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Submit a Manuscript to the Journal
Geo-spatial Information Science

For a Special Issue on
Remote sensing and machine learning in advancing carbon neutrality

Manuscript deadline
31 December 2022

Cover image - Geo-spatial Information Science

Special Issue Editor(s)

Huanfeng Shen, Wuhan University
[email protected]

Jane Liu, University of Toronto
[email protected]toronto.ca

Wenping Yuan, Sun Yat-Sen University
[email protected]

Yongguang Zhang, Nanjing University
[email protected]

Holly Croft, University of Sheffield
[email protected]

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Remote sensing and machine learning in advancing carbon neutrality

Guest Editors:

Huanfeng Shen, Wuhan University ([email protected]), Jane Liu, University of Toronto ([email protected]), Wenping Yuan, Sun Yat-Sen University ([email protected]), Yongguang Zhang, Nanjing University ([email protected]), Holly Croft, University of Sheffield ([email protected]), Xiaobin Guan, Wuhan University ([email protected]).

The dramatic increase in anthropogenic carbon emissions over the last five decades has already led to substantial damage to our environment, including increases in extreme weatherevents, loss of biodiversity, and a rise in sea level. Carbon neutrality, i.e., net-zero anthropogenic carbon emissions, is necessary to ensure the sustainable future of human beings, and hundreds of countries have pledged to achieve this goal by mid-century. Remote sensing techniques can acquire frequent observations of the Earth with various temporal and spatial resolutions, and provide substantial information for carbon emission monitoring and carbon cycle modeling. Remote sensing observations not only can be directly applied to retrieve the atmospheric concentrations of greenhouse gases (e.g., CO2, CO, CH4, CFCs, O3, et al.), but also can be employed to investigate the carbon budget of natural ecosystems. Since natural ecosystem carbon sinks are a low-cost option for carbon removal from the atmosphere, advancing our knowledge of the terrestrial ecosystem carbon cycle is particularly imperative. In recent years, machine learning has emerged as a powerful new technique within ecological and environmental studies, which has already shown great potential in advancing the monitoring of greenhouse gases concentrations and modeling of the ecosystem carbon cycle. Adopting remote sensing and machine learning can implement large-scale, ecosystem-specific monitoring of the Earth with unprecedented precision, in order to probe the potential, opportunities, and limits to achieving carbon neutrality over space and time.

This special issue is dedicated to presenting the latest advances in remote sensing and machine learning applications on carbon neutrality. Topics of interest include, but are not limited to:

  • Terrestrial ecosystem carbon cycle modeling;
  • Remote sensing of atmospheric concentrations of greenhouse gases;
  • High-resolution mapping of anthropogenic greenhouse gases emission plumes;
  • Novel applications of machine learning in simulating the global or regional carbon cycles, including related key parameters;
  • Remote sensing of vegetation structure, physiological, and functional parameters;
  • Applications of new generations of remote sensing data in the biosphere and atmosphere;
  • Uncertainties around the tree-planting based offset schemes and nature-based solutions in achieving carbon neutrality;
  • Tracking the footprints of organic waste and pollution using machine learning and remote sensing;
  • Interaction between terrestrial ecosystem and atmosphere under climate change;
  • Machine learning applications in remote sensing data processing: reconstruction, temporal filtering, downscaling, and data fusion.

Submission Instructions

Important dates:

  • December 31, 2022: deadline for submitting manuscripts

All manuscripts are refereed through a peer-review process. All accepted manuscripts will be published in Geo-Spatial Information Science (GSIS) in Open Access (OA). Perspective authors are recommended to prepare their manuscript by following the author instructions of the journal GSIS (see www.tandfonline.com/tgsi). Once you have finished preparing your manuscript, please submit it through the Taylor & Francis Submission Portal (see Manuscript Submission (tandfonline.com)), ensuring that you select the appropriate Special Issue. Geo-spatial Information Science (GSIS) is indexed by the SCI (IF2020:4.288) and other databases (EI, Scopus, DBLP, CSCD, GeoBase).

Publication charges (APCs) will be waived for invited manuscripts submitted to Geo-Spatial Information Science. Authors who require a waiver code should contact the Editorial Office ([email protected][email protected]) before papers are submitted.

Instructions for AuthorsSubmit an Article

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