Feature Review Paper in "Earth Sciences" Section
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".
Deadline for manuscript submissions: 30 May 2027 | Viewed by 40
Editors
Interests: hyperspectral imagery; remote sensing in geoscience; unmixing; subpixel mapping; deep learning
Special Issues, Collections and Topics in MDPI journals
Interests: marine geomorphology; coastal and marine volcanism; cartography; hazard; coastal archeology; coastal and marine pollution
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleague,
Earth sciences encompass the study of the Earth system and its complex interactions among the lithosphere, hydrosphere, atmosphere, biosphere, and anthroposphere. Rapid advances in Earth observation technologies, geospatial data analytics, artificial intelligence, and interdisciplinary research have significantly reshaped the field. This Special Issue aims to publish high-quality feature review papers that comprehensively summarize recent scientific advances, methodological developments, emerging research trends, and future perspectives across Earth sciences and geography. We particularly welcome authoritative reviews that critically evaluate current progress, identify existing knowledge gaps, discuss unresolved challenges, and propose promising future research directions in both fundamental and applied geosciences.
The topics of interest for this Special Issue include, but are not limited to:
- Advances in remote sensing, Earth observation, and geospatial technologies;
- Artificial intelligence, machine learning, and big data in Earth sciences;
- Digital Earth, GIScience, and spatiotemporal modeling;
- Earth system science and environmental change;
- Geohazards, climate change, and disaster risk assessment;
- Mineral, energy, and water resources;
- Geology, geophysics, geochemistry, and tectonics;
- Marine, atmospheric, cryosphere, and hydrological sciences;
- Human–environment interactions and sustainable development;
- Multi-source Earth observation data integration and future research perspectives.
Prof. Dr. Ke Wu
Dr. Salvatore Passaro
Dr. Andrea Di Capua
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 250 words) can be sent to the Editorial Office for assessment.
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences 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 2400 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.
Keywords
- remote sensing
- geoinformatics
- GIScience
- earth observation
- geohazards
- geochemistry
- geophysics
- geology
- geosciences
- mineral resources
- energy resources
- geotechnical engineering
- marine geoscience
- hydrology
- hydrogeology
- climate change
- environmental contamination
- earth system science
- digital earth
- spatiotemporal modeling
- big data
- artificial intelligence
- machine learning
- multi-source data fusion
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