Google Earth Engine Applications Since Inception: Usage, Trends, and Potential

The Google Earth Engine (GEE) portal provides enhanced opportunities for undertaking earth observation studies. Established towards the end of 2010, it provides access to satellite and other ancillary data, cloud computing, and algorithms for processing large amounts of data with relative ease. However, the uptake and usage of the opportunity remains varied and unclear. This study was undertaken to investigate the usage patterns of the Google Earth Engine platform and whether researchers in developing countries were making use of the opportunity. Analysis of published literature showed that a total of 300 journal papers were published between 2011 and June 2017 that used GEE in their research, spread across 158 journals. The highest number of papers were in the journal Remote Sensing, followed by Remote Sensing of Environment. There were also a number of papers in premium journals such as Nature and Science. The application areas were quite varied, ranging from forest and vegetation studies to medical fields such as malaria. Landsat was the most widely used dataset; it is the biggest component of the GEE data portal, with data from the first to the current Landsat series available for use and download. Examination of data also showed that the usage was dominated by institutions based in developed nations, with study sites mainly in developed nations. There were very few studies originating from institutions based in less developed nations and those that targeted less developed nations, particularly in the African continent.

[1]  Stuart R. Phinn,et al.  Mapping woody vegetation clearing in Queensland, Australia from Landsat imagery using the Google Earth Engine , 2015 .

[2]  Françoise Salager-Meyer,et al.  Scientific Publishing in Developing Countries: Challenges for the Future. , 2008 .

[3]  Noel Gorelick,et al.  Google Earth Engine , 2012 .

[4]  Jagdish N. Sinha.  Scientific Communities in the Developing World , 1999 .

[5]  Olivier Leo,et al.  Crop mapping applications at scale: Using Google Earth Engine to enable global crop area and status monitoring using free and open data sources , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[6]  Yuqi Bai,et al.  Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine , 2017 .

[7]  A. Niekerk,et al.  A New GISc Framework and Competency Set for Curricula Development at South African Universities , 2014 .

[8]  R. Congalton,et al.  Automated cropland mapping of continental Africa using Google Earth Engine cloud computing , 2017 .

[9]  J. Muñoz,et al.  [Lost Science in the Third World]. , 1996, Gaceta medica de Mexico.

[10]  Jie Xiong.  Cloud Computing for Scientific Research , 2018 .

[11]  Kotaro Iizuka,et al.  Employing crowdsourced geographic data and multi-temporal/multi-sensor satellite imagery to monitor land cover change: A case study in an urbanizing region of the Philippines , 2017, Comput. Environ. Urban Syst..

[12]  Onisimo Mutanga,et al.  Remote sensing of crop health for food security in Africa: Potentials and constraints , 2017 .

[13]  Ladislav Hluchý,et al.  A roadmap for a dedicated Earth Science Grid platform , 2010, Earth Sci. Informatics.

[14]  Michael Dixon,et al.  Google Earth Engine: Planetary-scale geospatial analysis for everyone , 2017 .

[15]  Onisimo Mutanga,et al.  Detecting Sirex noctilio grey-attacked and lightning-struck pine trees using airborne hyperspectral data, random forest and support vector machines classifiers , 2014 .

[16]  D. Timothy,et al.  Quantifying aboveground biomass in African environments : A review of the trade-offs between sensor estimation accuracy and costs , 2016 .

[17]  Onisimo Mutanga,et al.  Progress in the remote sensing of C3 and C4 grass species aboveground biomass over time and space , 2016 .

[18]  Emma Izquierdo-Verdiguier,et al.  A Cloud-Based Multi-Temporal Ensemble Classifier to Map Smallholder Farming Systems , 2018, Remote. Sens..

[19]  Justin L. Huntington,et al.  Climate Engine: Cloud Computing and Visualization of Climate and Remote Sensing Data for Advanced Natural Resource Monitoring and Process Understanding , 2017 .

[20]  C. Justice,et al.  High-Resolution Global Maps of 21st-Century Forest Cover Change , 2013, Science.

[21]  M. Hansen,et al.  Google Earth Engine: a new cloud-computing platform for global-scale earth observation data and analysis , 2011 .

[22]  Manuel Martínez-Bueno,et al.  Scientific Publication Trends and the Developing World , 2000, American Scientist.

[23]  Franklin G. Horowitz.  MODIS Daily Land Surface Temperature Estimates in Google Earth Engine as an Aid in Geothermal Energy Siting , 2015 .

[24]  Ricardo B. Duque,et al.  Collaboration Paradox , 2005 .

[25]  Paul A. Longley,et al.  The emergence of geoportals and their role in spatial data infrastructures , 2005, Comput. Environ. Urban Syst..