Time-Series MODIS-Based Remote Sensing and Explainable Machine Learning for Assessing Grassland Resilience in Arid Regions

作者：Ruihan Liu, Yang Yu, Ireneusz Malik, Małgorzata Wistuba, Zengkun Guo, Yuanbo Lu, Xiaoyun Ding, Jing He, Lingxiao Sun, Chunlan Li, Ruide Yu · 发表于：Remote Sensing · 年份：2025 · DOI：10.3390/rs17162749 · 被引用次数：2 · 研究领域：Ecosystem dynamics and resilience、Rangeland Management and Livestock Ecology、Remote Sensing in Agriculture

Grassland ecosystems in arid regions increasingly experience resilience loss due to intensifying climatic variability. However, the limited interpretability of conventional machine learning models constrains our understanding of underlying ecological drivers. This study constructs an integrative framework that combines temporal autocorrelation (TAC) metrics with explainable machine learning, employing Random Forest and SHAP (SHapley Additive exPlanations) analysis. Time series of satellite-derived vegetation indices from MODIS (2001–2023), particularly the kernel Normalized Difference Vegetation Index (KNDVI), support the generation of TAC and its trend-based derivative δTAC. The framework assesses ecosystem resilience across seven representative grassland types in Xinjiang, capturing diverse responses to climate variability and vegetation dynamics. Results reveal pronounced spatial heterogeneity: resilience declines in radiation-stressed arid zones, while hydrothermally stable regions maintain stronger recovery capacity. Key drivers include temperature variability and vegetation dynamics, with divergent effects among grassland types. Meadow and Typical Steppe exhibit higher resilience under stable hydrothermal regimes, whereas desert and alpine systems show greater sensitivity to warming and climatic fluctuations. This framework enhances diagnostic transparency and ecological insight, offering a spatially explicit, data-driven tool for resilience monitoring. The findings sup...