Google Earth Engine for Remote Sensing Big Data Landscapes

Dear Colleagues,

In recent years, airborne and spaceborne sensors have collected large amounts of Remote Sensing (RS) data with various characteristics (e.g., different spectral, spatial, temporal, and radiometric resolutions). The availability of open-access RS datasets and advances in sensor and image processing technology are likely to continue this trend in the near future. In this regard, we face challenges in managing and processing petabytes of RS data, which can be divided into two main groups: those associated with common aspects (e.g., big data computing and collaboration) and those associated with individual aspects (e.g., deployment, fusion, and visualization). A cloud computing platform developed by Google, called Google Earth Engine (GEE), is designed to address these challenges and to facilitate the processing of big geospatial data over large areas and monitoring the environment over long periods of time. GEE provides free access to MODIS, Landsat, and Sentinel data, as well as other imagery and ancillary datasets (e.g., land-use, climate, and soil data), via Javascript and Python APIs. In addition to requiring only a web browser and internet access, these platforms enable a new generation of analysts to gain access to earth observation data without requiring extensive infrastructure or software investments. With the help of Google CoLabs, GEE users now have access to advanced data science and machine learning techniques, enabling the development of new methods and web services for big RS data processing.

Our Special Issue invites submissions addressing methodologies and applications of big data processing using GEE across different geographical scales. We are particularly interested in studies introducing novel techniques for analysing big data, addressing challenges associated with implementing large-scale or long-term series analyses and sharing code or application examples. Moreover, case studies illustrating how GEE functions and tools can be used to advance scientific understanding of environmental and societal concerns are also welcomed.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

• Remote sensing big data analysis;
• Land-use and land-cover (LULC) classification from local up to global level;
• Land-use and land-cover (LULC) change detection, monitoring, and modeling;
• Flood detection and monitoring through remote sensing data and integration with other geospatial data (i.e., GNSS, social media data);
• Multi-Sensor and multi-resolution big data analysis;
• Machine and deep learning for big remote sensing data processing;
• Water resources monitoring and modeling;
• Forests and vegetation dynamics monitoring and modeling and deforestation;
• Ecosystem response to climate change;
• Crop yield estimation and Crop area mapping;
• Disaster extent and response;
• Surface sediment monitoring;
• Compositing, Masking, and Mosaicking of remote sensing data.

We look forward to receiving your contributions.

Dr. Armin Moghimi
Dr. Meisam Amani
Dr. Mohammad Kakooei
Dr. Reza Shah-Hosseini
Dr. Masood Varshosaz
Dr. Ali Mohammadzadeh
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• Google Earth Engine (GEE)
• Remote Sensing (RS)
• big data
• classification
• change detection
• flood detection
• large-scale mapping
• time series
• cloud computing