Remote Sensing for Monitoring Nutrients in Coastal and Inland Waters
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".
Deadline for manuscript submissions: 30 June 2026 | Viewed by 22
Special Issue Editors
Interests: remote sensing; spatial analysis; geographic information system; spatial statistics; coastal water quality
Interests: aquaculture; environment; nutrients; eutrophication; water quality assessment; water quality monitoring; environmental impact assessment
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues，
Nutrient enrichment driven by human activities (e.g., agriculture, aquaculture, industry, or urban wastewater) and climate change-influenced natural processes (e.g., increased temperature or flood-derived nutrient inputs) may increase primary production beyond the carrying capacity of coastal and inland ecosystems, leading to eutrophication. There is an imperative need for timely management actions to prevent biodiversity loss and ecosystem degradation, as their consequences might impact local communities and economies. In ocean color remote sensing, the assessment of eutrophication frequently relies on chlorophyll a (chl a) estimation, as it represents the measurable phytoplankton response. However, monitoring of nutrients is also crucial, as they serve as key parameters to predict phytoplankton growth and implement preventive measures. In contrast to chl a, estimating nutrients via remote sensing is challenging due to their non-optical nature. Existing models are site-specific and lack validation across varied regions and seasons, highlighting the need for greater transferability and further research.
This Special Issue aims to highlight recent advances and the current state of the art in quantitative nutrient concentration retrieval using remote sensing techniques.
Article types may include original research articles, reviews, technical notes, and communications that employ data from spaceborne, airborne/UAVs, or ground-based multispectral or hyperspectral sensors. The topics should cover, but not be limited to, the following:
- In situ data collection strategies for predictive model input.
- Multi-sensor integration.
- Novel approaches for optimal band selection and combination.
- Models employed in nutrient retrieval: empirical, semi-empirical, analytical, or AI.
- Classification techniques.
- Climate change impact.
Dr. Christos Domenikiotis
Prof. Dr. Nikos Neofitou
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. 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.
Keywords
- remote sensing
- water quality
- nutrients
- eutrophication
- coastal waters
- inland waters
- regression
- classification
- machine learning
- deep learning
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