Abstract: Rapid urbanization has a stressful effect on regional ecological environment. Timely monitoring of ecolo-gical quality changes is thus crucial for urban ecological management and planning. The remote sensing-based ecological index (RSEI) is an objective, quick, and easy tool for monitoring and evaluating ecological quality, which has been frequently used in ecological research. However, large-scale and long-term monitoring is frequently confronted with the problem of cloud blockage. Based on Google Earth Engine (GEE) cloud platform and its massive MODIS remote sensing images, we calculated remote sensing indices of greenness, humidity, dryness, and heat of Chengdu-Chongqing Urban Agglomeration (CCUA) annually by using the median synthesis method, and constructed the RSEI using the principal component analysis to evaluate the temporal and spatial variations of ecological quality in this area in the past 20 years. The results showed that: (1) The RSEI of the CCUA showed a slow upward trend and remained stable since 2012. The ecological quality in hilly area of central Sichuan and Chengdu Plain had been deteriorated dramatically, and were significantly improved in mountainous areas of the northeastern Chongqing; (2) The changes in the CCUA’s ecological quality showed clear spatial autocorrelation. The Moran’s I index was 0.825. Greenness and wetness indices were positively correlated with RSEI, while heat and dryness indices were negatively correlated with it, and the greenness was the primary driver for the changes in ecological quality of the CCUA. (3) Image processing based on GEE cloud computing may considerably enhance image processing efficiency, and address the problem of remote sensing data missing due to overcast and wet conditions. It also has the potential to broaden the scope of RSEI’s use in large-scale and long-term sequence ecological monitoring. Our findings can be used as a theoretical basis for environmental conservation and land management under the context of rapid urbanization.

Key words: Chengdu-Chongqing Urban Agglomeration, Google Earth Engine, ecological quality, remote sensing ecological index.