Advances in Multispectral Image Processing for Land Use and Land Cover Mapping

Dear Colleagues,

Accurate Land Use and Land Cover (LULC) mapping is fundamental for understanding environmental dynamics, urban expansion, agricultural monitoring, and biodiversity conservation. The rapid growth of multispectral remote sensing technologies—ranging from high-resolution aerial imagery to satellite platforms (e.g., Sentinel-2, Landsat-9)—has dramatically enhanced our ability to characterize terrestrial surfaces. However, challenges remain in harmonizing heterogeneous datasets, mitigating atmospheric and radiometric inconsistencies, and developing generalizable algorithms that can adapt across diverse geographic and ecological regions.

Recent advances in machine learning, deep neural networks, and multimodal data fusion have enabled new opportunities to improve classification accuracy and spatial consistency in LULC mapping. Integrating multispectral imagery with LiDAR, SAR, and environmental covariates enables richer feature representation and the extraction of subtle land-cover transitions that traditional methods often overlook. This Special Issue aims to showcase the latest developments, methodologies, and applications that push the boundaries of multispectral image processing for robust, scalable, and interpretable LULC analysis.

This Special Issue seeks to bring together cutting-edge research on multispectral image analysis and its application in land use and land cover mapping. It aligns with the scope of Remote Sensing by emphasizing innovations in data processing, AI-driven classification, and the fusion of optical and auxiliary datasets to enhance spatial understanding of Earth’s surface. We welcome both methodological advancements and applied case studies that address pressing environmental and urban challenges by using multispectral and hyperspectral remote sensing.

We invite original research articles, technical notes, and comprehensive reviews addressing, but not limited to, the following topics:

• Deep learning and foundation models for multispectral and hyperspectral image classification;
• Cross-sensor harmonization and large-scale LULC mapping pipelines;
• Data fusion between multispectral, LiDAR, and SAR for vegetation and urban studies;
• Change detection and temporal dynamics of land cover;
• Integration of multispectral data with ecology/climate data for long-term mapping of forests, urban areas, and croplands;
• Applications in urban expansion, deforestation monitoring, and climate resilience assessment;
• Uncertainty quantification and interpretability in LULC classification.

Dr. Zhuohong Li
Dr. Junjue Wang
Dr. Haonan Guo
Dr. Jiaqi Zou
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. 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.