2001—2020年中国地表温度时空分异及归因分析

doi:10.11821/dlxb202207010

Abstract

Abstract:

The variation of land surface temperature (LST) has a vital impact on the energy balance of the land surface process and the ecosystem stability. Based on MDO11C3, we used methods including regression analysis, GIS spatial analysis, correlation analysis, and center-of-gravity model, etc., to analyze the LST variation and its spatiotemporal diversity in China from 2001 to 2020. Finally, the Geodetector was used to identify the leading factors of LST variation in 38 eco-geographical zones of China, and explore the causes of its pattern. The results show that: (1) the average LST in China from 2001 to 2020 is 9.6 ℃, which is high in the plains, and low in the mountainous areas. Generally, LST has a striking negative correlation with altitude, with a correlation coefficient of -0.66. China's First Ladder has the most conspicuous negative correlation, with a correlation coefficient of -0.76 and the lapse rate of LST is 0.57 ℃/100 m. (2) The change rate of LST in China during the study period is 0.21 ℃/10 a, and the warming area accounts for 78%, showing the spatial characteristics of "multi-core warming and axial cooling" in general. (3) LST's variation has prominent seasonal characteristics in the whole country. The spatial distribution of average value in winter and summer is quite different and fluctuates obviously; the moving trajectory of the centroid in the warming/cooling area is close to a loop shape. The movement direction shows the corresponding seasonal reverse, and the movement range in the cooling zone is larger, indicating that the regional difference and seasonal variability of the cooling zone are more obvious. (4) China's LST variation is driven by natural conditions and human activities, of which natural factors contribute more, with sunshine hours and altitude being the key factors. The boundary trend between the two dominant type areas is highly consistent with the "Heihe-Tengchong Line". The easern region is mostly dominated by human activity intensity and interacts with terrain factors, while the western region is dominated by natural factors, which enhance/weaken the change range of LST through mutual coupling with the climate, terrain, vegetation, and other factors. This study can provide scientific references for dealing with climate change, analyzing surface environmental models, and protecting the ecological environment.

Key words: land surface temperature, spatiotemporal differentiation, geodetector, dominant factor, China

TIAN Hao, LIU Lin, ZHANG Zhengyong, CHEN Hongjin, ZHANG Xueying, WANG Tongxia, KANG Ziwei. Spatiotemporal diversity and attribution analysis of land surface temperature in China from 2001 to 2020[J].Acta Geographica Sinica, 2022, 77(7): 1713-1729.

Figures/Tables 11

Tab. 1

Data introduction

数据	时段/分辨率	空间分辨率	数据来源	说明
中国生态地理分区^[36]	-	-	中国科学院资源环境科学数据中心 (http://www.resdc.cn/)	基于温度和干湿划分48个分区。为便于分析,本文在原分区基础上依据地貌特征和空间邻接关系,将其归并至38个分区(SHAPE格式,图1)。
LST (K)	2001-01—2021-02 逐月	5500 m	LAADS DAAC (https://ladsweb.nascom.nasa.gov/search/)	MOD11C3(HDF格式),经MRT软件镶嵌及重投影。
日照时数 (h)	2001—2020 逐日	1000 m	中国气象数据网 (http://data.cma.cn/)	中国833个气象站点逐日日照时数数据,剔除无效、异常值后,借助ArcGIS软件合成年数据并进行spline空间插值获得栅格数据(TIFF格式)。
降水 (mm)	2001—2020 逐年	1000 m	中国科学院资源环境科学数据中心 (http://www.resdc.cn/)	中国1980年以来逐年年平均气温空间插值数据集,基于全国2400多个气象站点日观测数据,通过整理、计算和使用ANUSPLIN插值软件处理生成(GRID格式)。
人口密度^[37] (人/km²)	2015	1000 m		中国人口空间分布公里网格数据集,基于行政单位人口统计值结合土地利用类型、夜间灯光亮度、居民点密度等数据的加权计算(GRID格式)。该数据既可表征中国人口的空间分布特征,也能定量刻画人类活动强度。
土地利用^[38]	2020	1000 m		中国多时期土地利用土地覆被遥感监测数据集(CNLUCC),基于2020年Landsat 8影像解译生成(GRID格式),一级分类体系为：耕地、林地、草地、水域、城乡/工矿/居民用地、未利用地、海洋。
DEM (m)	-	250 m		中国海拔高度(DEM)空间分布数据,基于SRTM V4.1数据重采样生成(GRID格式)。借助ArcGIS软件提取坡度、坡向和地形起伏度数据。
NDVI^[39]	2001—2018逐年	1000 m		中国年度植被指数(NDVI)空间分布数据集,基于SPOT/VEGETATION以及MODIS等数据经MVC法合成(TIFF格式)。

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编号	名称	编号	名称	编号	名称	编号	名称	编号	名称
A1	三江平原	D5	四川盆地	B1	东北东部山地	AC1	三河山麓平原丘陵	A4	淮南与长江中下游平原
A2	东北平原	E2	贵州高原	B3	青东祁连山地	C1	辽东胶东山地丘陵	A5	闽粤桂山地丘陵平原
A3	华北平原	J1	台湾诸岛	C2	鲁中山地丘陵	B2	东喜马拉雅南翼	BD1	阿尔泰山与塔城盆地
B4	藏南山地	J2	琼雷诸岛	C3	华北山地丘陵	BE1	昆仑山高山高原	CB1	江南丘陵与岭南山地
B6	阿里山地	E3	云南高原	C4	滇南谷底丘陵	E1	晋中陕北甘高原	D3	塔里木与吐鲁番盆地
B7	大兴安岭	B5	昆仑山北翼	D7	晋南关中盆地	E4	果洛那曲丘状高原	I1	川西藏东高山深谷
D2	伊犁盆地	D1	准噶尔盆地	H1	羌塘高原湖盆	EA1	内蒙古高平原
D4	汉中盆地	D6	柴达木盆地	G1	青南高原宽谷	F1	阿拉善及河西走廊

区域	相关系数	海拔梯度递减率(℃/100 m)
中国	-0.66	0.41
第一阶梯	-0.76	0.44
第二阶梯	-0.29	0.57
第三阶梯	-0.17	0.15

季节	春季	夏季	秋季	冬季
LST均温(℃)	11.080	21.020	10.050	-4.390
均温标准差	8.030	8.640	7.730	10.380
LST变化倾向率(℃/10 a)	0.450	0.164	0.080	0.157
倾向率标准差	0.045	0.048	0.038	0.050
升温区气候平均值(℃/10 a)	0.610	0.440	0.330	0.450
降温区气候平均值(℃/10 a)	-0.230	-0.360	-0.280	-0.310
升温区面积占比(%)	80.210	65.770	60.070	61.220
降温区面积占比(%)	18.790	34.230	39.930	38.780

Spatiotemporal diversity and attribution analysis of land surface temperature in China from 2001 to 2020

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