Crowdsourced air temperatures contrast satellite measures of the urban heat island and its mechanisms

被引:175
作者
Venter, Zander S. [1 ]
Chakraborty, Tirthankar [2 ]
Lee, Xuhui [2 ]
机构
[1] Norwegian Inst Nat Res NINA, Terr Ecol Sect, N-0349 Oslo, Norway
[2] Yale Univ, Sch Environm, New Haven, CT USA
关键词
SURFACE; VARIABILITY; IMPACTS; EVAPOTRANSPIRATION; VEGETATION; CLIMATE; SUMMER;
D O I
10.1126/sciadv.abb9569
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
The ubiquitous nature of satellite data has led to an explosion of studies on the surface urban heat island (SUHI). Relatively few have simultaneously used air temperature measurements to compare SUHI with the canopy UHI (CUHI), which is more relevant to public health. Using crowdsourced citizen weather stations (>50,000) and satellite data over Europe, we estimate the CUHI and SUHI intensity in 342 urban clusters during the 2019 heat wave. Satellites produce a sixfold overestimate of UHI relative to station measurements (mean SUHI 1.45 degrees C; CUHI 0.26 degrees C), with SUHI exceeding CUHI in 96% of cities during daytime and in 80% at night. Using empirical evidence, we confirm the control of aerodynamic roughness on UHI intensity, but find evaporative cooling to have a stronger overall impact during this time period. Our results support urban greening as an effective UHI mitigation strategy and caution against relying on satellite data for urban heat risk assessments.
引用
收藏
页数:9
相关论文
共 72 条
  • [1] Methods to Calculate the Heat Index as an Exposure Metric in Environmental Health Research
    Anderson, G. Brooke
    Bell, Michelle L.
    Peng, Roger D.
    [J]. ENVIRONMENTAL HEALTH PERSPECTIVES, 2013, 121 (10) : 1111 - 1119
  • [2] [Anonymous], 1980, J AMN STAT ASS
  • [3] [Anonymous], 2010, TECHNICAL DESCRIPTIO
  • [4] Armstrong B, 2017, ENVIRON HEALTH PERSP, V125, DOI [10.1289/EHP1756, 10.1289/ehp1756]
  • [5] Bonafoni S, 2015, 2015 JOINT URBAN REMOTE SENSING EVENT (JURSE)
  • [6] Random forests
    Breiman, L
    [J]. MACHINE LEARNING, 2001, 45 (01) : 5 - 32
  • [7] Copernicus Global Land Cover Layers-Collection 2
    Buchhorn, Marcel
    Lesiv, Myroslava
    Tsendbazar, Nandin-Erdene
    Herold, Martin
    Bertels, Luc
    Smets, Bruno
    [J]. REMOTE SENSING, 2020, 12 (06)
  • [8] Evaluation of ten machine learning methods for estimating terrestrial evapotranspiration from remote sensing
    Carter, Corinne
    Liang, Shunlin
    [J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2019, 78 : 86 - 92
  • [9] A simplified urban-extent algorithm to characterize surface urban heat islands on a global scale and examine vegetation control on their spatiotemporal variability
    Chakraborty, T.
    Lee, X.
    [J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2019, 74 : 269 - 280
  • [10] Understanding Diurnality and Inter-Seasonality of a Sub-tropical Urban Heat Island
    Chakraborty, Tirthankar
    Sarangi, Chandan
    Tripathi, Sachchida Nand
    [J]. BOUNDARY-LAYER METEOROLOGY, 2017, 163 (02) : 287 - 309