## 4 Discussion
### Limitations and Future Research Directions  
The RESI evaluation's accuracy has been validated both qualitatively and quantitatively. Qualitative comparisons with 2020's EI results, depicted in Figure 2, show that RESI correlates more closely with geographic locations, offering a refined view of the ecological environment's spatiotemporal variations and providing essential data for analyzing driving forces. For a quantitative accuracy assessment, we conducted ecological environmental surveys in June 2023, examining the ecological conditions and vegetation cover at 12 locations in the Qinling region, with specifics detailed in Supplementary Table S1. Despite the time gap, these surveys reveal consistent patterns. By comparing the surveyed ecological grades with RSEI's assessments via a confusion matrix, we determined an accuracy rate of 83.3% for RSEI in remote sensing ecological evaluations. Although qualitative and quantitative evaluations indicate that the accuracy of the RSEI is generally sufficient, certain limitations must be acknowledged. RSEI's reliance on remote sensing methods introduces a notable sensitivity to the timing of image captures. Variability in the dates of image acquisition can induce significant discrepancies, with potential errors in RSEI of up to 19.2% within a one-month period [63]. Furthermore, the demand for cloud-free images over large areas exacerbates the issue of the data revisit period. This study has also employed a commonly used method to address this issue by using median calculations with data from the target year combined with the two years before and after [64]. However, it is inevitable that annual RSEI values may still fluctuate significantly between adjacent years. In terms of improving temporal resolution, MODIS imagery with its 16-day revisit period has been utilized to monitor ecological quality over large areas, although this approach inevitably loses some detail due to the inherent spatial resolution limitations [65]. The recently developed Continuous Change Detection and Classification (CCDC) technique fully leverages the available observations in time-series imagery [63] and enables the synthesis of images at any given time [66]. Therefore, integrating the strengths of multi-source remote sensing data and enhancing algorithms to optimize temporal and spatial resolution for obtaining continuous time-series images over large areas is a direction for achieving more precise and continuous ecological quality monitoring. The ecological environment is complex, making it difficult to measure with a single indicator. The current RSEI index, which includes four indicators, cannot fully represent the ecological state of a region. For example, it does not consider the impact of water on the ecological environment, because RSEI is not suitable for assessing the ecological quality of water [59]. Water is an indispensable component of the watershed ecological environment, and future research should further incorporate the ecological effects of water into the regional ecological environmental assessment system. Despite its limitations, it is among the most comprehensive ecological assessment tools available [67]. The Qinling region's uniform vegetation and consistent geomorphology lend credibility to the RSEI assessment results in this study. However, to further improve the accuracy of the assessment, scholars are considering the introduction of more diversified spatial data indicators, such as gross primary product (GPP), land aerosol optical depth (AOD), et al. [57]. In addition, some researchers have demonstrated that the improved RSEI with sharpened LST imagery can effectively quantify regional ecological status, which is also a direction for improving assessment precision [65]. Therefore, it is crucial to develop and optimize new evaluation indicators for regions with distinct characteristics, and to establish a more rational, scientific, and comprehensive ecological environmental quality assessment system.