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BY-NC-ND 3.0 license Open Access Published by De Gruyter Open Access November 10, 2014

Spatiotemporally characterizing urbantemperatures based on remote sensing and GISanalysis: a case study in the city of Saskatoon(SK, Canada)

  • Li Shen EMAIL logo , Xulin Guo and Kang Xiao
From the journal Open Geosciences


The purpose of this study is to spatiotemporallyexplore the characteristics of urban temperaturesbased on multi-temporal satellite data and historical insitu measurements. As one of the most rapidly urbanizedcities in Canada, Saskatoon (SK) was selected as our studyarea. Surface brightness retrieving, Pearson correlation,linear regression modeling, and buffer analysis were appliedto different satellite datasets. The results indicatethat both Landsat and MODIS data can yield pronouncedestimations of daily air temperature with a significantlyadjusted R2 of 0.803 and 0.518 at the spatial scales of 120mand 1000 m, respectively. MODIS monthly LST data ishighly suitable for monitoring the trend of monthly urbanair temperature throughout summer (June, July, and August)due to a high average R2 of 0.8 (P<0.05), especiallyfor the warmest month (July). Our findings also reveal thatboth the Saskatchewan River and urban green spaces havestatistically significant cooling effects on the surroundingurban surface temperatures within 500 m and 200 m, respectively.In addition, a multiple linear regression modelwith four influential factors as independent variables canbe developed to estimate urban surface temperatures witha highest adjusted R2 of 0.649 and a lowest standard errorof 0.076.


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Received: 2014-02-03
Accepted: 2014-06-23
Published Online: 2014-11-10

© 2015 Li Shen et al.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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