Surface coal mining activities usually bring negative impacts on the surrounding ecological environment. However, surface coal mines are widely distributed in different climatic and geomorphological conditions, and are characterized by various land use/cover. It leads to the lack of efficient methods for evaluating the ecological quality of surface coal mines in different climatic and geomorphological regions. To efficiently meet the need for large-scale ecological environmental quality assessments of various surface coal mines, this paper proposes a Surface coal Mine Ecological Index (SurMEI) for evaluating the ecological environment quality for various surface coal mines, which can be automatically realized in Google Earth Engine (GEE). The realization of SurMEI consists of four steps: First, the Dynamic World dataset was used to classify the land covers of surface coal mines. Second, considering the characteristics of different land covers, 10 representative indicators suitable for cloud computing were selected and calculated. Third, the indicators were normalized to avoid the impact of different value ranges on weight settings. Fourth, the selected indicators were integrated to generate SurMEI. The effectiveness of SurMEI was demonstrated by assessing the ecological environment quality of four coal bases located in different natural climatic and geomorphological regions, as well as six typical surface coal mines. The results show that: (1) The SurMEI effectively integrates comprehensive information from 10 indicators. The SurMEI ranking of surface coal mines in different climatic and geomorphic region is as follows: Chaoyang mine (0.83, located in Sanjiang Plain, a cold-temperate continental monsoon climate) > Xiaolongtan mine (0.66, located in Yunnan, a subtropical monsoon climate) > Baorixile mine (0.65, located in the Inner Mongolia grassland region, a sub-cold continental monsoon climate) > Zhonglian Runshi mine (0.32, located in the Gobi region of Xinjiang, a temperate continental arid climate). The spatio-temporal differences of SurMEI under different natural climatic and geomorphological conditions are comparable, which is more suitable for comprehensively evaluating the ecological environment quality in various surface coal mines in different locations. (2) Compared with existing ecological indices, the SurMEI provides more valuable information of spatial variations for ecological environment quality, and performs well with different land use/cover within surface coal mines, especially water collection ponds. (3) SurMEI achieves automated computation in Google Earth Engine (GEE) and is capable of highly efficient application for a number of surface mines at a large scale. Overall, the SurMEI proposed in this paper is of significant practical importance for achieving continuous monitoring of ecological environment quality for various surface coal mines at a national or global scale.