Journal of Remote Sensing Special Issue
Remote Sensing for Environmental and Societal Changes Using Google Earth Engine
Journal of Remote Sensing, a Science Partner Journal, is now considering submissions for its first special issue, Remote Sensing for Environmental and Societal Changes Using Google Earth Engine.
The Earth is constantly changing, bringing forth profound challenges for the environment and human society. To address these challenges at the global scale, the remote sensing community relies heavily on geospatial datasets acquired from satellite, airborne, and mobile sensors. However, the explosive growth in geospatial datasets during the past decades is overwhelming the remote sensing community’s capacity for storage, analysis, and visualization. The advent of the Google Earth Engine (GEE) cloud-computing platform makes it possible to access, manipulate, and analyze large volumes of geospatial datasets on-the-fly. GEE contains a huge public data catalog with over 35 petabytes of satellite imagery and geospatial datasets, such as Landsat, Sentinel, MODIS, and NAIP. New satellite imagery and geospatial datasets are added to the public data catalog on a daily basis. During the past few years, GEE has become very popular in the remote sensing community and has empowered numerous Earth science studies at local, regional, and global scales (see https://earthobservations.org/article.php?id=447).
This special issue from Journal of Remote Sensing will provide an outlet for state-of-the-art GEE applications for benefit of the environment and human society at various spatial and temporal scales, as well as prospects for future development. Review articles on GEE applications and articles that include machine learning, deep learning, artificial intelligence, object identification, change detection, as well as multi-sensor (e.g., short and long wave multispectral, SIF, SAR, LiDAR, Low-Earth Orbit and Geosynchronous Equatorial Orbit) data analysis, including spatial-temporal-spectral fusion, are especially encouraged.
Potential topics for this special issue may include, but are not limited to, novel applications and advances of GEE as applied to the environment and society:
- Land use and land cover mapping and change analysis
- Mapping of changes in urban areas and infrastructure
- Ecosystem monitoring, assessment, and response to climate change
- Vegetation mapping and modeling
- Assessment and monitoring of greenhouse gas emissions
- Urban heat island effect and thermal sensing
- Disaster mapping and change analysis
- Spatio-temporal analysis of air, soil, vegetation, and water quality
- Mapping and monitoring of natural resources (e.g., water, forest, wetland, grassland)
- Review of GEE-based projects, progress, and perspectives
Dr. Nicholas Clinton, Google, Inc., USA (email@example.com)
Dr. Gennadii Donchyts, Deltares, The Netherlands (firstname.lastname@example.org)
Dr. Jinwei Dong, Chinese Academy of Sciences, China (email@example.com)
Dr. Qiusheng Wu, University of Tennessee, Knoxville, USA (firstname.lastname@example.org)
Dr. Le Yu, Tsinghua University, China (email@example.com)
Dr. Yelu Zeng, University of Wisconsin‐Madison, USA (firstname.lastname@example.org)
Manuscripts may be submitted starting July 1, 2021 and through June 30, 2022.
Please indicate in your cover letter that your submission is intended for inclusion in the special issue, Remote Sensing for Environmental and Societal Changes Using Google Earth Engine. There are no submission fees and article processing charges (APCs) are waived through June 2023.
Google Earth Engine analytics platform is a trademark of Google LLC and this special issue is not endorsed by or affiliated with Google in any way.
Table of Contents
As articles within the special issue are published they will appear below.
Guangwei Chen, Runjie Jin, Zhanjiang Ye, Qi Li, Jiali Gu, Min Luo, Yongming Luo, George Christakos, James Morris, Junyu He, Dan Li, Hengwei Wang, Li Song, Qiuxuan Wang, and Jiaping Wu
Journal of Remote Sensing, Vol. 2022, Article ID 9793626, 15 pages | Jan 20, 2022
Xiao Zhang, Liangyun Liu, Xidong Chen, Yuan Gao, and Mihang Jiang
Journal of Remote Sensing, Vol. 2021, Article ID 9873816, 16 pages | Oct 16, 2021