Abstract

The Yellow River Basin (YRB), a core ecological–economic corridor in China, plays a critical role in meeting climate, land, and water-related Sustainable Development Goals (SDGs). To capture the dynamic evolution of Eco-environmental Quality (EEQ), this study developed a process-oriented space-time cube framework based on the Remote Sensing Ecological Index (RSEI) using MODIS data from 2000 to 2021 on the Google Earth Engine (GEE) platform. By integrating Emerging Hotspot Analysis (EHSA) with the RSEI cube, we identified and tracked 17 EEQ hotspot–coldspot evolution types and revealed their spatial clustering, temporal persistence, and cumulative transition effects. The driving mechanisms were further investigated through a comprehensive analytical framework combining Geodetector (GD), Ordinary Least Squares (OLS), and Multiscale Geographically Weighted Regression (MGWR). Results show that: (1) the RSEI-based space–time cube effectively detects co-evolving hotspot–coldspot trajectories and uncovers fine-grained spatiotemporal instability across the basin; (2) dynamic hotspot types, including diminishing, oscillating, and sporadic patterns, occupy a larger proportion of the basin than persistent hotspots, indicating widespread ecological fluctuation; (3) hotspot transition analysis reveals that only 4.94% of the 17.04% emerging hotspots became stable from 2000 to 2010, to 2011 to 2021, while the majority remained transitional or reverted to coldspots, reflecting fragile early-stage ecological recovery; and (4) Fractional vegetation cover (FVC) exhibited the highest explanatory power for EEQ hotspot–coldspot transition dynamics, followed by topography and temperature, whereas anthropogenic factors exerted their influence primarily through interactions with natural factors. These findings provide a robust scientific basis for precision ecological restoration, risk identification in fragile ecological zones, and adaptive sustainable management of large river basins under complex environmental pressures.

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