Ecological quality monitoring and evaluation is the basis and key to carrying out ecological and environmental protection work reasonably and efficiently.Ecological quality monitoring is the basis and key process for effective ecological conservations， and it also plays an important role in ecological civilization and high quality development of our society. In this study， we constructed a Remote Sensing based Ecological Index （RSEI） to monitor the spatio-temporal changes of ecological quality in the Hangzhou Greater Bay Area during 1995-2020. On the basis of which， we explored the quantitative relationship between ecological quality and land use types or conversions by coupling RSEI and land use data. The results showed that， （1） the overall ecological quality of Hangzhou Greater Bay Area first increased， then decreased， followed by an increase at the end. The RSEI maintained at 0.62， indicating that a relatively good ecological quality. The ecological quality of its belonging prefecture-level cities exhibited three different trends： decline， rise and fluctuation from north to south. （2） The area with improved ecological quality （57.5%） was greater than the one with degraded ecological quality （42.5%） in the Hangzhou Greater Bay Area. The forests， central urban areas and coastal wetlands were the hotspots for ecological quality imporvement， while the processes of rapid urbanization and tidal flat reclamation were responsible for degraded ecological environments. （3） In recent 25 years， the ecological quality of forest and build-up areas increased rapidly （△RSEI>+0.1）， but the ecological quality of cultivated land decreased significantly （△RSEI=-0.08）. This migrated the gap of ecological quality within human dominant environments， meanwile increased the ecological cost of the transformation from nature to human dominant environments. The study is expected to provide scientific basis for balancing coastal resource developments and ecological conservations， and serve for the high-quality development of coastal urban agglomerations.