Abstract

Surface urban heat island (SUHI) in the context of urbanization has gained much attention in recent decades; however, the seasonal variations of SUHI and their drivers are still not well documented. In this study, the Beijing-Tianjin-Hebei (BTH) urban agglomeration, one of the most typical areas experiencing drastic urbanization in China, was selected to study the SUHI intensity (SUHII) based on remotely sensed land surface temperature (LST) data. Pure and unchanged urban and rural pixels from 2000 to 2010 were chosen to avoid non-concurrency between land cover data and LST data and to estimate daytime and nighttime thermal effects of urbanization. Different patterns of the seasonal variations were found in daytime and nighttime SUHIIs. Specifically, the daytime SUHII in summer (4 °C) was more evident than in other seasons while a cold island phenomenon was found in winter; the nighttime SUHII was always positive and higher than the daytime one in all the seasons except summer. Moreover, we found the highest daytime SUHII in August, which is the growing peak stage of summer maize, while nighttime SUHII showed a trough in the same month. Seasonal variations of daytime SUHII showed higher significant correlations with the seasonal variations of ∆LAI (leaf area index) (R2 = 0.81, r = −0.90) compared with ∆albedo (R2 = 0.61, r = −0.78) and background daytime LST (R2 = 0.69, r = 0.83); moreover, agricultural practices (double-cropping system) played an important role in the seasonal variations of daytime SUHII. Seasonal variations of the nighttime SUHII did not show significant correlations with either of seasonal variations of ∆LAI, ∆albedo, and background nighttime LST, which implies different mechanisms in nighttime SUHII variation needing future studies.

Highlights

  • Despite only a small proportion of global land cover, urban areas place 54% of world’s population according to the 2014 revision of the World Urbanization Prospects [1,2,3], and the urban area of 2000Remote Sens. 2017, 9, 121; doi:10.3390/rs9020121 www.mdpi.com/journal/remotesensingRemote Sens. 2017, 9, 121 may triple by 2030 if the trend continues [4]

  • In order to quantitatively measure the thermal effects of urbanization, a set of specific urban and rural paired regions were chosen to compare the land surface temperature (LST) difference between urban and nearby cropland areas, following the below four principles: (1) Selected regions had undergone significant urban expansions; (2) The spatial distribution of the selected regions were throughout the study area to represent the entire region; (3) In the selected regions, conversion of cropland to built-up land has occurred; (4) There were sufficient optimal values of biophysical parameters in the chosen regions to ensure the reliability of statistical results

  • In this study, based on satellite remote sensing LST data and selected pure and unchanged urban and nearby cropland pixels, we assessed the seasonal variations of daytime and nighttime SUHI intensity (SUHII) and established its relations with the seasonal variations of ∆leaf area index (LAI), ∆albedo, and background LST in the BTH

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Summary

Introduction

Despite only a small proportion of global land cover, urban areas place 54% of world’s population according to the 2014 revision of the World Urbanization Prospects [1,2,3], and the urban area of 2000Remote Sens. 2017, 9, 121; doi:10.3390/rs9020121 www.mdpi.com/journal/remotesensingRemote Sens. 2017, 9, 121 may triple by 2030 if the trend continues [4]. Along with more and more urban inhabitants, a large number of artificial architectural landscapes and impervious surfaces (such as paving and roofing materials) will replace the original underlying landscapes containing transpiring vegetation and pervious surfaces [1,5,6,7,8] These changes will modify surface properties such as albedo, emissivity, leaf area index (LAI), vegetation cover fraction and roughness length [9,10,11,12,13,14,15], which will subsequently alter exchanges of energy and water between land surface and atmosphere, and modify surface microclimatic variables such as the temperatures, humidity, and near-surface winds [10,16]. A better understanding of UHI intensity and its drivers is critical to support urban climate research, urban planning, and urban sustainability [16,17,28,29]

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