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.

The Google Earth Engine (GEE) is a cloud computing platform designed to store and process huge data sets (at petabyte-scale) for analysis and ultimate decision making

Clear sky shortwave solar radiation varies in response to altitude and elevation, surface gradient (slope) and orientation (aspect), as well as position relative to neighbouring surfaces. While the measurement of radiation flux on a relatively flat surface is straightforward, it requires a dense network of stations for mountainous terrain. The model presented here uses a digital elevation model to compute potential direct solar radiation and diffuse radiation over a large area, though the model may be modified to include parameters such as cloud cover and precipitable water content of the atmosphere. The purpose of this algorithm is for applied work in forestry, ecology, biology and agriculture where spatial variation of solar radiation is more important than calibrated values. The ability of the model to integrate radiation over long time periods in a computationally inexpensive manner enables it to be used for modelling radiation per se, or input into other hydrological, climatological or biological models. The model has been implemented for commercially available GIS (viz. Arc Info and Genasys) and is available over the Internet.