What is going on within Google Earth Engine? A Systematic Review and Meta-Analysis

Google Earth Engine (GEE) is a geospatial processing platform based on geo-information applications in the 'cloud'. This platform provides free access to huge volumes of satellite data for computing, and offers support tools to monitor and analyse environmental features on a large scale. Such facilities have been widely used in numerous studies about land management and planning. Considering the current lack of relevant overviews, it may be useful to evaluate the utilization paths of GEE and its impact on the scientific community. For this purpose, a systematic review has been conducted using the PRISMA methodology based on 343 articles published from 2020 to 2022 in high-impact scientific journals, selected from the Scopus and Google Scholar databases. After an overview of the publishing context, an analysis of the frequency of satellite features, processing methods, applications are carried out, and a special attention is given to the COVID-19 studies. Finally, the geographical distribution of the reviewed articles is evaluated, and the citation impact metrics is analysed. On a bibliometric approach, 90 journals published articles on GEE in the reference period (January 2020 to April 2022), and this large number of journals reveals the multidisciplinary application of GEE platform as well as the interest of publishers towards this topic of relevance for the international scientific community. The results of the meta-analysis following the systematic review showed that: (i) the Landsat 8 was the most widely-used satellite (25%); (i) the non-parametric classification methods, mainly Random Forest, were the most recurrent algorithms (31%); and (iii) the water resources assessment and prediction were the most common methodological applications (22%). A low number of articles about COVID-19, in spite of the planetary importance of the pandemic effects. The reviewed articles were geographically distributed among 86 countries, China, United States, and India accounting for the large number. 'Remote Sensing' and 'Remote Sensing of Environment' were the leading journals in the citation impact metrics, while the Random Forest method and the agriculture-related applications being the mostly cited. It is expected that these results might change over the mid to long term, due to fast progress in environmental and spatial information technologies, although currently our findings may be worthwhile and useful for assessing the current global deployment of GEE platform.