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Remote Sens. 2019, 11(2), 182; https://doi.org/10.3390/rs11020182

Spatial Patterns of Land Surface Temperature and Their Influencing Factors: A Case Study in Suzhou, China

1
College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
2
College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China
3
School of Earth and Environmental Sciences, The University of Queensland, Brisbane, QLD 4072, Australia
*
Authors to whom correspondence should be addressed.
Received: 28 December 2018 / Revised: 14 January 2019 / Accepted: 15 January 2019 / Published: 18 January 2019
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Abstract

Land surface temperature (LST) is a fundamental Earth parameter, on both regional and global scales. We used seven Landsat images to derive LST at Suzhou City, in spring and summer 1996, 2004, and 2016, and examined the spatial factors that influence the LST patterns. Candidate spatial factors include (1) land coverage indices, such as the normalized difference built-up index (NDBI), the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI), (2) proximity factors such as the distances to the city center, town centers, and major roads, and (3) the LST location. Our results showed that the intensity of the surface urban heat island (SUHI) has continuously increased, over time, and the spatial distribution of SUHI was different between the two seasons. The SUHIs in Suzhou were mainly distributed in the city center, in 1996, but expanded to near suburban, in 2004 and 2016, with a substantial expansion at the highest level of SUHIs. Our buffer-zone-based gradient analysis showed that the LST decays logarithmically, or decreases linearly, with the distance to the Suzhou city center. As inferred by the generalized additive models (GAMs), strong relationships exist between the LST and the candidate factors, where the dominant factor was NDBI, followed by NDWI and NDVI. While the land coverage indices were the LST dominant factors, the spatial proximity and location also substantially influenced the LST and the SUHIs. This work improved our understanding of the SUHIs and their impacts in Suzhou, and should be helpful for policymakers to formulate counter-measures for mitigating SUHI effects. View Full-Text
Keywords: land surface temperature (LST); surface urban heat islands (SUHIs); spatial patterns; gradient change; generalized additive model (GAM); dominant factors land surface temperature (LST); surface urban heat islands (SUHIs); spatial patterns; gradient change; generalized additive model (GAM); dominant factors
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Feng, Y.; Gao, C.; Tong, X.; Chen, S.; Lei, Z.; Wang, J. Spatial Patterns of Land Surface Temperature and Their Influencing Factors: A Case Study in Suzhou, China. Remote Sens. 2019, 11, 182.

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