Machine Learning Advances Research on Ecosystem Carbon and Nitrogen Cycles
A special issue of Biology (ISSN 2079-7737). This special issue belongs to the section "Ecology".
Deadline for manuscript submissions: 31 May 2026 | Viewed by 37
Special Issue Editors
Interests: ecosystem modeling; biogeochemical cycle; climate change
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing; change detection; natural disaster mapping; invasive species mapping; landcover mapping; vegetation property extraction with remote sensing techniques
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Understanding the complexity of biogeochemical cycles —especially those involving carbon and nitrogen—is crucial for addressing major environmental challenges such as climate change, land-use change, and ecosystem degradation. These cycles are integral to supporting vital ecosystem functions and maintaining the resilience of Earth's biosphere. Researchers have long used field observations, remote sensing, and ecosystem modeling to understand these processes across various regions and time scales. However, traditional methods often struggle to capture the full complexity and variability of these systems, particularly when dealing with large- or multi-scale data.
In recent years, machine learning (ML) and artificial intelligence (AI) have gained significant traction in the biological sciences. When applied to biogeochemical cycles, ML holds transformative potential to better integrate diverse datasets (such as satellite imageries and sensor networks), model complex nonlinear behaviors, and improve forecasts under changing environmental conditions.
We are pleased to invite you to submit your research to this Special Issue, which will focus on how machine learning is reshaping our understanding and modeling of ecosystem biogeochemical cycles, with a particular emphasis on carbon and nitrogen fluxes, and their responses to both anthropogenic and natural drivers. We invite original research articles and reviews on a range of topics, including (but not limited to) the following:
- ML applications in simulating carbon and nitrogen cycling at local to global scales.
- Hybrid modeling frameworks that combine ML and process-based ecosystem models.
- Data-driven predictions of greenhouse gas fluxes under climate and land-use change.
- ML-assisted model calibration, parameterization, and uncertainty quantification.
- AI-driven analysis of long-term monitoring data (e.g., chamber observations, flux towers, or remote sensing).
Dr. Fenghui Yuan
Dr. Tao Liu
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. Biology 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 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.
Keywords
- machine learning
- biogeochemical cycles
- ecosystem modeling
- carbon cycling
- nitrogen cycling
- greenhouse gas
- climate change
- land-use change
- data assimilation
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.
