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Seasonal Contrast and Interactive Effects of Potential Drivers on Land Surface Temperature in the Sichuan Basin, China.

Authors :
Wang, Ziyi
Sun, Dongqi
Hu, Chunguang
Wang, Yu
Zhang, Jingxiang
Source :
Remote Sensing. Mar2022, Vol. 14 Issue 5, pN.PAG-N.PAG. 1p.
Publication Year :
2022

Abstract

Little is known about the seasonal heterogeneity of land surface temperature (LST) and the interaction relationship between potential drivers in Sichuan Basin, China. In this study, based on exploring the spatial heterogeneity of LST in Sichuan Basin, China, multi-source remote sensing data as potential drivers were selected and a Geo-detector model was applied to analyze the main drivers and the interactive relationship between drivers on LST during different seasons. The results showed that the high-temperature areas in Sichuan Basin in different seasons all appeared in the cities near the high mountains on the edge of the basin. This phenomenon was summarized as "sinking heat island" by us. From the driving factors, the biophysical parameters (DEM, SLOPE and NDVI) had the greatest impact on LST in each season, reaching the peak in the transition season. The climate parameters (WIND, HUM, PRE and TEM) and socioeconomic parameters (LIGHT, POP and ROAD) also had a certain impact on LST. The influence of a single landscape parameter (SHDI, PD, LPI, ED and LSI) on LST is limited. From the effect of factor interaction on LST, the interaction of biophysical parameters, climatic parameters and landscape parameters from summer to the transitional season was strengthened obviously, and it showed a downward trend in the winter; in contrast, the socioeconomic parameters showed the opposite characteristics, indicating that the interaction between human activities and other factors affected LST more obviously in the winter. The results of this study are not only valuable for understanding the spatial features of LST but also important for formulating mitigation strategies and sustainable development of urban heat island in Sichuan Basin. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20724292
Volume :
14
Issue :
5
Database :
Academic Search Index
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
155734679
Full Text :
https://doi.org/10.3390/rs14051292