## 1 Introduction
With a growing emphasis on linking ecological quality with public health and sustainable societies, researchers increasingly explore ecological risks, such as microplastics, integrating insights from other disciplines such as hydrology and biology [10; 18]. Moreover, the substantial potential of multisource remote sensing and indicator weighting techniques in assessing ecological quality has become evident. Recent advancements in land use and vegetation parameter datasets for future scenarios serve as a foundation for formulating and adjusting land management policies based on estimated sustainable targets [19; 20]. PCA continues to exhibit outstanding advantages in reducing redundant information and dimensionality in data. In the field of ecological remote sensing, refinements, such as spatial principal component analysis, are gradually being improved and applied to enhance ecological monitoring [21]. In spite of the efforts invested in existing indices, these indicators encounter significant challenges when assessing ecological quality on a large scale, particularly in areas undergoing intense urbanization with substantial human activities. Firstly, the majority of REEI-related studies have been confined to small study areas, and the feasibility and scalability of REEI in complex regions where the ecological environment interacts with human-land relations are yet to be conclusively verified [22]. Furthermore, prevailing studies tend to focus on individual cities or small regions, limiting the applicability of established indicators due to geographical restrictions [23]. The determination of weights for ecological evaluation indicators is predominantly based on objective information, often neglecting the inherent physical significance of the indicators themselves [24; 25]. More critically, the evaluation of ecological quality in intricate ecosystems such as urban agglomerations necessitates a more comprehensive and scientifically grounded approach that considers the intricate biological components, regional climate changes, and super-numerary human activities [26; 27]. These factors collectively alter energy flow processes and environmental conditions, resulting in pronounced spatiotemporal heterogeneity of ecological quality [28]. Therefore, our research is dedicated to the development of a more comprehensive framework and the application of scientific weighting techniques to evaluate ecological quality and the impact of urbanization at the scale of urban agglomerations, aligning with a refined understanding of the concept's definition. As a concept of combining various elements, ecological quality was defined as the essential indicator measuring the healthy condition of ecosystems, implying that an ecosystem with high quality can maintain a stable structure, function, and organization despite external influences [29]. Ecosystems, as integral components of interconnected natural systems, are continually developed and utilized by humans, with material and energy being conserved during the processes of production, loss, and recovery. In alignment with this understanding, we have integrated four dimensions into evaluating ecological quality: ecological function measuring resources and production, ecological interaction quantifying influence from natural elements, ecological pressure from human activities, and ecological stability depicting resilience [30]. Given the intricate nature of the factors influencing ecological quality, a comprehensive framework, the \"Function-Interaction-Pressure-Stability\" (FIPS) model, is proposed, which incorporates various factors, including human activities, climate change, soil conditions, and land cover [31; 32]. To quantitatively assess ecological quality, we employed the PCA index weighting method, refined by the Analytic Hierarchy Process (AHP), resulting in the construction of the Ecological Quality Index (EQI). The spatiotemporal patterns of EQI were then analyzed in three urban agglomerations.