A time-series-based remote sensing ecological index model for sustainable long-term ecological monitoring of desertification

• Identified the source of instability in traditional RSEI time-series analysis. • Developed a robust TRSEI model to enhance time-series stability. • Proposed an automated correction method for RSEI inverse value issues. • Autumn is optimal for desertification monitoring, while summer decisively impacts ecology. • Demonstrated that dryness is the dominant factor driving LSEQ variations. Desertification threatens global ecosystems and land productivity, making timely monitoring essential for ecological protection and sustainability. The remote sensing ecological index (RSEI) is widely used to assess land surface ecological quality (LSEQ), but its instability in time-series analysis remains a challenge. This study investigates the causes of RSEI instability and proposes an improved time-series-based TRSEI model for LSEQ monitoring. Additionally, dynamic time warping (DTW) is used to identify the most representative season, simplifying calculations. Geodetector is applied to analyze the main driving forces of LSEQ change. The results show that the normalized RSEI remains relatively stable in representing LSEQ over individual periods, but its stability is compromised due to the influence of extreme values introduced during normalization. In contrast, the proposed TRSEI preserves the purity of each pixel’s information, thereby enabling a more accurate reflection of ecological changes by transferring normalization and principal component analysis (PCA) from the spatial to the temporal scale. The DTW analysis reveals that the TRSEI trend in autumn closely mirrors interannual changes, making autumn the best season for representing ecological variation in desert regions. However, summer, along with changes in dryness, emerged as the dominant factor driving LSEQ variations. Overall, the TRSEI time-series analysis provides a more reliable method for dynamic monitoring of the LSEQ, offering stronger support for decision-making in ecological protection and sustainable development.