A total of 349 peer-reviewed articles published in 146 different journals between 2010 and October 2019 were reviewed. 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. 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. Yet after a decade since GEE was launched, its impact on remote sensing and geospatial science has not been carefully explored. The development of GEE has created much enthusiasm and engagement in the remote sensing and geospatial data science fields. 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 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. Google Earth Engine (GEE) is a cloud-based geospatial processing platform for large-scale environmental monitoring and analysis.
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