Improving Remote Sensing Ecological Assessment in Arid Regions: Dual-Index Framework for Capturing Heterogeneous Environmental Dynamics in the Tarim Basin

Monitoring ecosystem dynamics in arid regions requires robust indicators that can capture spatial heterogeneity and diverse ecological drivers. In this study, we introduce and evaluate two novel ecological indices: the Arid-region Remote Sensing Ecological Index (ARSEI), specifically designed for desert environments, and the Composite Remote Sensing Ecological Index (CoRSEI), which integrates both desert and non-desert systems. These indices are compared with the traditional Remote Sensing Ecological Index (RSEI) in the Tarim River Basin from 2000 to 2023. Principal component analysis (PCA) revealed that RSEI maintained the highest structural compactness (average PCA1 = 87.49%). In contrast, ARSEI (average PCA1 = 78.62%) enhanced sensitivity to albedo and vegetation (NDVI) in arid environments. Spearman correlation analysis further demonstrated that ARSEI was more strongly correlated with NDVI (ρ = 0.49) and precipitation (ρ = 0.62) than RSEI, confirming its improved responsiveness under water-limited conditions. CoRSEI exhibited higher internal consistency and spatial adaptability (mean values ranging from 0.45 to 0.56), with slight ecological improvements observed between 2000 and 2023. Ecological drivers varied across habitat types. In desert areas, evapotranspiration, precipitation, and soil moisture were the main determinants of ecological status, showing high coupling and synchrony. In non-desert regions, soil moisture and precipitation remained dominant, but vegetation indices and disturbance factors (e.g., fire density) exerted stronger long-term influences. Partial dependence analyses further confirmed nonlinear, region-specific responses, such as the threshold effects of precipitation on vegetation growth. Overall, our findings highlight the importance of differentiated ecological modeling. ARSEI enhances sensitivity in desert ecosystems, whereas CoRSEI captures landscape-scale variability across desert and non-desert regions. Both indices contribute to more accurate long-term ecological assessments in hyper-arid environments.