【摘要】 聚焦中国西南地区这一关键碳汇区域，利用谷歌地球引擎(GEE)平台的MOD13 A1V6数据，构建了2002—2022年，西南五省(重庆市、四川省、贵州省、云南省、西藏自治区)核归一化差异植被指数(kNDVI)数据集，综合分析了西南地区植被覆盖的时空变化。并结合气象数据，采用一元线性回归、泰尔-森(Theil-Sen)中位数趋势分析、曼-肯德尔(Mann-Kendall)检验及偏相关分析等方法，探讨了气候变化对植被动态的影响。结果显示：(1)2002—2022年，西南地区整体植被覆盖状况较好，植被覆盖度呈现“东南高、西北低”的空间分布格局，且植被覆盖度整体呈上升趋势，线性拟合曲线为y=0.001x-1.691(R~2=0.806)。(2)核归一化差异植被指数与气温和降水的相关性在空间分布上存在异质性，但总体上气候因素对核归一化差异植被指数的影响以正向促进作用为主，气候驱动的区域主要集中在西藏东部、四川、重庆、贵州和云南部分地区。更多还原

【Abstract】 Focusing on the crucial carbon sink region of Southwest China, a kernel normalized difference vegetation index(kNDVI) dataset for the five southwestern provinces(Chongqing, Sichuan, Guizhou, Yunnan, and the Tibet Autonomous Region) was constructed using MOD13 A1V6 data from the Google Earth Engine(GEE) platform for the period from 2002 to 2022. This dataset was used to comprehensively analyze the temporal and spatial variations of vegetation cover in the Southwest. Additionally, meteorological data were incorporated, and methodologies such as univariate linear regression, Theil-Sen median trend analysis, Mann-Kendall test, and partial correlation analysis were employed to explore the impact of climate change on vegetation dynamics. The results showed that:(1) From 2002 to 2022, the overall vegetation cover in the Southwest region exhibited a favorable status, with a spatial distribution pattern of vegetation coverage showing “higher in the southeast and lower in the northwest”. Overall, vegetation cover exhibited an upward trend, with the linear fit curve represented as y=0.001x-1.691(R~2=0.806).(2) The correlation between the kernel normalized difference vegetation index and temperature and precipitation displayed spatial heterogeneity. However, overall, climate factors primarily exerted a positive influence on the kernel normalized difference vegetation index. Climate-driven regions were mainly concentrated in eastern Tibet, as well as parts of Sichuan, Chongqing, Guizhou, and Yunnan.更多还原