MODIS-Based Spatiotemporal Inversion and Driving-Factor Analysis of Cloud-Free Vegetation Cover in Xinjiang from 2000 to 2024.

The Xinjiang Uygur Autonomous Region, characterized by its complex and fragile ecosystems, has faced ongoing ecological degradation in recent years, challenging national ecological security and sustainable development. To promote the sustainable development of regional ecological and landscape conservation, this study investigates Fractional Vegetation Cover (FVC) dynamics in Xinjiang. Existing studies often lack recent data and exhibit limitations in the selection of driving factors. To mitigate the issues, this study utilized Google Earth Engine (GEE) and cloud-free MOD13A2.061 data to systematically generate comprehensive FVC products for Xinjiang from 2000 to 2024. Additionally, a comprehensive and quantitative analysis of up to 15 potential driving factors was conducted, providing an updated and more robust understanding of vegetation dynamics in the region. This study integrated advanced methodologies, including spatiotemporal statistical analysis, optimized spatial scaling, trend analysis, and Geographical Detector (GeoDetector). Notably, we propose a novel approach combining a Theil-Sen Median trend analysis with a Hurst index to predict future vegetation trends, which to some extent enhances the persuasiveness of the Hurst index alone. The following are the key experimental results: (1) Over the 25-year study period, Xinjiang's vegetation cover exhibited a pronounced north-south gradient, with significantly higher FVC in the northern regions compared to the southern regions. (2) A time series analysis revealed an overall fluctuating upward trend in the FVC, accompanied by increasing volatility and decreasing stability over time. (3) Identification of 15 km as the optimal spatial scale for FVC analysis through spatial statistical analysis using Moran's I and the coefficient of variation. (4) Land use type, vegetation type, and soil type emerged as critical factors, with each contributing over 20% to the explanatory power of FVC variations. (5) To elucidate spatial heterogeneity mechanisms, this study conducted ecological subzone-based analyses of vegetation dynamics and drivers.