近年来，随着遥感技术的快速发展，积累了海量的对地观测遥感数据。传统桌面端遥感处理平台（例如ERDAS和ENVI等）无法满足当前遥感大数据的应用需求。作为领先的遥感云计算平台，GEE（Google Earth Engine）的出现改变了传统遥感数据处理和分析模式，为海量数据快速处理与信息挖掘带来了新的契机。截止目前，科研人员已利用GEE成功开展了大量研究工作，发表多篇学术论文与综述。然而，还没有一项研究系统地分析GEE是如何推动遥感科学发展的。因此，本文旨在探讨GEE云平台相比于传统桌面端遥感处理平台分别在资源、方法和应用3个方面的创新性变革：（1）在资源方面，GEE以其集大数据/云计算为一体的特点，打破了传统数据、模型算法、算力分离的局面，实现上述3者的云端部署，在大规模数据快速处理与分析方面展现出巨大潜力；（2）在方法方面，GEE提供的遥感分析新方法，突破了传统遥感技术瓶颈，促进了遥感数据处理与分析的技术革新，极大提高了海量数据处理与信息挖掘效率；（3）在应用方面，GEE不仅为全球尺度的长时间序列快速分析带来发展机遇，而且推动了数据、算法和产品的快速共享，进一步迎来了开放、共享的遥感时代。通过系统归纳总结GEE平台的优势，不仅可以帮助潜在新用户了解GEE，同时加深现有用户的理解，还能促使谷歌开发人员完善和改进GEE，并且催生地球系统科学研究的新发现。

The rapid development of remote sensing technology has enabled accumulation of massive earth observation data in recent years. Traditional desktop-based remote sensing platforms (e.g., ERDAS and ENVI) cannot satisfy the current application requirements of remote sensing big data. Google Earth Engine (GEE), as a leading cloud-based remote sensing platform, has not only changed the traditional processing and analysis means of data but also brought new opportunities for the rapid processing and information mining of massive data. To date, many researchers have successfully conducted a large number of works and published many academic papers and reviews based on GEE. However, no one has systematically analyzed how GEE promotes the development of remote sensing science. Therefore, this study aims to explore the innovative changes caused by GEE in the aspect of resources, methods, and applications, compared with the traditional desktop-based platform. (1) In terms of resources, GEE breaks the separation of traditional data, model algorithms, and computing power, realizes cloud deployment of the three, and shows great potential in the rapid processing and analysis of massive data through combining big data and cloud computing together. (2) With regard of methods, innovative remote sensing analysis methods provided by GEE break through the bottleneck of traditional remote sensing technology and promote the technological innovation of data processing and analysis. Thus, the efficiency of massive data processing and information mining greatly improves. (3) In the perspective of applications, GEE not only brings development opportunities for the rapid global-scale long-time analysis but also promotes the rapid sharing of data, algorithms, and products, which further ushers in the era of opening and sharing remote sensing. Systematically summarizing the advantages of GEE will not only help the potential new users understand GEE as well as deepen the existing users’ understanding but also encourage Google developers to perfect and improve GEE while catalyzing new discoveries in the scientific research of the earth system.