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Monitoring and Assessment of Watershed Aquatic Ecosystems Based on Remote Sensing Technology

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ecological Remote Sensing".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 653

Editors

Dr. Nan Shan

E-Mail Website
Guest Editor
Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing, China
Interests: ecosystem carbon and water cycle model optimization and remote sensing simulation; watershed aquatic ecosystem monitoring and assessment
Dr. Qian Zhang

E-Mail Website
Guest Editor
School of Geometics Science and Technology, Nanjing Tech University, Nanjing 211816, China
Interests: quantitative remote sensing; carbon cycle; plant photosynthesis; aboveground biomass; spectral observation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Against the backdrop of global climate change and increasing anthropogenic pressure, watershed aquatic ecosystems are facing unprecedented challenges, including water quality degradation, habitat loss and biodiversity decline. The accurate and spatially explicit monitoring of these dynamic systems is essential for effective ecosystem management and conservation. Remote sensing technology provides unique capabilities for large-scale, repeatable and high-frequency observations of aquatic environments, enabling the retrieval of key water quality parameters (e.g., chlorophyll-a, total suspended matter, colored dissolved organic matter), the mapping of aquatic vegetation distribution and the assessment of hydrological connectivity and ecosystem health.

This Special Issue highlights recent advances in the application of remote sensing technology for the monitoring and assessment of watershed aquatic ecosystems. It brings together innovative studies on water quality parameter retrieval, aquatic habitat mapping, spatiotemporal dynamics analysis and machine-learning-driven ecological assessment, addressing critical challenges in watershed ecosystem monitoring. The Special Issue aims to provide theoretical and technical support for aquatic ecosystem protection, water resource management and ecological restoration.

This Special Issue invites innovative research that advances the application of remote sensing technology in the monitoring and assessment of watershed aquatic ecosystems. We welcome contributions focused on developing novel methods for aquatic parameter retrieval, exploring multi-sensor data fusion techniques and utilizing machine learning and artificial intelligence to improve the accuracy and interpretability of aquatic ecosystem assessments. The scope encompasses theoretical developments in aquatic remote sensing, methodological innovations in water quality monitoring and habitat mapping and practical applications addressing challenges in ecosystem health assessment, trophic state evaluation and watershed-scale ecological monitoring.

We welcome original research papers, review articles and methodological contributions on a wide range of topics, including, but not limited to, the following:

  • Retrieval of water quality parameters (chlorophyll-a, suspended matter, colored dissolved organic matter) using optical and hyperspectral remote sensing
  • Mapping and monitoring of aquatic vegetation (submerged, emergent and floating plants)
  • Assessment of trophic state and eutrophication in lakes, reservoirs and rivers
  • Detection of algal blooms and cyanobacteria using multi-source satellite data
  • Spatiotemporal dynamics of water extent and hydrological connectivity
  • Multi-sensor data fusion (optical, SAR and UAV) for high-resolution aquatic monitoring
  • Validation and uncertainty quantification of satellite-derived aquatic products
  • Case studies on watershed-scale aquatic ecosystem assessment and restoration

Researchers are encouraged to submit original research papers and review articles to explore the latest advances in remote sensing technology and its applications in the monitoring and assessment of watershed aquatic ecosystems.

Dr. Nan Shan
Dr. Qian Zhang
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 parameter retrieval
  • eutrophication assessment
  • evapotranspiration
  • watershed management
  • river flow
  • chlorophyll-a concentration
  • drought and flood monitoring
  • watershed ecosystem assessment

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Published Papers (1 paper)

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Research

21 pages, 7333 KB  
Article
Bloom or Bluff? Benchmarking Vision–Language Models Against Classical Machine Learning for Harmful Algal Bloom Detection from Satellite Imagery
by Harsh Deep Singh Narula
Remote Sens. 2026, 18(13), 2147; https://doi.org/10.3390/rs18132147 - 2 Jul 2026
Viewed by 443
Abstract
In recent years, there has been growing interest in applying vision–language models (VLMs) to quantitative remote sensing. This study evaluates whether three commercial VLMs (GPT-4o, GPT-5.5, and Claude Sonnet 4.6) can detect and classify the severity of harmful algal blooms (HABs) from Sentinel-2 [...] Read more.
In recent years, there has been growing interest in applying vision–language models (VLMs) to quantitative remote sensing. This study evaluates whether three commercial VLMs (GPT-4o, GPT-5.5, and Claude Sonnet 4.6) can detect and classify the severity of harmful algal blooms (HABs) from Sentinel-2 satellite imagery of western Lake Erie and compares them against classical machine learning classifiers (Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost)) trained on both a three-band red, green, blue (RGB) composite representation of the imagery and a 10-band multi-spectral reflectance representation. Forty bloom events identified from the National Oceanic and Atmospheric Administration (NOAA) Harmful Algal Bloom Operational Forecast System (HAB-OFS) severity assessments were assembled into the evaluation dataset, spanning seven bloom seasons (2019–2025). For binary bloom detection, the VLMs did not match the classical RGB classifiers; their F1 scores (0.69–0.75) fell below the best RGB classifier (Random Forest, 0.76) and below a trivial always-present baseline (F1 = 0.77), and they carried false positive rates of 73–93% on bloom-absent images, against 27–40% for the RGB classifiers. The VLMs reached high recall by labeling most scenes as bloom-positive, which makes them operationally unreliable in this configuration. For severity classification, the VLMs assigned 60–70% of their predictions to the “moderate” category regardless of actual conditions and identified at most one of the two severe blooms, whereas the classical classifiers tracked the ground-truth distribution and delivered two to nearly three times the exact-match accuracy (0.44–0.59 vs. 0.20–0.225). The strongest method across all metrics was the multi-spectral SVM (F1 = 0.833, false positive rate 27%, accuracy 0.795). Switching the same SVM from RGB to multi-spectral features raised accuracy from 0.675 to 0.795, a 12-percentage-point gain that measures the spectral information carried by red-edge and shortwave infrared bands that are accessible through multi-spectral sensors but unavailable to standard VLM vision encoders. Feature-importance analysis showed that the multi-spectral classifiers ranked chlorophyll-specific indices, the Normalized Difference Chlorophyll Index (NDCI) and the Floating Algae Index (FAI), among their top predictors, the same signatures used in established operational algorithms, while the RGB classifiers relied on red-channel variability and green-dominant pixel fractions because RGB inputs cannot compute those indices. Two compounded limitations therefore constrain off-the-shelf VLMs for aquatic remote sensing: the limited spectral information available through standard RGB channels and a mismatch between the land-dominated training distributions of these models and aquatic optical conditions. Domain-specific classifiers operating on multi-spectral data remain the more suitable tools for continued development of HAB monitoring and water-quality retrieval. Full article
(This article belongs to the Special Issue Monitoring and Assessment of Watershed Aquatic Ecosystems Based on Remote Sensing Technology)
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