Google Earth Engine (GEE) is a cloud-based geospatial processing platform for large-scale environmental monitoring and analysis. The free-to-use GEE platform provides access to (1) petabytes of publicly available remote sensing imagery and other ready-to-use products with an explorer web app; (2) high-speed parallel processing and machine learning algorithms using Google’s computational infrastructure; and (3) a library of Application Programming Interfaces (APIs) with development environments that support popular coding languages, such as JavaScript and Python. Together these core features enable users to discover, analyze and visualize geospatial big data in powerful ways without needing access to supercomputers or specialized coding expertise. The development of GEE has created much enthusiasm and engagement in the remote sensing and geospatial data science fields. Yet after a decade since GEE was launched, its impact on remote sensing and geospatial science has not been carefully explored. Thus, a systematic review of GEE that can provide readers with the “big picture” of the current status and general trends in GEE is needed. To this end, the decision was taken to perform a meta-analysis investigation of recent peer-reviewed GEE articles focusing on several features, including data, sensor type, study area, spatial resolution, application, strategy, and analytical methods. A total of 349 peer-reviewed articles published in 146 different journals between 2010 and October 2019 were reviewed. Publications and geographical distribution trends showed a broad spectrum of applications in environmental analyses at both regional and global scales. Remote sensing datasets were used in 90% of studies while 10% of the articles utilized ready-to-use products for analyses. Optical satellite imagery with medium spatial resolution, particularly Landsat data with an archive exceeding 40 years, has been used extensively. Linear regression and random forest were the most frequently used algorithms for satellite imagery processing. Among ready-to-use products, the normalized difference vegetation index (NDVI) was used in 27% of studies for vegetation, crop, land cover mapping and drought monitoring. The results of this study confirm that GEE has and continues to make substantive progress on global challenges involving process of geo-big data.

Google Earth Engine（GEE）是一个基于云的地理空间处理平台，用于大规模环境监控和分析。免费使用的GEE平台可通过Explorer Web应用程序访问（1）PB的公共遥感图像和其他即用型产品；（2）使用Google的计算基础架构的高速并行处理和机器学习算法；（3）具有开发环境的应用程序编程接口（API）库，这些开发环境支持流行的编码语言，例如JavaScript和Python。这些核心功能共同使用户能够以强大的方式发现，分析和可视化地理空间大数据，而无需访问超级计算机或专业的编码专家。GEE的发展在遥感和地理空间数据科学领域引起了极大的热情和参与。然而，自GEE推出以来已有十年，但尚未仔细研究其对遥感和地理空间科学的影响。因此，需要对GEE进行系统的审查，以便为读者提供GEE的现状和总体趋势的“全景”。为此，决定对最近经过同行评审的GEE文章进行荟萃分析研究，重点关注几个功能，包括数据，传感器类型，研究区域，空间分辨率，应用，策略和分析方法。在2010年至2019年10月期间，共对146种不同期刊上发表的349篇经同行评审的文章进行了审阅。出版物和地理分布趋势显示了在区域和全球范围内环境分析中的广泛应用。90％的研究使用了遥感数据集，而10％的文章使用了现成的产品进行分析。具有中等空间分辨率的光学卫星图像，尤其是存档超过40年的Landsat数据，已得到广泛使用。线性回归和随机森林是用于卫星图像处理的最常用算法。在现成的产品中，有27％的研究使用归一化植被指数（NDVI）进行植被，作物，土地覆盖图和干旱监测。这项研究的结果证实，GEE在涉及地理大数据处理的全球挑战方面已经并将继续取得实质性进展。