Shen, Li; Guo, Xulin; Xiao, Kang (2015). Spatiotemporally characterizing urban temperatures based on remote sensing and GIS analysis: a case study in the city of Saskatoon (SK, Canada). OPEN GEOSCIENCES, 7(1), 27-39.

Abstract
The purpose of this study is to spatiotemporally explore the characteristics of urban temperatures based on multi-temporal satellite data and historical in situ measurements. As one of the most rapidly urbanized cities in Canada, Saskatoon (SK) was selected as our study area. Surface brightness retrieving, Pearson correlation, linear regression modeling, and buffer analysis were applied to different satellite datasets. The results indicate that both Landsat and MODIS data can yield pronounced estimations of daily air temperature with a significantly adjusted R-2 of 0.803 and 0.518 at the spatial scales of 120 m and 1000 m, respectively. MODIS monthly LST data is highly suitable for monitoring the trend of monthly urban air temperature throughout summer (June, July, and August) due to a high average R-2 of 0.8 (P < 0.05), especially for the warmest month (July). Our findings also reveal that both the Saskatchewan River and urban green spaces have statistically significant cooling effects on the surrounding urban surface temperatures within 500 m and 200 m, respectively. In addition, a multiple linear regression model with four influential factors as independent variables can be developed to estimate urban surface temperatures with a highest adjusted R-2 of 0.649 and a lowest standard error of 0.076.

DOI:
10.1515/geo-2015-0005

ISSN:
2391-5447