Spatiotemporal variation of land surface temperature and its driving factors in Xinjiang, China

doi:10.1007/s40333-024-0072-5

Journal of Arid Land

2024, Vol. 16

Issue (3): 373-395 DOI: 10.1007/s40333-024-0072-5

Research article

Spatiotemporal variation of land surface temperature and its driving factors in Xinjiang, China

ZHANG Mingyu¹^,², CAO Yu¹^,², ZHANG Zhengyong¹^,²^,^*(

), ZHANG Xueying³, LIU Lin¹^,², CHEN Hongjin¹^,², GAO Yu¹^,², YU Fengchen¹^,², LIU Xinyi¹^,²

¹College of Sciences, Shihezi University, Shihezi 832000, China
²Key Laboratory of Oasis Town and Mountain-Basin System Ecology, Xinjiang Production and Construction Corps, Shihezi 832000, China
³Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China

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Abstract

Land surface temperature (LST) directly affects the energy balance of terrestrial surface systems and impacts regional resources, ecosystem evolution, and ecosystem structures. Xinjiang Uygur Autonomous Region is located at the arid Northwest China and is extremely sensitive to climate change. There is an urgent need to understand the distribution patterns of LST in this area and quantitatively measure the nature and intensity of the impacts of the major driving factors from a spatial perspective, as well as elucidate the formation mechanisms. In this study, we used the MOD11C3 LST product developed on the basis of Moderate Resolution Imaging Spectroradiometer (MODIS) to conduct regression analysis and determine the spatiotemporal variation and differentiation pattern of LST in Xinjiang from 2000 to 2020. We analyzed the driving mechanisms of spatial heterogeneity of LST in Xinjiang and the six geomorphic zones (the Altay Mountains, Junggar Basin, Tianshan Mountains, Tarim Basin, Turpan-Hami (Tuha) Basin, and Pakakuna Mountain Group) using geographical detector (Geodetector) and geographically weighted regression (GWR) models. The warming rate of LST in Xinjiang during the study period was 0.24°C/10a, and the spatial distribution pattern of LST had obvious topographic imprints, with 87.20% of the warming zone located in the Gobi desert and areas with frequent human activities, and the cooling zone mainly located in the mountainous areas. The seasonal LST in Xinjiang was at a cooling rate of 0.09°C/10a in autumn, and showed a warming trend in other seasons. Digital elevation model (DEM), latitude, wind speed, precipitation, normalized difference vegetation index (NDVI), and sunshine duration in the single-factor and interactive detections were the key factors driving the LST changes. The direction and intensity of each major driving factor on the spatial variations of LST in the study area were heterogeneous. The negative feedback effect of DEM on the spatial differentiation of LST was the strongest. Lower latitudes, lower vegetation coverage, lower levels of precipitation, and longer sunshine duration increased LST. Unused land was the main heat source landscape, water body was the most important heat sink landscape, grassland and forest land were the land use and land cover (LULC) types with the most prominent heat sink effect, and there were significant differences in different geomorphic zones due to the influences of their vegetation types, climatic conditions, soil types, and human activities. The findings will help to facilitate sustainable climate change management, analyze local climate and environmental patterns, and improve land management strategies in Xinjiang and other arid areas.

Key words： land surface temperature MOD11C3 climate change geographical detector (Geodetector) geographically weighted regression (GWR) source-sink effect Xinjiang

Received: 20 October 2023 Published: 31 March 2024

Corresponding Authors: *ZHANG Zhengyong (E-mail: zyz0815@163.com)

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The first and second authors contributed equally to this work.

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	ZHANG Mingyu
	CAO Yu
	ZHANG Zhengyong
	ZHANG Xueying
	LIU Lin
	CHEN Hongjin
	GAO Yu
	YU Fengchen
	LIU Xinyi

Cite this article:

ZHANG Mingyu, CAO Yu, ZHANG Zhengyong, ZHANG Xueying, LIU Lin, CHEN Hongjin, GAO Yu, YU Fengchen, LIU Xinyi. Spatiotemporal variation of land surface temperature and its driving factors in Xinjiang, China. Journal of Arid Land, 2024, 16(3): 373-395.

URL:

http://jal.xjegi.com/10.1007/s40333-024-0072-5 OR http://jal.xjegi.com/Y2024/V16/I3/373

Fig. 1 Overview of Xinjiang Uygur Autonomous Region and the six geomorphic zones (the Altay Mountains, Junggar Basin, Tianshan Mountains, Turpan-Hami (Tuha) Basin, Tarim Basin, and Pakakuna Mountain Group) based on the digital elevation model (DEM). Note that the figure is based on the standard map (GS(2023)2767) of the Map Service System (https://bzdt.ch.mnr.gov.cn/), and the standard map has not been modified. The Pamir Plateau, Karakoram Mountains, Kunlun Mountains, and Altun Mountains are collectively referred to as the Pakakuna Mountain Group.

Table 1 Detailed description of data used in the study

Table 2 Classification criteria for the change trend of LST in Xinjiang

Table 3 Description of interaction modes caused by the interaction between driving factors

Fig. 2 Spatial distribution of multi-year average annual LST (a), rate of change in annual average LST (b), and change trend of annual average LST (c) in Xinjiang during 2000-2020. LST, land surface temperature. Numbers of 11, 21, and 31 represent non-significant, significant, and highly significant decreasing, respectively; numbers of 12, 22, and 32 represent non-significant, significant, and highly significant increasing, respectively. Note that the figures are based on the standard map (GS(2023)2767) of the Map Service System (https://bzdt.ch.mnr.gov.cn/), and the standard map has not been modified.

Fig. 3 Temporal variation of annual average LST in Xinjiang from 2000 to 2020

Fig. 4 Distribution characteristics of multi-year average annual LST in various geomorphic zones in Xinjiang. The upper and lower horizontal lines represent the maximum LST and minimum LST, respectively; the bottom and top lines of the box represent the 25^th and 75^th percentile quartiles, respectively; the line and square in the middle of the box represent the average and median of LST, respectively; and the right-hand dots and trend lines represent the data points and the distributions, respectively.

Table 4 Statistical results of the multi-year average annual LST and rate of change of annual LST, as well as the area proportions of warming area and cooling area in Xinjiang and each geomorphic zone during 2000-2020

Fig. 5 Variations in the multi-year average annual LST and DEM along (a) the line of longitude (87.30°E) and (b) the line of latitude (43.30°N). A-H represent the Pakakuna Mountain Group, Tarim Basin, Tianshan Mountains, Junggar Basin, Altay Mountains, Ili River Valley, Bogda Mountains, and Barkol Mountains, respectively.

Table 5 Correlation coefficients between LST and DEM in Xinjiang and three mountainous regions

Fig. 6 Spatial distribution of multi-year average seasonal LST (a, d, g, and j), rate of change in seasonal average LST (b, e, h, and k), and change trend of seasonal average LST (c, f, i, and l) in Xinjiang during 2000-2020. Numbers of 11, 21, and 31 represent non-significant, significant, and highly significant decreasing, respectively; numbers of 12, 22, and 32 represent non-significant, significant, and highly significant increasing, respectively. Note that the figures are based on the standard map (GS(2023)2767) of the Map Service System (https://bzdt.ch.mnr.gov.cn/), and the standard map has not been modified.

Table 6 Rate of change in seasonal LST in Xinjiang and each geomorphic zone during 2000-2020

Fig. 7 Factor detection and interactive detection results (indicated by the q values) for various driving factors influencing the spatial differentiation of LST in Xinjiang. (a), factor detection; (b), interaction detection. X₁-X₁₁ represent longitude, latitude, DEM, slope, aspect, precipitation, sunshine duration, wind speed, normalized difference vegetation index (NDVI), population density, and land use and land cover (LULC), respectively.

Fig. 8 Spatial distribution of regression coefficients of dominant factors driving the spatial heterogeneity of LST. (a), DEM; (b), latitude; (c), wind speed; (d), precipitation; (e), sunshine duration; (f), NDVI. Note that the figures are based on the standard map (GS(2023)2767) of the Map Service System (https://bzdt.ch.mnr.gov.cn/), and the standard map has not been modified.

Table 7 Regression coefficients of major driving factors in Xinjiang and each geomorphic zone

Fig. 9 Area transfer among different LULC types (a), identification of the heat source and sink landscape (HSI; b), and contribution index (CI) of each LULC type to LST (c) in Xinjiang during 2000-2020. In Figure 9b, each color of the bar chart corresponds to each LULC type; numbers of 1-6 represent cultivated land, forest land, grassland, water body, construction land, and unused land, respectively.

Table 8 Identification of the heat source and sink landscape (HSI) and contribution index (CI) of each LULC type to LST in Xinjiang and each geomorphic zone in 2020


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