Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

@article{Amani2020GoogleEE,
  title={Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review},
  author={Meisam Amani and Arsalan Ghorbanian and Seyed Ali Ahmadi and Mohammad Kakooei and Armin Moghimi and S. Mohammad Mirmazloumi and Sayyed Hamed Alizadeh Moghaddam and Sahel Mahdavi and Masoud Ghahremanloo and Saeid Parsian and Qiusheng Wu and Brian Brisco},
  journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year={2020},
  volume={13},
  pages={5326-5350}
}
  • M. Amani, A. Ghorbanian, B. Brisco
  • Published 1 September 2020
  • Computer Science, Environmental Science
  • IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Remote sensing (RS) systems have been collecting massive volumes of datasets for decades, managing and analyzing of which are not practical using common software packages and desktop computing resources. In this regard, Google has developed a cloud computing platform, called Google Earth Engine (GEE), to effectively address the challenges of big data analysis. In particular, this platform facilitates processing big geo data over large areas and monitoring the environment for long periods of… 
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85 Citations
Highly Influential Citations
4
Background Citations
25
Methods Citations
22

85 Citations

Leveraging Google Earth Engine platform to characterize and map small seasonal wetlands in the semi-arid environments of South Africa.
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Using Open Remote Sensing Data to build an Agriculture Big Data System
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The current open datasets and how these datasets can be used to solve various problems in agriculture are discussed and a cloud-based scalable agricultural information system which provides actionable insights to farmers is discussed.
Downscaling MODIS Land Surface Temperature Product Using an Adaptive Random Forest Regression Method and Google Earth Engine for a 19-Years Spatiotemporal Trend Analysis Over Iran
TLDR
The outcomes of this study not only exhibit the spatiotemporal trends of LST across Iran, but also reveal the substantial benefits of the ARFR method in downscaling LST using GEE.
Application of Google Earth Engine Cloud Computing Platform, Sentinel Imagery, and Neural Networks for Crop Mapping in Canada
TLDR
This study demonstrated that the proposed cloud computing method should be investigated further because it was more efficient in terms of cost, time, computation, and automation.
A Google Earth Engine Application to Retrieve Long-Term Surface Temperature for Small Lakes. Case: San Pedro Lagoons, Chile
TLDR
This work aims to test, implement and automate six methods to retrieve LSWT fields from satellite imagery using the Google Earth Engine platform, using 30 m spatial resolution images from Landsat 7 and 8 satellites for 2000–2020.
Google Earth Engine Sentinel-3 OLCI Level-1 Dataset Deviates from the Original Data: Causes and Consequences
TLDR
This study demonstrates that the Google Earth Engine (GEE) dataset of Sentinel-3 Ocean and Land Color Instrument (OLCI) level-1 deviates from the original Copernicus Open Access Data Hub Service (DHUS) data by 10–20 W m−2 sr−1μμm−1 per pixel per band and suggests that the scaling factors reported in the GEE dataset description must not be used.
Evaluating the impact of flood inundation with the cloud computing platform over vegetation cover of Ganga Basin during COVID-19
Around the month of July 2020, the people suffering from the COVID-19 pandemic got added burden of disastrous floods. The current study tries to understand the impact of flood inundation on the…
Spatiotemporal mapping of inundation area at Lake Limboto in Gorontalo, Indonesia, using cloud computing technology
Monitoring activities on the dynamics of water shrinkage at Lake Limboto are essential to the lake’s ecosystem’s recovery. A remote sensing technology functions to monitor the dynamics of lake…
Detection of Urban Forest Change in Jabodetabek Megacity Using Sentinel 2 and Landsat 8 Imagery Through Google Earth Engine Cloud Computing Platform
The urban forest is a green area in urban areas overgrown with woody vegetation and provides physiological, sociological, economic, and aesthetic benefits for urban communities. Unfortunately, urban…
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References

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TLDR
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; Landsat was the most widely used dataset; it is the biggest component of the GEE data portal.
Google Earth Engine for geo-big data applications: A meta-analysis and systematic review
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