Proximal and Remote Sensing for Low-Cost Soil Carbon Stock Estimation

Dear Colleagues,

Soil organic carbon (SOC) is a critical component of soil health, influencing soil fertility, structure, and its ability to sequester carbon, which has significant implications for climate change mitigation. Accurate estimation and monitoring of SOC are essential for sustainable land management and agricultural practices. However, traditional methods of SOC assessment can be labor-intensive and costly. Advances in remote sensing (RS) technologies, including proximal sensing techniques like visible near-infrared (VNIR) and mid-infrared (MIR) spectroscopy, combined with artificial intelligence (AI) and machine learning (ML), offer new opportunities for low-cost, large-scale SOC estimation and monitoring.

The aim of this Special Issue is to highlight innovative methodologies, workflows, and sensors for estimating soil organic carbon (SOC) using remote sensing data. By leveraging digital soil mapping and AI techniques, we seek to enhance the accuracy and cost-effectiveness of SOC estimation. This Special Issue aims to cover the entire scope of SOC estimation from data acquisition and preprocessing to model development and application. We welcome both original research articles, review papers and communication and frontiers dynamics that explore these advancements and their practical applications.

We invite researchers to contribute original research articles, reviews, and case studies focusing on the remote (proximal) sensing of SOC. Topics of interest include, but are not limited to, the following:

• SOC estimation from unmanned aerial vehicles (UAVs), airborne, and satellite imagery: Techniques for mapping SOC using data from UAVs, airborne platforms, and satellite imagery, including data from programs like Copernicus.
• VNIR and MIR spectroscopy for SOC estimation: Methods utilizing visible, near-infrared and mid-infrared spectroscopy for accurate and cost-effective SOC measurement.
• Monitoring SOC dynamics: Methods for tracking changes in SOC over time to assess the impact of land use, climate change, and management practices.
• Impact of land management on SOC: Assessing how different land use and management practices affect SOC levels and soil health.
• Reducing carbon footprint in agriculture: Applications of remote sensing in promoting sustainable agricultural practices that enhance SOC and reduce carbon emissions.
• Advanced sensors and data fusion: Utilization of optical hyperspectral data, LiDAR, gamma radiometric, and novel sensor technologies, including data fusion techniques.
• Strategies for minimizing errors in SOC mapping: Band optimization, error source quantification, uncertainty allocation and algorithm optimization.
• Interactions between SOC and atmospheric carbon: Investigating reciprocal interactions between SOC and atmospheric carbon, with a focus on feedback mechanisms and their impacts on global climate dynamics.

Dr. Tong Li
Prof. Dr. Songchao Chen
Dr. Anquan Xia
Dr. Francesco Fava
Dr. Yash Dang
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. 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.

• soil fertility
• visible near-infrared (VNIR) spectroscopy
• mid-infrared (MIR) spectroscopy
• climate change
• machine learning
• digital soil mapping
• land-use management
• unmanned aerial vehicles (UAVs)
• airborne, and satellite imagery