The impact of human activities on the ecological environment is becoming more intense, and timely dynamic monitoring of the ecological status and change information is of great significance to urban ecological management, protection and sustainable development. The remote sensing-based ecological index (i.e. RSEI) is an objective, fast and simple ecological quality monitoring and evaluation method and has been widely applied in the field of ecological studies. But it often faces the problems of cloud occlusion and difficulty in mosaic when conducting large-scale and long-term monitoring. Thus, in this study, we collected 3530 Landsat images from 1988 to 2018 over the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) and removed their clouds on Google Earth Engine(GEE) platform. The median value composite was adopted to calculate remote sensing indicators such as greenness, wetness, dryness and heat year by year and the RSEI was constructed using principal component analysis to evaluate the temporal and spatial changes of ecological quality in the region over the past 30 years. The method ameliorates RSEI in terms of the problems of data missing and image mosaic for large-scale and long-term monitoring, and increases the comparability of time series. The results show that: (1) the RSEI can better characterize the ecological quality of the GBA, in which greenness and wetness indicators have a positive correlation with RSEI, while dryness and heat indicators are negatively correlated with it. (2) In perspective of time, the ecological quality of GBA has shown a fluctuating downward trend of "up-down-up-down" in the past 30 years. In perspective of space, the ecological quality has presented obviously spatial heterogeneity, mainly showing the state of high in the northwest and northeast and low in the middle. The severe and moderately degraded areas are mainly concentrated in the central area, and the overall improvement areas are mainly located in the west and northeast. The unchanging areas mainly include the northern area and the Hong Kong, and the lightly degraded areas are scattered. (3) When using the RSEI, image processing of median composite based on the GEE cloud computing can better improve the problems of remote sensing image data missing, chromatic aberration and time inconsistency, greatly improve the efficiency of image processing, and extend the remote sensing ecological index in a large-scale and long-term sequence of ecology monitoring application. The research results can provide a reference for improving the scope and accuracy of the RSEI, and provide a theoretical basis for ecological protection and land management in the context of rapid urbanization.