and night on the reference LST (RLST) (e.g., rural areas). Consequently, this article proposed a novel method about SUHI intensity estimation for the multicenters (mcSUHII) of urban agglomeration in Guangdong-Hong Kong-Macao Greater Bay Area (GHMBay) using nighttime light (NTL) data (i.e., DMSP/OLS) obtained in October, 2010. The mcSUHII method considered the RLST of SUHII estimation based on multicenter structure, and was more flexible to adapt the impact of human activity intensity. The study showed that compared with other RLSTs, such as suburban and forest, mcSUHII mitigates the underestimation bias caused by ignoring the multicenter structure. Importantly, the change in SUHII for urban agglomerations is greater than for a single city. Moreover, it was illustrated that the variation of SUHII presented an obvious inverted U-shape along the gradient from the inland to the coastal cities. The highest SUHIIs in the delta cities at day and night are ∼7.27 ± 1.71°C and ∼4.46 ± 1.42°C, respectively. Additionally, NTL served as the dominator together with other factors that were capable of explaining more than 90% of the spatial variation in SUHII in GHMBay. Therefore, considering multicenters more in estimation of SUHII of urban agglomeration for the sustainable development.

Assessing the impact of urban geometry on surface urban heat island using complete and nadir temperatures

“…Thus, the impact of such error on the estimated SUHIc is rather limited. Another aspect related to the bias on the SUHIIc estimates is that a different choice of the rural reference may lead to a large bias on the values in either SUHII (Yao et al ., 2019; Li et al ., 2020). This question was not investigated in this study, however.…”

Examining Spatio-Temporal Characteristics of Urban Heat Islands and Factors Driving Them in Hangzhou, China

“…The spatial information on urban public facilities contained in the POI data can be used to explore the spatio-temporal distributions of the densities of economic and social activities, and the relationship between the POI and UHIs has been verified [60], [61]. The NLD is also widely used to study UHIs at different scales [62], [63]. It is clear that natural and social factors had opposite effects on the UHI.…”

Modelling Inter-pixel Spatial Variation of Surface Urban Heat Island Intensity

“…LST) derived from multiple airborne or satellite sources to measure the radiative temperature differences between continuous urban and surrounding nonurban surfaces with similar geographic features (Peng et al 2012;Voogt and Oke 2003;Zhang and Cheng 2019;Zhou and Chen 2018), instead of relying on ground-based meteorological monitoring data which are usually spatially scarce and sparsely distributed (Li and Li 2020;Oke 1982;Smoliak et al 2015;Zhou and Chen 2018). With the advent and rapid advancements of thermal remote sensing technology and easy accessibility to a large corpora of remote sensing data with wall-to-wall coverage of land surface and continuous temporal operation, SUHII has gained increasing attention and wide application in recent decades (Li et al 2020a;Lu et al 2020;Meng et al 2018;Rasul et al 2017;Shen et al 2020;Zhou and Chen 2018). It has greatly improved the scientific understanding of the characteristics of surface urban heat island phenomenon and associated driving forces (Buyantuyev and Wu 2010;Lai et al 2018;Streutker 2002;Tran et al 2006), and provide useful information for designing various anthropogenic interventions to mitigate heat risks in tandem with cognate environmental and public health problems (Bonafoni et al 2017;Deilami et al 2018;Jenerette et al 2016;Peng et al 2020).…”

“…The most common and widely adopted approach for quantifying SUHII is to compare LST differences between urban and surrounding rural/reference areas (Li et al 2020a;Lu et al 2020;Peng et al 2012;Rasul et al 2017;Schwarz et al 2011). While this SUHII measurement could provide useful UHI information at various spatio-temporal scales (Li and Li 2020) and facilitate inter-city comparison (Shen et al 2020), it is obviously sensitive to the selection of representative pixels which can adequately delineate urban and rural/reference areas (Deilami et al 2018;Li et al 2018;Li et al 2020a;Streutker 2002). The empirical differentiation between urban and rural areas is indeed, in most cases, fuzzy and inconsistent (Li et al 2012;Rajasekar and Weng 2009;Stewart and Oke 2012).…”