State of the Art in Land Cover Classification and Mapping: Building Up Digital Twins of Earth
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing for Geospatial Science".
Deadline for manuscript submissions: 31 August 2026 | Viewed by 825
Special Issue Editors
Interests: geostatistics; remote sensing; digital terrain analysis; vegetation mapping; land cover
Special Issues, Collections and Topics in MDPI journals
Interests: irrigation and drainage engineering; agricultural drought and water resource management; drought monitoring, mitigation, planning, and policy; risk and vulnerability management; remote sensing for drought monitoring and management; soil moisture and hydrologic/watershed modelling
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Land cover classification and mapping have advanced significantly, methodologically and technically, with ongoing improvements in conceptualization, models and methods, remote sensing science and systems, instrumentation, computer algorithms, and implementations. However, due to complexity in semantics, scale, phenology, and other characteristics inherent to land cover, many challenging issues remain. Certain cover types (e.g., vegetation) are more difficult to classify and map than others (e.g., water bodies). Inconsistency and variability are common, even for experienced image analysts. Scientific rigor, technical sophistication, higher accuracy, and cost-effectiveness are crucial, as are guidelines and overviews of good practice in these areas.
This Special Issue (SI), revised from its earlier theme on land cover classification and mapping, aims to bring together multidisciplinary scientists and specialists to advance research on concepts, models, methods, algorithms, and practicalities concerning land cover classification and mapping. Key strategic research areas will be thoroughly discussed to identify bottlenecks to breakthroughs. This SI will facilitate communication among researchers and practitioners on topics of mutual interest. Topics of interest include, but are not limited to, the following:
- Classification system, harmonization, interoperability, and standards;
- Semantics and thematic resolution;
- Conceptualization of land cover as fields vs. objects;
- Multi-resolution, proportional, and fuzzy representations of land cover;
- Models of scale and minimum mapping units (MMUs);
- Upscaling and downscaling;
- Sampling design for reference data acquisition;
- Image interpretation, interpreter variability, consistency, and quality assurance;
- Training datasets for machine learning oriented for land cover mapping;
- Spectral, spatial, and temporal features and their informativeness;
- Phenology and time series analysis;
- Statistical vs. rule-based classification methods;
- Physics-informed and explainable machine learning in land cover classification and mapping;
- Fusion of sensors, data, features, and classifiers;
- Data cubes of existing land cover products and land cover primitives;
- Well-targeted re-mapping of land cover;
- Accuracy metrics and assessments for pixels, classes, and all problem domains;
- Uncertainty characterization;
- Thematic and regional case studies of cropland, grassland, shrub, forest, wetland, impervious surfaces, water bodies, and other broad cover types;
- Best practice in land cover classification and mapping.
Prof. Dr. Jingxiong Zhang
Prof. Dr. Won-Ho Nam
Dr. Wangle Zhang
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-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.
Keywords
- classification
- mapping
- classification systems
- land cover
- remote sensing images
- scale
- resolution
- semantics
- spectral–spatial–temporal features
- phenology
- pattern analysis
- machine learning
- rule bases
- accuracy metrics and assessment
- uncertainty
- confusion matrix
- mixed pixels
- sampling
- reference samples
- image interpretation
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.
Further information on MDPI's Special Issue policies can be found here.
