A new perspective to assess the urban heat island through remotely sensed atmospheric profiles

被引:85
作者
Hu, Leiqiu [1 ]
Brunsell, Nathaniel A. [1 ]
机构
[1] Univ Kansas, Dept Geog, Lawrence, KS 66045 USA
基金
美国国家航空航天局;
关键词
Urban heat island; MODIS; Atmospheric profiles; Urban heat island curve; Near-surface temperature; LAND-SURFACE TEMPERATURE; VAPOR-PRESSURE DIFFERENCES; BOUNDARY-LAYER; AIR-TEMPERATURE; WATER-VAPOR; MODIS; IMPACT; MOISTURE; CLOUD; EVAPOTRANSPIRATION;
D O I
10.1016/j.rse.2014.10.022
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The detection of urban heat island (UHI) is generally conducted from ground observations of air temperature and remote sensing of land surface temperature (LST). Satellite remotely sensed LST has many merits, such as global coverage and consistent periodicity, which overcomes the weaknesses of ground observations related to the footprint, site distributions, and costs. For human related studies and urban climatology, air temperatures are equally important. This study explores the potential to estimate the near-surface air and dew-point temperatures from the MODIS 07 atmospheric profile product (MOD07_L2) to capture the UHI dynamics at 5 km resolution. Four mega-cities in North America: Phoenix, Houston, Chicago, and Toronto, are evaluated during 2003-2013 summers. The comparison between the MODIS near-surface temperature and the ground observations suggests an accuracy of 3-7 K RMSE for different cities and times of day. For air temperature, the Aqua overpass has better agreements, and nighttime has higher accuracy than daytime in most cases. In general, very dry (Phoenix) and very moist (Houston) climate conditions increase the variability of the MODIS temperature accuracy. This study also develops an urban heat island curve (UHIC) to represent UHI intensity by integrating the urban surface heterogeneity in a curve, showing the relationship between air temperature and urban fraction. UHIC provides a new way to quantify UHI city by city, which emphasizes the temperature gradients, consequently decreasing the impact of data biases. (C) 2014 Elsevier Inc. All rights reserved.
引用
收藏
页码:393 / 406
页数:14
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