GeoAI and EO Big Data Driven Advances in Earth Environmental Science (Second Edition)

Dear Colleagues,

With the extensive development of earth observation (EO) technologies (e.g., optical and microwave remote sensing, LiDAR, GNSS, and geospatial sensor web) in recent years, EO data have accumulated quickly to the petabyte-level, which provides the greatest opportunities yet for earth environmental science, though they also pose the grandest challenges for the processing of these EO big data. Owing to the development and advancement of artificial intelligence (AI), especially Geospatial AI (GeoAI) methods and techniques (e.g., spatiotemporal machine learning and deep learning), the modeling, processing, and analysis of EO big data have arrived at a new paradigm. By integrating EO big data and GeoAI methods, more comprehensive and in-depth investigations into earth environmental science have become possible.

This Special Issue invites the submission of methodological or applied studies using GeoAI and EO big data for investigating matter, energy, and information in the hydrosphere, lithosphere, biosphere, and atmosphere on the surface of the Earth. The scale can be local, regional, or global, but large-scale and long time-series studies will be preferred. In addition, monitoring and analysis studies of the key thematic indicators for high-impact events or disasters such as droughts, floods, earthquakes, tsunamis, and volcanic eruptions are especially welcome.

Articles may address, but are not limited to, the following topics:

• Analysis and mining of EO (e.g., optical and microwave remote sensing, LiDAR, GNSS, and geospatial sensor web) big data;
• Novel GeoAI models and frameworks (e.g., spatiotemporal machine learning/deep learning) for modeling/processing/analyzing EO big data;
• Retrievals of environmental variables (e.g., precipitation, land/sea surface temperature, soil moisture, aerosols, vegetation index, sea ice concentration, sea surface salinity, snow cover, chlorophyll-a concentration);
• Environmental variables’ monitoring and prediction;
• Postprocessing of environmental variable retrievals (e.g., multi-source data fusion, downscaling, and image restoration);
• Extracting information from EO big data (e.g., classification, segmentation, target detection, dynamic monitoring, and prediction);
• Natural hazards’ (e.g., drought, flood, waterlogging, wildfire, landslide, surge earthquake, tsunami, and volcanic eruption) monitoring and evaluation;
• Crop yield estimation;
• Land cover land use mapping and scenario prediction;
• Monitoring and analysis of high-impact events (e.g., epidemic outbreaks, oil spills, gas pipeline ruptures, carbon neutrality, and emission peak).

Dr. Min Huang
Prof. Dr. Hui Lin
Prof. Dr. Nengcheng Chen
Dr. Daoye Zhu
Dr. Kaiqi Chen
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.

• earth observation big data
• GeoAI
• multisource/multimodal data fusion
• long time-series analysis
• retrievals of environmental variables
• postprocessing of environmental variable retrievals
• monitoring, evaluation, and prediction
• land cover land use
• natural hazards
• high-impact events

• GeoAI and EO Big Data Driven Advances in Earth Environmental Science in Remote Sensing (25 articles)