###### Abstract
For instance, some studies focus on landscape change patterns or ecological functions in one aspect [20; 24] but still need to fill the gap in different aspects. Second, connectivity and integrity are of great importance to the ecosystem [25], but it is challenging to consider the spatio-temporal continuous ES in recent identification methods. Hence, integrating various perspectives, including ecological functions and landscape patterns, and extending spatio-temporal evolutionary modeling approaches for ES identification present significant challenges and significance. ESV assessments are a critical step in ES research that affect the accuracy of ES identification. The equivalent factor method has been frequently used in ESV assessments because it requires fewer elements and is applicable on a regional scale [26]. More improvements in the equivalent factor method are required for integrating perspectives of ecosystem service into the ES identification framework. Although most studies have used diverse components (e.g., NPP, precipitation) to correct the equivalent factor method [27; 28], fewer studies have fully integrated ecological quality, carbon, and landscape aesthetic characteristics into the ESV revision. The inclusion of ecological quality elements into ESV can provide relevant insights for identifying ES spatio-temporal dynamic changes [29]. Carbon sequestration maintains healthy ecosystems and serves many benefits for human beings [30; 31]. Meanwhile, ES also may provide nonmaterial contributions through recreation and aesthetic experiences but faces exceptional challenges to be integrated concretely and comprehensively into ES identification [32]. To thoroughly consider the multiple ecosystem characteristics, ecological quality, carbon sinks, and landscape aesthetics are urgently needed in an integrated adjustment of ESV to identify ES. Vegetation productivity also plays an important role in ES identification [33]. As an essential ecosystem element, vegetation productivity stands out as highly responsive to climate change and human activities, offering a visible representation of shifts in ecological quality across a region [34; 35]. Despite vegetation productivity being important and widely used in different ecosystem processes [36; 37], integrating it into the ES extraction method has yet to be addressed. The normalized difference vegetation index (NDVI) offers extensive, multi-scale ecosystem monitoring and long-term vegetation trend information, being employed as a proxy for vegetation productivity [38]. However, it is also troubled by saturation effects for higher vegetation cover areas [39]. It is vital and urgent to consider a more suitable vegetation productivity proxy factor comprehensively. In contrast to the seasonal oversaturation effect of NDVI, the kernel normalized difference vegetation index (kNDVI), proposed by [PERSON] [40], is more suitable for modeling across multiple spatio-temporal scales. Moreover, it exhibits a certain level of correlation with the productivity parameters of plants [41; 42]. Given that, it is worth considering incorporating kNDVI into the ecological source identification research framework. Existing studies have discussed the importance of ESV and vegetation productivity in the ES identification framework, respectively, and the relationship between the vegetation condition and ecosystem service has been widely established [43]. However, the interaction and combination between ESV and vegetation productivity were often overlooked, which would be detrimental to understanding the overall ecological process interactions. Therefore, it is important to explore whether the combination of ESV and vegetation productivity can lead to more valuable outcomes of ES identification. To solve the problems mentioned above, the primary objectives of this study were as follows: (1) Consider ESV as a pivotal factor in ES identification and revise it from multiple perspectives (landscape aesthetics, carbon sink characteristics, and ecological quality).