Applications of Remote Sensing and Machine Learning for Digital Soil Mapping
A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".
Deadline for manuscript submissions: 10 December 2024 | Viewed by 111
Special Issue Editors
Interests: digital soil mapping; multi-source remote sensing; soil properties prediction; soil nutrients cycling
Interests: remote sensing; digital soil mapping; pedometrics; biogeochemical modeling
Special Issues, Collections and Topics in MDPI journals
Interests: soil-landscape relationships; machine learning and AI; legacy soil data utilization; precision agriculture; multi-scale landscape metrics; remote sensing-derived variables
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The convergence of agriculture, environmental science, and cutting-edge technology is ushering in a new era in soil management and conservation. Soil mapping serves as a fundamental activity underpinning numerous environmental and agricultural endeavors. Traditional approaches, while foundational, are often characterized by their time-intensive nature, labor demands, and a potential lack of dynamism in capturing soil properties. The integration of machine learning (ML) with remote sensing technology offers a groundbreaking alternative, enhancing the precision, efficiency, and scope of soil analyses. The aim of this Special Issue is to demonstrate the enhanced capabilities that machine learning and remote sensing technologies bring to digital soil mapping. It seeks to bridge ML and traditional soil science, fostering a multidisciplinary exchange that elevates our ability to forecast, scrutinize, and manage soil resources with unprecedented accuracy.
We are soliciting original research articles and reviews covering, but not limited to, the following topics:
- Integration of machine learning algorithms and remote sensing for soil property prediction (as well as for soil classification);
- Machine learning approaches for soil classification and taxonomy;
- Soil spectral library, including visible–near-infrared and mid-infrared spectroscopy;
- Proximal, airborne, and satellite remote sensing;
- Advanced analytics in soil science utilizing big data and artificial intelligence;
- Case studies demonstrating the impact of these technologies in agricultural and environmental contexts.
Dr. Jing Geng
Dr. Yongsheng Hong
Dr. Yiyun 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. Agriculture 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
- machine/deep learning
- remote sensing
- digital soil mapping
- soil property prediction
- big data analytics
- soil resource management
