Advancing Soil Organic Carbon Stock Prediction with Digital Mapping
A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".
Deadline for manuscript submissions: 20 January 2026 | Viewed by 19
Special Issue Editors
Interests: pedometrics; soil carbon modeling; remote and proximal sensing; spatial data science; digital soil mapping
Special Issues, Collections and Topics in MDPI journals
Interests: soil science; artificial intelligence; earth observation; large language models
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Soil Organic Carbon (SOC) plays a pivotal role in global climate regulation, soil fertility, and sustainable agriculture. As the largest terrestrial carbon pool, SOC is central to international climate goals and nature-based solutions. In recent years, considerable efforts have been made to improve the spatial prediction and monitoring of SOC stocks, enabling more accurate carbon accounting and land management strategies.
This Special Issue aims to gather cutting-edge research on innovative approaches for mapping and evaluating SOC stocks at multiple spatial and temporal scales. We welcome contributions that advance Digital Soil Mapping (DSM) techniques, integrate remote and proximal sensing data, and explore machine learning and deep learning applications in SOC prediction. Interdisciplinary studies that incorporate microbiology, spectroscopy, land use dynamics, and novel monitoring frameworks (e.g., MRV systems) are especially encouraged.
We are particularly interested in studies addressing persistent challenges in SOC mapping, such as data scarcity, uncertainty quantification, harmonization across regions, and underrepresentation of vulnerable ecosystems. Contributions that propose scalable, transparent, and policy-relevant solutions are essential to support soil-centered climate actions and sustainable land use transitions.
Original research articles, reviews, methodological papers, and case studies are all welcome.
Prof. Dr. Raul Roberto Poppiel
Dr. Nikolaos Tziolas
Guest Editors
Manuscript Submission Information
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Keywords
- soil organic carbon (SOC)
- digital soil mapping (DSM)
- remote sensing
- proximal sensing
- machine learning
- deep learning
- SOC monitoring, reporting and verification (MRV)
- spectroscopy
- soil health and climate change
- spatial prediction of SOC
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