Digital Soil Mapping and Precision Agriculture
A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land, Soil and Water".
Deadline for manuscript submissions: 31 December 2025 | Viewed by 41
Special Issue Editors
Interests: digital soil mapping and remote sensing
Interests: proximal soil sensing; soil spectroscopy; digital soil mapping; carbon sequestration; soil biogeochemical modeling
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Soil is a critical natural resource underpinning global food security, ecosystems, and climate regulation. However, traditional soil mapping and agricultural practices often lack the resolution and adaptability needed to address contemporary challenges such as land degradation, climate change, and population growth. Recent advances in geospatial technologies, machine learning (deep learning), and sensor networks have revolutionized our capacity to analyze soil systems and optimize agricultural practices at unprecedented scales. Digital soil mapping (DSM) and precision agriculture (PA) are emerging as transformative approaches that integrate high-resolution data, predictive modeling, and site-specific management to enhance soil health, crop productivity, and environmental sustainability.
This Special Issue aims to compile original research and review articles that advance the science and application of DSM and PA, emphasizing their role in sustainable land management. We aim to address gaps in spatial soil data, model accuracy, and technology adoption in diverse agroecosystems.
This Special Issue welcomes contributions on, but not limited to, the following topics:
- High-resolution soil property mapping using remote sensing, ML, or IoT.
- AI/ML-driven predictive models for soil health and yield optimization.
- Data/model fusion frameworks integrating multi-source geospatial and field data.
- Environmental impacts and circular agriculture tied to PA systems.
- Innovations in DSM (e.g., AI/ML applications, hyperspectral imaging, and UAV-based sensing).
- Integration of multi-source data (remote sensing, in situ measurements, and legacy data).
- Climate-resilient agricultural practices leveraging DSM/PA.
- Case studies on regional-to-global DSM frameworks.
Submissions may include original research, reviews, case studies, or methodological papers. In particular, research on the influence of human factors on soil property mapping is encouraged.
Dr. Xiaobo Wu
Prof. Dr. Songchao 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. Land is an international peer-reviewed open access monthly 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 2600 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
- digital soil mapping
- precision agriculture
- geospatial technologies
- machine learning
- soil health
- sustainable land management
- remote sensing
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