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Remote Sensing for Monitoring Nutrients in Coastal and Inland Waters

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A Special Issue of Remote Sensing (ISSN 2072-4292) belonging to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: 28 February 2027 | Viewed by 1218

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Editors

Dr. Christos Domenikiotis

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Guest Editor

Department of Ichthyology and Aquatic Environment, School of Agricultural Sciences, University of Thessaly, 38446 Volos, Greece
Interests: remote sensing; spatial analysis; geographic information system; spatial statistics; coastal water quality

Prof. Dr. Nikos Neofitou

E-Mail Website
Guest Editor

Department of Ichthyology and Aquatic Environment, School of Agricultural Sciences, University of Thessaly, 38446 Volos, Greece
Interests: aquaculture; environment; nutrients; eutrophication; water quality assessment; water quality monitoring; environmental impact assessment
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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-anonymized 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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Research

28 pages, 29499 KB

Open AccessArticle

Disentangling Spectral and Environmental Controls on Inland River–Lake Water Quality Using Satellite Earth Observation-Driven Optimized Machine Learning

by Bazel Al-Shaibah, Xingpeng Liu, Ali R. Al-Aizari, Zhijun Tong, Jiquan Zhang, Soroush Abolfathi and Hassan Alzahrani

Remote Sens. 2026, 18(17), 2921; https://doi.org/10.3390/rs18172921 - 31 Aug 2026

Viewed by 291

Abstract

Accurate monitoring of surface water quality remains challenging due to pronounced spatial heterogeneity and limited ground observations. Satellite remote sensing offers scalable solutions, yet the extent to which environmental drivers enhance predictive performance, particularly in complex river–lake systems, remains insufficiently understood. This study develops a parallel comparative river–lake modeling framework to estimate permanganate index (CODmn), total phosphorus (TP), and total nitrogen (TN) by integrating satellite spectral data with climatic and land-use variables. Four model configurations were evaluated: spectral predictors alone (M₁), spectral predictors combined with climate variables (M₂), spectral predictors combined with land-use information (M₃), and full integration of all predictors (M₄). LightGBM models were optimized using Bayesian hyperparameter tuning (Optuna) and trained over rivers (January 2021–July 2025) and lakes (January 2021–December 2024) datasets in the Dongliao Basin, China. Spectral predictors alone (M₁) provided robust performance for CODmn (

R^{2}

= 0.78), with marginal improvement when land-use variables were included (M₂) in rivers (

R^{2}

= 0.80). In contrast, nutrient predictions showed stronger dependence on environmental covariates. TN predictions improved substantially with land-use inputs (

M 3

) (R² = 0.75 in rivers and 0.63 in lakes with M₂), with further gains with full integration (

M 4

) in lakes (

R^{2}

= 0.66). TP predictions exhibited marked improvements with land-use variables in rivers (R² = 0.76) and with full integration in lakes (

R^{2}

= 0.72). Model interpretability analysis using SHAP revealed that spectral features dominate CODmn estimation, while climatic and watershed characteristics exert greater influence on TN variability. Seasonal analysis indicated that hydrological drivers dominate during wet seasons, while land-use effects and internal biogeochemical processes become more important in dry seasons. The proposed framework advances predictive accuracy and process understanding, supporting more effective monitoring and management of water quality in complex river–lake systems. Full article

(This article belongs to the Special Issue Remote Sensing for Monitoring Nutrients in Coastal and Inland Waters)

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26 pages, 3769 KB

Open AccessArticle

Monitoring the Concentration of Dissolved Inorganic Nitrogen and Phosphorus at the Sea Surface Using a Hyperspectral Image—A Case Study of Sheyang Estuary, Yellow Sea

by Yong Xu and Dong Zhang

Remote Sens. 2026, 18(16), 2686; https://doi.org/10.3390/rs18162686 - 10 Aug 2026

Viewed by 383

Abstract

The concentrations of DIN and DIP are important indicators in an offshore ecosystem; although they do not have optical activity, their concentrations are affected by optically active substances, such as sediment, chlorophyll, and dissolved organic matter, an association that is especially close in coastal waters. This study aimed to identify this relationship to provide a theoretical basis for using remote sensing to monitor DIN/DIP concentrations. This study first used correlation analysis to analyze the relationship between water quality indicators and the field-measured spectrum in the Sheyang estuary. The results show a strong positive correlation between the DIN and DIP concentrations and spectrum in near-infrared range, similar to that between the suspended sediment concentrations and spectrum; this indicates a close relationship between DIN/DIP concentrations and sediment concentration in this sea area. Traditional regression models for DIN and DIP concentrations were constructed using the sensitive bank factors of a Hyperion image. By comparing the physical meaning of the factors and the precision and stability of the models, the quadratic model established by the ratio factor of 45th and 10th bands was selected as the DIN concentration inversion model, the quadratic model established by the ratio factor of the 45th and 9th bands was selected as the DIP inversion model, and the inversion results of the image conformed to the actual distribution pattern of DIN and DIP concentrations. In order to fully utilize the spectral information of the Hyperion data, the model coupled using partial least squares (PLS) and support vector machine (SVM) was used to construct regression models of DIN and DIP concentrations. By comparing the standardized coefficients of PLS regression, the 8~16th bands and 37~57th bands of the Hyperion image were selected; all these bands were extracted as two orthogonal components to construct the SVM regression model. Finally, the parameter combinations of radial basis model with C = 10, γ = 0.05, and ε = 0.1 and C = 1, γ = 0.1, and ε = 0.001 were determined as the inversion models for DIN and DIP concentrations, respectively. The prediction accuracy of the models was significantly improved compared to the traditional regression models, and the inversion results were superior to those of the traditional regression models, demonstrating the potential of this algorithm in hyperspectral image modeling. Full article

(This article belongs to the Special Issue Remote Sensing for Monitoring Nutrients in Coastal and Inland Waters)

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