In practice, images do not have the cloud pixels removed by default, and users have access to ancillary layers (e.g., Landsat Collection 1 Level-1 Quality Assessment Band) or algorithms (e.g., SimpleCloudScore, F-Mask) and decide when they use it in their scripts. Most of the images have already been cleaned of cloud cover and have been mosaicked (by previous users) for quicker and easier processing however, original imagery is available as well and the amount of original imagery far outweighs the amount of pre-build cloud-removed mosaics. While the initial setup included remote sensing data only, large amounts of vector, social, demographic, digital elevation models, and weather and climate data layers have now been added. The complete list can be obtained from the portal webpage ( ). It also does not include most of the geophysical, demographic, and climate and weather data. The table also does not include the datasets with the spatial coverage at national and regional extents. It does not include other derived products, such as landcover and topographic features, that are available on the GEE platform.
Table A1 gives a list of various satellite-based products, including raw and pre-processed bands, indices, composites, and elevation models that have worldwide coverage.
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The data available is from multiple satellites, such as the complete Landsat series Moderate Resolution Imaging Spectrometer (MODIS) National Oceanographic and Atmospheric Administration Advanced very high resolution radiometer (NOAA AVHRR) Sentinel 1, 2, and 3 Advanced Land Observing Satellite (ALOS) etc. The data repository is a collection of over 40 years of satellite imagery for the whole world, with many locations having two-week repeat data for the whole period, and a sizeable collection of daily and sub-daily data as well.
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The Google Earth Engine (GEE) is a web portal providing global time-series satellite imagery and vector data, cloud-based computing, and access to software and algorithms for processing such data.
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. Examination of data also showed that the usage was dominated by institutions based in developed nations, with study sites mainly in developed nations. 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. The application areas were quite varied, ranging from forest and vegetation studies to medical fields such as malaria. There were also a number of papers in premium journals such as Nature and Science. The highest number of papers were in the journal Remote Sensing, followed by Remote Sensing of Environment. 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. 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. However, the uptake and usage of the opportunity remains varied and unclear. 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.
The Google Earth Engine (GEE) portal provides enhanced opportunities for undertaking earth observation studies.