Simultaneous investigation of surface and canopy urban heat islands over global cities

Du, Huilin; Zhan, Wenfeng; Liu, Zihan; Li, Jiufeng; Li, Long; Lai, Jiameng; Miao, Shiqi; Huang, Fan; Wang, Chenguang; Wang, Chunli; Fu, Huyan; Jiang, Lu; Hong, Falu; Jiang, Shuoxing

doi:10.1016/j.isprsjprs.2021.09.003

Cited by 60 publications

(32 citation statements)

References 77 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In contrast, the mean urban‐rural difference in T a (Δ T a ) from the CWS measurements was only 0.12°C (5th percentile = −1.92°C; 95th percentile = 2.19°C) at ≈1:30 p.m. (Figure 4b) and 0.05°C (5th percentile = −2.18°C; 95th percentile = 2.17°C) at ≈10:30 a.m. (Figure S2b in Supporting Information ). The lower Δ T a than Δ T s during daytime is consistent with previous results from various data sources and at multiple scales (Chakraborty et al., 2017; Du et al., 2021; Ho et al., 2016; Hoffman et al., 2020; Venter et al., 2021; Zhang et al., 2014). Urban areas are also generally drier than their surroundings with a mean urban‐rural difference in RH (ΔRH) of −0.6% (5th percentile = −7.16%; 95th percentile = 6.43%) for the Aqua daytime overpass (Figure 4c).…”

Section: Resultssupporting

confidence: 90%

“…Of these, Δ T a is equivalent to the commonly studied CUHI and Δ T s is similar to SUHI (Bonafoni et al., 2015; Chakraborty et al., 2017; Du et al., 2021; Venter et al., 2021). Although RH is a function of both absolute moisture content and ambient temperature, we call its urban‐rural differences the UDI effect since it is a standard weather‐relevant variable and one of the inputs used to estimate HI 0 (Equation ).…”

Section: Methodsmentioning

confidence: 99%

“…However, T a is more relevant for heat exposure than T s , but is difficult to measure in cities due to the dearth of standard weather stations and hard to model due to multiple confounding factors (Hardin et al., 2018; Ho et al., 2016; Muller et al., 2013; Stone et al., 2019). The two variables— T a and T s —are physically distinct (Jin & Dickinson, 2010), and the urban‐rural differences in T a (Δ T a ; also called canopy urban heat island; CUHI) and T s (Δ T s ; also called surface urban heat island; SUHI) are also not well‐correlated, especially during the day (Du et al., 2021; Venter et al., 2021; Zhang et al., 2014). This daytime decoupling between CUHI and SUHI brings into question the potential public health and policy implications of T s ‐based urban studies.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Lower Urban Humidity Moderates Outdoor Heat Stress

et al. 2022

View full text Add to dashboard Cite

Surface temperature is often used to examine heat exposure in multi‐city studies and for informing urban heat mitigation efforts due to scarcity of urban air temperature measurements. Cities also have lower relative humidity, traditionally not accounted for in large‐scale observational urban heat risk assessments. Here, using crowdsourced measurements from over 40,000 weather stations in ≈600 urban clusters in Europe, we show the moderating effect of this urbanization‐induced humidity reduction on outdoor heat stress during the 2019 heatwave. We demonstrate that daytime differences in heat index between urban clusters and their surroundings are weak, and associations of this urban‐rural difference with background climate, generally examined from the surface temperature perspective, are diminished due to moisture feedbacks. We also examine the spatial variability of surface temperature, air temperature, and heat index within these clusters—relevant for detecting hotspots and potential disparities in heat exposure—and find that surface temperature is a poor proxy for the intra‐urban distribution of heat index during daytime. Finally, urban vegetation shows much weaker (∼1/6th as strong) associations with heat index than with surface temperature, which has broad implications for optimizing urban heat stress mitigation strategies. These findings are valid for operational metrics of heat stress for shaded conditions (apparent temperature and humidex), thermodynamic proxies (wet‐bulb temperature), and empirical heat indices. Based on this large‐scale empirical evidence, surface temperature, used due to the lack of better alternatives, may not be suitable for accurately informing heat mitigation strategies within and across cities, necessitating more urban‐scale observations and better urban‐resolving models.

show abstract

Section: Resultssupporting

confidence: 90%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Lower Urban Humidity Moderates Outdoor Heat Stress

et al. 2022

View full text Add to dashboard Cite

show abstract

“…In particular, of issues on the thermal environment over urban area, urban heat island (UHI) effect is observable globally, a phenomenon that urban area is usually provided with higher temperatures compared against its rural surroundings (Voogt and Oke, 2003). Currently, many studies have been conducted in monitoring and understanding UHI, with intention to find suitable or effective measures to mitigate its adverse influence and to enhance the capacity in urban sustainable development (Du et al, 2021). Generally, there are different types of urban heat island, mainly including surface UHI (SUHI), canopy UHI (CUHI), boundary UHI, and subsurface UHI, while the SUHI and CUHI have been most investigated and compared (Voogt and Oke, 2003;Du et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Currently, many studies have been conducted in monitoring and understanding UHI, with intention to find suitable or effective measures to mitigate its adverse influence and to enhance the capacity in urban sustainable development (Du et al, 2021). Generally, there are different types of urban heat island, mainly including surface UHI (SUHI), canopy UHI (CUHI), boundary UHI, and subsurface UHI, while the SUHI and CUHI have been most investigated and compared (Voogt and Oke, 2003;Du et al, 2021). Particularly, the SUHI is based on land surface temperature (LST) usually obtained from the remotely sensed thermal infrared data.…”

Section: Introductionmentioning

confidence: 99%

Thermal Environment of Residential Communities Over a Coast Area in Southeastern China

Chen

Shen²,

Huang³

et al. 2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. The characters of the residential communities over Tong’an District of Xiamen City in thermal environment as well as in biophysical factors were investigated mainly based on the Lansat-8 OLI/TIRS Collection 2 Level-2 products. Specifically, the surface temperature product was used in measuring thermal environment, and the surface reflectance product was used to derive biophysical factors through the tasseled cap transformation as well as the normalized difference vegetation index (NDVI). Among the four types of residential community, the old community in urban area was generally lower in NDVI and in the Wetness component and therefore had higher surface temperature. Varied relationships of surface temperature with biophysical factors among four types were observed, which also demonstrated seasonal variation. At the same time, the preliminary investigation showed that the residential communities located in rural-urban fringe and a small portion of village communities had confronted with problems in thermal environment as well as in surface biophysical conditions. Furthermore, limitations of this study were discussed, mainly in spatial resolution and temporal representativeness of the Landsat-8 OLI/TIRS data, in biophysical components derived from the multi-spectral reflectance without depicting actual landscape, and in no consideration for the background of individual community. As the whole district covers wide and complex territory along with different local climate types, more investigations in details are required.

show abstract