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Water Quality Monitoring and Quantitative Analysis in Marine and Inland...
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Water Quality Monitoring and Quantitative Analysis in Marine and Inland Areas Utilizing Remote Sensing Techniques

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: 31 January 2026 | Viewed by 2502

Special Issue Editors

Dr. Jing Tan

E-Mail Website
Guest Editor
Scripps Institution of Oceanography, University of California San Diego, 8810 Shellback Way, La Jolla, CA 92093, USA
Interests: remote sensing; ocean color; water quality; eutrophication; spectral sensor
Dr. Ishan Joshi

E-Mail Website
Guest Editor
Marine Physical Laboratory, Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA 92093-0238, USA
Interests: ocean color remote sensing; water quality; data mining
Dr. Zhigang Cao

E-Mail Website
Guest Editor
Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, China
Interests: aquatic remote sensing; ocean color; water optics; lake environment and global change

Special Issue Information

Dear Colleagues,

Degraded water quality, driven by nutrient enrichment, harmful algal blooms (HABs), and other anthropogenic and environmental factors, poses significant threats to marine and inland aquatic ecosystems, biodiversity, and public health. These issues, often exacerbated by agricultural runoff, wastewater discharge, and climate change, impact ecological balance, economic activities, and water resource management. Monitoring and quantitatively analyzing water quality at regional and global scales is essential for the effective management and mitigation of these challenges.

Remote sensing, leveraging data from satellite, airborne, and drone platforms, has become a critical tool for monitoring and quantifying water quality in both marine and inland water systems. By integrating multi-platform remote sensing data—such as ocean color satellites, hyperspectral sensors, and high-resolution aircraft systems—researchers can assess spatial and temporal variations in key water quality indicators, including chlorophyll-a, nutrient concentrations, total suspended matter, dissolved organic matter, and HABs.

This Special Issue invites submissions that advance the application of remote sensing techniques for water quality monitoring and quantitatively analyzing water quality in marine and inland environments. We welcome contributions that present innovative methodologies, multi-platform data integration, and advancements in algorithms aimed at enhancing the understanding and management of water quality dynamics.

We welcome submissions on a wide range of topics, including but not limited to the following:

  • Application of multi-satellite and multi-sensor approaches for water quality monitoring in marine and inland waters.
  • Integration of satellite, aircraft, and drone platforms for high-resolution water quality assessments.
  • Remote sensing techniques for detecting and quantifying HABs and nutrient loading.
  • Use of hyperspectral data to analyze biogeochemical processes affecting water quality.
  • Development and application of algorithms for quantifying chlorophyll-a, phytoplankton composition, and other water quality parameters.
  • Quantitative assessment of water quality impacts on aquatic ecosystems using remote sensing.
  • Case studies on water quality monitoring in coastal, estuarine, and freshwater systems.
  • Combining remote sensing with in situ observations for comprehensive water quality analysis.
  • Assessing economic impacts of degraded water quality on fisheries, tourism, and water resources using remote sensing data.
  • Evaluating societal impacts of water quality issues, including public health risks and water management challenges, with remote sensing supporting policy decisions.
  • Modeling long-term impacts of water quality degradation on biodiversity and ecosystem services using remote sensing data.

We encourage interdisciplinary contributions that integrate remote sensing with oceanography, limnology, and biogeochemistry to address the challenges of water quality monitoring and management.

Dr. Jing Tan
Dr. Ishan Joshi
Dr. Zhigang Cao
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.

Keywords

  • ocean color
  • eutrophication
  • water quality
  • multi-sensor data
  • optical properties
  • atmospheric correction
  • marine ecosystems
  • remote sensing data

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Published Papers (2 papers)

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Research

22 pages, 14069 KB  
Article
Assessment of Atmospheric Correction Algorithms for Landsat-8/9 Operational Land Imager over Inland and Coastal Waters
by Yiqiang Hu, Haigang Zhan, Qingyou He and Weikang Zhan
Remote Sens. 2025, 17(17), 3055; https://doi.org/10.3390/rs17173055 - 2 Sep 2025
Cited by 1 | Viewed by 954
Abstract
Atmospheric correction (AC) over inland and coastal waters remains a key challenge in ocean color remote sensing, often limiting the effective use of satellite data for aquatic monitoring. AC algorithm performance is highly sensitive to water type and optical properties. To address this, [...] Read more.
Atmospheric correction (AC) over inland and coastal waters remains a key challenge in ocean color remote sensing, often limiting the effective use of satellite data for aquatic monitoring. AC algorithm performance is highly sensitive to water type and optical properties. To address this, we systematically evaluated six state-of-the-art AC algorithms—ACOLITE, C2RCC, iCOR, L2GEN, OC-SMART, and POLYMER—using Landsat-8/9 OLI data. This study leverages 440 high-quality in situ radiometric matchups spanning a wide range of aquatic environments, including inland lakes from China’s Satellite-Ground Synchronous Campaign and coastal waters from the globally distributed GLORIA dataset. These complementary datasets provide a robust benchmark for evaluating AC algorithm performance. A unified Optical Water Type (OWT) classification framework ensured consistency across environmental conditions. Results highlight significant variability in algorithm performance based on water type. In coastal waters, L2GEN demonstrated the lowest errors in visible bands, whereas OC-SMART achieved superior overall accuracy in inland waters. Notably, ACOLITE exhibited better performance than other algorithms in the blue spectral region (443 and 482 nm) for inland waters. OWT-specific analysis showed that OC-SMART maintained robust accuracy across the turbidity gradient, while ACOLITE and iCOR excelled in highly turbid waters (OWTs 5–6). In contrast, L2GEN, C2RCC, and POLYMER were more effective in clearer waters (OWTs 3–4). The study further discusses the applicability of each algorithm and offers recommendations for mitigating adjacency effects (AE) to improve AC accuracy. These findings provide valuable guidance for selecting and optimizing AC strategies for inland and coastal water monitoring. Full article
(This article belongs to the Special Issue Water Quality Monitoring and Quantitative Analysis in Marine and Inland Areas Utilizing Remote Sensing Techniques)
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24 pages, 2276 KB  
Article
Key Environmental Drivers of Summer Phytoplankton Size Class Variability and Decadal Trends in the Northern East China Sea
by Jung-Woo Park, Huitae Joo, Hyo Keun Jang, Jae Joong Kang, Joon-Soo Lee and Changsin Kim
Remote Sens. 2025, 17(11), 1954; https://doi.org/10.3390/rs17111954 - 5 Jun 2025
Viewed by 896
Abstract
Phytoplankton size classes (PSC), which categorize phytoplankton into pico- (<2 µm), nano- (2–20 µm), and microphytoplankton (>20 µm), have been widely used to describe functional group responses to environmental variability. Distribution of PSCs heavily influences marine ecosystems and biogeochemical processes. Despite the importance [...] Read more.
Phytoplankton size classes (PSC), which categorize phytoplankton into pico- (<2 µm), nano- (2–20 µm), and microphytoplankton (>20 µm), have been widely used to describe functional group responses to environmental variability. Distribution of PSCs heavily influences marine ecosystems and biogeochemical processes. Despite the importance of PSC distributions, especially in the face of climate change, long-term studies on PSC variability and its driving factors are lacking. This study aimed to identify the key environmental drivers affecting summer PSC variability in the northern East China Sea (NECS) by analyzing 27 years (1998–2024) of satellite-derived data. Statistical analyses using random forest and multiple linear regression models revealed that euphotic depth (Zeu) and suspended particulate matter (SPM) were the primary factors influencing PSC variation; deeper Zeu values favored smaller picophytoplankton, whereas higher SPM concentrations supported larger PSCs. Long-term trend analysis showed a clear shift toward increasing picophytoplankton contributions (+2.4% per year), with corresponding declines in nano- and microphytoplankton levels (2.2% and 0.4% annually, respectively). These long-term changes are hypothesized to result from a persistent decline in SPM concentrations, which modulate light attenuation and nutrient dynamics in the euphotic zone. Marine heat waves intensify these shifts by promoting picophytoplankton dominance through enhanced stratification and reduced nutrient availability. These findings underscore the need for continuous monitoring to inform ecosystem management and predict the impacts of climate change in the NECS. Full article
(This article belongs to the Special Issue Water Quality Monitoring and Quantitative Analysis in Marine and Inland Areas Utilizing Remote Sensing Techniques)
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