Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.5
2.5
Journals
Applied Sciences
Special Issues
Soil Organic Matter and Carbon Content Analysis Using Machine Learning...
applsci-logo
Submit to Applied Sciences Review for Applied Sciences Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Soil Organic Matter and Carbon Content Analysis Using Machine Learning and Classical Approaches

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 March 2026) | Viewed by 4869

Special Issue Editors

Dr. Yaping Xu

E-Mail Website
Guest Editor
Department of Environmental and Geosciences, Sam Houston State University, Huntsville, Texas 77340, USA
Interests: Precision Agriculture; Soil Moisture Mapping; Population-Environmental Modeling; Agroclimatic Study; Machine Learning
Special Issues, Collections and Topics in MDPI journals
Dr. Rafael López Núñez

E-Mail Website
Guest Editor
IRNAS-CSIC, Institute of Natural Resources and Agrobiology of Seville, Avda Reina Mercedes 10, 41012 Sevilla, Spain
Interests: soil organic matter; heavy metals; urban agriculture; nitrogen
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to a Special Issue of Applied Sciences entitled "Soil Organic Matter and Carbon Content Analysis Using Machine Learning". Soil organic analysis is of paramount importance for understanding soil health, fertility, and overall ecosystem dynamics. Traditional methods for soil analysis are often labor-intensive and time-consuming, limiting the scale and depth of studies. However, recent advancements in machine learning techniques coupled with innovative sensing technologies have opened up new avenues for rapid and accurate soil organic analysis. This Special Issue aims to explore the intersection of soil science and machine learning, offering insights into advanced methods for soil organic analysis or mapping.

This Special Issue aims to bridge the gap between soil science and machine learning by showcasing cutting-edge research in soil organic analysis. By harnessing the power of machine learning algorithms, researchers can analyze large datasets derived from various sensing platforms, such as satellite imagery, drone cameras, and spectroscopic techniques. The resulting insights can revolutionize our understanding of soil properties, enabling more informed decision-making in agriculture, environmental monitoring, and land management. This topic aligns closely with the scope of Applied Sciences, which welcomes interdisciplinary research at the intersection of engineering, technology, and applied sciences. Through this Special Issue, we aspire to gather a diverse collection of articles that will push the boundaries of soil analysis methodologies.

Suggested themes and article types for submissions:
In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • The development of machine learning algorithms for soil organic analysis;
  • The integration of remote-sensing data into machine learning techniques for soil mapping;
  • Applications of drone cameras and other advanced sensing technologies for soil characterization;
  • The merging of several advanced detection technologies;
  • Case studies demonstrating the efficacy of machine learning in soil organic analysis;
  • Challenges and future directions in the field of soil science and machine learning.

We look forward to receiving your contributions.

Dr. Yaping Xu
Dr. Rafael López Núñez
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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences 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 2400 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

  • soil science
  • machine learning
  • remote sensing
  • soil mapping
  • organic analysis
  • drone technology
  • spectroscopic analysis
  • proximal soil-sensing techniques

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

16 pages, 28839 KB  
Article
Assessment of Carbon Dynamics Using Remote Sensing, Machine Learning, and Cellular Automata in a Semi-Arid Region
by Vincenzo Barrile, Emanuela Genovese, Clemente Maesano, Davide Borrello and Fatma Ben Brahim
Appl. Sci. 2026, 16(10), 4801; https://doi.org/10.3390/app16104801 - 12 May 2026
Viewed by 98
Abstract
Soil Organic Matter (SOM) and Soil Organic Carbon (SOC) are essential for regulating ecosystem functions, soil fertility, and influencing climate change processes, especially in semi-arid regions. The recent improvements in remote sensing instruments and the development of artificial intelligence methodologies, such as machine [...] Read more.
Soil Organic Matter (SOM) and Soil Organic Carbon (SOC) are essential for regulating ecosystem functions, soil fertility, and influencing climate change processes, especially in semi-arid regions. The recent improvements in remote sensing instruments and the development of artificial intelligence methodologies, such as machine learning, enable an improved understanding of carbon dynamics, facilitate the estimation of SOC content, and support predictive modeling. This study presents an integrated framework to analyze past and future carbon dynamics in the Sfax Governorate (Tunisia). Land-use and land-cover (LULC) maps for the years 2019, 2020, 2022, and 2024 were generated using a Random Forest algorithm applied to multispectral satellite data in the Google Earth Engine platform, achieving high classification accuracy (overall accuracy up to 0.90). Carbon stocks and their temporal variations were estimated using the InVEST Carbon Storage and Sequestration model, while carbon emissions and the Net Ecosystem Carbon Balance (NECB) were derived by integrating land-use-specific emission factors. Future LULC scenarios for 2030 were simulated through a Cellular Automata model under three alternative development pathways: conservation-oriented (CONS), business-as-usual (BAU), and urban expansion (URB+). The study demonstrates how the integration of machine learning, remote sensing, and ecosystem modeling supports spatially explicit assessment of SOC-related carbon dynamics and provides useful insights for land management and climate mitigation strategies. Full article
(This article belongs to the Special Issue Soil Organic Matter and Carbon Content Analysis Using Machine Learning and Classical Approaches)
►▼ Show Figures

Figure 1

17 pages, 3512 KB  
Article
Assessing Baseline Soil Carbon, Organic Matter, and Nitrogen Content Associated with Different Rangeland Management Practices in Oregon, USA
by Carlos G. Ochoa, Mohamed A. B. Abdallah, María Jose Iglesias Thome, Daniel G. Gómez and Ricardo Mata-González
Appl. Sci. 2026, 16(9), 4212; https://doi.org/10.3390/app16094212 - 25 Apr 2026
Viewed by 791
Abstract
Understanding how land management influences soil carbon (C) and nitrogen (N) dynamics is critical for improving ecosystem resilience and carbon sequestration potential in semiarid rangelands. This study used classical field- and laboratory-based methods to assess soil organic carbon (SOC), organic matter (OM), and [...] Read more.
Understanding how land management influences soil carbon (C) and nitrogen (N) dynamics is critical for improving ecosystem resilience and carbon sequestration potential in semiarid rangelands. This study used classical field- and laboratory-based methods to assess soil organic carbon (SOC), organic matter (OM), and N content at 13 sites across four ecological provinces in eastern Oregon, USA. Treated sites—where traditional rangeland restoration and management practices had been applied to them (i.e., juniper removal, sagebrush removal, post-fire grass seeding, and land conversion to pasture)—were paired with adjacent untreated control sites. Soil samples were collected at two depths, 0 to 10 cm and 15 to 25 cm and analyzed for C, N, OM, bulk density (BD), soil volumetric water content (SVWC), porosity, and texture. Soil C and N stocks were calculated on an area basis (t ha−1), and statistical analyses were conducted using one-way ANOVA and correlation tests. Treated sites generally exhibited higher soil C, N, and OM content compared to untreated sites, particularly in the upper 10 cm of soil. Data obtained from the two soil depths (0 to 10 cm and 15 to 25 cm) were averaged and assumed to represent the top 30 cm of the soil profile, corresponding to the effective rooting zone at each field. The site where sagebrush removal was followed by grass seeding exhibited the highest soil C and N stocks (115.8 t C ha−1 and 9.2 t N ha−1, respectively). This site also had the highest OM content (9.53%), which was observed in the topsoil layer (0 to 10 cm) across all sites and depths. Strong positive correlations between C and N were detected across all sites (mean r = 0.92), while negative correlations were observed between soil C and bulk density at several locations. Results suggest that vegetation management practices such as woody plant removal and grass establishment can enhance soil C storage and nutrient retention in semiarid rangeland ecosystems. These findings provide baseline data to inform land management strategies aimed at improving soil health and carbon sequestration potential in the Pacific Northwest region in the USA. Full article
(This article belongs to the Special Issue Soil Organic Matter and Carbon Content Analysis Using Machine Learning and Classical Approaches)
►▼ Show Figures

Figure 1

15 pages, 12295 KB  
Article
A Comprehensive Evaluation of Machine Learning Algorithms for Digital Soil Organic Carbon Mapping on a National Scale
by Dorijan Radočaj, Danijel Jug, Irena Jug and Mladen Jurišić
Appl. Sci. 2024, 14(21), 9990; https://doi.org/10.3390/app14219990 - 1 Nov 2024
Cited by 6 | Viewed by 2841
Abstract
The aim of this study was to narrow the research gap of ambiguity in which machine learning algorithms should be selected for evaluation in digital soil organic carbon (SOC) mapping. This was performed by providing a comprehensive assessment of prediction accuracy for 15 [...] Read more.
The aim of this study was to narrow the research gap of ambiguity in which machine learning algorithms should be selected for evaluation in digital soil organic carbon (SOC) mapping. This was performed by providing a comprehensive assessment of prediction accuracy for 15 frequently used machine learning algorithms in digital SOC mapping based on studies indexed in the Web of Science Core Collection (WoSCC), providing a basis for algorithm selection in future studies. Two study areas, including mainland France and the Czech Republic, were used in the study based on 2514 and 400 soil samples from the LUCAS 2018 dataset. Random Forest was first ranked for France (mainland) and then ranked for the Czech Republic regarding prediction accuracy; the coefficients of determination were 0.411 and 0.249, respectively, which was in accordance with its dominant appearance in previous studies indexed in the WoSCC. Additionally, the K-Nearest Neighbors and Gradient Boosting Machine regression algorithms indicated, relative to their frequency in studies indexed in the WoSCC, that they are underrated and should be more frequently considered in future digital SOC studies. Future studies should consider study areas not strictly related to human-made administrative borders, as well as more interpretable machine learning and ensemble machine learning approaches. Full article
(This article belongs to the Special Issue Soil Organic Matter and Carbon Content Analysis Using Machine Learning and Classical Approaches)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop