Abstract: Using the Fuxian Lake basin in Yunnan Province as a case study, this study quantitatively revealed the spatiotemporal evolution patterns and driving mechanisms of ecological environment quality in typical highland deep-water lakes. Based on Landsat series remote sensing data from 2000 to 2021, we constructed a Remote Sensing Ecological Index (RSEI) and conducted modelling analysis using Geodetector. Results indicated that the RSEI model demonstrated high applicability within the Fuxian Lake basin, with the first principal component (PC1) consistently contributing over 65% of variance. The loadings of all indicators aligned with ecological theory (greenness and wetness are positively correlated; aridity and thermal intensity are negatively correlated). The basin's ecological environment quality exhibited a three-stage evolution pattern of "degradation-improvement-stabilisation": significant degradation occurred between 2000 and 2006 (declining RSEI mean), gradual improvement followed from 2006 to 2015 (the proportion of significantly improved areas rising to 5.5%), and a stabilisation phase commenced post-2015 (with areas showing no significant change accounting for 32.6%). Spatially, a gradient distribution pattern centred on the lake body was observed. Moran's I index (0.25-0.33) indicated a weak positive spatial correlation in ecological quality, with areas of high ecological quality (RSEI > 0.6) significantly expanding after 2015. Driving factor analysis indicated that annual precipitation (impact factor q=0.418) and leaf area index (q=0.411) were dominant factors, with their interaction (annual precipitation ∩ leaf area index, q=0.701) exerting the strongest influence on spatial differentiation of ecological quality. Contributions from human activities (e.g., night-time light index) had progressively increased annually. This study validates the applicability of the RSEI model in deep-water lake basins and reveals the spatiotemporal heterogeneity of ecological environment quality driven by multiple synergistic factors.