A global urban heat island intensity dataset: Generation, comparison, and analysis

Yang, Qiquan; Xu, Yi; Chakraborty, Tirthankar; Du, Meng; Hu, Ting; Zhang, Ling; Liu, Yue; Yao, Rui; Yang, Jie; Chen, Shurui; Xiao, Changjiang; Liu, Renrui; Zhang, Mingjie; Chen, Rui

doi:10.1016/j.rse.2024.114343

A global urban heat island intensity dataset: Generation, comparison, and analysis

Journal Article · Tue Jul 30 00:00:00 EDT 2024 · Remote Sensing of Environment

DOI:https://doi.org/10.1016/j.rse.2024.114343· OSTI ID:2476331

Yang, Qiquan ^[1]; Xu, Yi ^[2]; Chakraborty, Tirthankar ^[3]; Du, Meng ^[4]; Hu, Ting ^[4]; Zhang, Ling ^[5]; Liu, Yue ^[6]; Yao, Rui ^[7]; Yang, Jie ^[7]; Chen, Shurui ^[2]; Xiao, Changjiang ^[2]; Liu, Renrui ^[8]; Zhang, Mingjie ^[8]; Chen, Rui ^[8]

Macau University of Science and Technology (China); Tongji University, Shanghai (China)
Tongji University, Shanghai (China)
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Nanjing University of Information Science and Technology (China)
Sun Yat-Sen Univ., Guangzhou (China)
Guangdong Academy of Sciences (China)
Wuhan Univ. (China)
Macau University of Science and Technology (China)

The urban heat island (UHI) effect, a phenomenon of local warming over urban areas, is the most well-known impact of urbanization on climate. Globally consistent estimates of the UHI intensity (UHII) are crucial for examining this phenomenon across time and space. However, publicly available UHII datasets are limited and have several constraints: (1) they are for clear-sky surface UHII, not all-sky surface UHII and canopy (air temperature) UHII; (2) the estimation methods often neglect anthropogenic disturbance, introducing uncertainties in the estimated UHII. To address these issues, this study proposes a new dynamic equal-area (DEA) method that can minimize the influence of various confounding factors on UHII estimates through a dynamic cyclic process. Utilizing the DEA method and leveraging various gridded temperature data, we develop a global-scale (>10,000 cities), long-term (over 20 years by month), and multi-faceted (clear-sky surface, all-sky surface, and canopy) UHII dataset. Further, based on these estimates, we provide a comprehensive analysis of the UHII and its trends in global cities. The UHII is found to be greater than zero in >80% of cities, with global annual average magnitudes around 1.0 °C (day) and 0.8 °C (night) for surface UHII, and close to 0.5 °C for canopy UHII. Furthermore, an interannual upward trend in UHII is observed in >60% of cities, with global annual average trends exceeding 0.1 °C/decade (day) and over 0.06 °C/decade (night) for surface UHII, and slightly surpassing 0.03 °C/decade for canopy UHII. Notably, there exists a positive correlation between the magnitude and trend of UHII, suggesting that cities with stronger UHII tend to experience faster growth in UHII. Additionally, discrepancies in UHII are found between different temperature data, stemming not only from distinctions in data types (surface or air temperature) but also from differences in data acquisition times (Terra or Aqua), weather conditions (clear-sky or all-sky), and processing methodologies (with or without gap filling). Overall, our proposed method, dataset, and analysis results have the potential to provide valuable insights for future urban climate studies. The UHII dataset is publicly available at https://doi.org/10.6084/m9.figshare.24821538.

View Accepted Manuscript (DOE)

Research Organization:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC05-76RL01830

OSTI ID:: 2476331

Report Number(s):: PNNL-SA--193597

Journal Information:: Remote Sensing of Environment, Journal Name: Remote Sensing of Environment Vol. 312; ISSN 0034-4257

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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