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Remote Sensing
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Recent Advances in Remote Sensing Products for Water and Environment...
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Recent Advances in Remote Sensing Products for Water and Environment Monitoring

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

Deadline for manuscript submissions: 31 August 2026 | Viewed by 3525

Special Issue Editors

Dr. Gonzalo E. Espinoza-Dávalos

E-Mail Website
Guest Editor
Environmental Systems Research Institute (ESRI), Redlands, CA 92374, USA
Interests: water resource engineering; hydroinformatics; water resource management; geospatial and temporal analysis
Dr. Mhd. Suhyb Salama

E-Mail Website
Guest Editor
Faculty of Geo-Information Science and Earth Observation, University of Twente, Hengelosestraat 99, 7514 AE Enschede, The Netherlands
Interests: water quality; validation; radiative transfer; inland and coastal waters; science valorization
Special Issues, Collections and Topics in MDPI journals
Dr. Marouane Temimi

E-Mail Website
Guest Editor
Department of Civil, Environmental, and Ocean Engineering (CEOE), Stevens Institute of Technology, Hoboken, NJ 07030, USA
Interests: hydrometeorology; remote sensing; water resources
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing products have enhanced our capacity to monitor water resources and the environment, providing novel global datasets of Earth observations. The integration of remote sensing data in hydrologic models improves our understanding of water systems and positively influences water management practices. The use of remote sensing products in water resources is well-established. However, there is potential for further improvement in the areas of validation with in situ measurements, development of hydroinformatic applications for sharing results in near real-time, and the analysis of long-term trends and extreme events. The development and integration of remote sensing products for the monitoring of water and the environment will continue to experience significant growth in the coming years. The assessment of remote sensing products is crucial for improving reliability across different temporal and spatial scales, reducing modeling uncertainty, and increasing overall confidence in supporting water resource management tools and applications.

This Special Issue aims to include research studies that contribute to highlighting recent advances in the integration of remote sensing products for water resources applications. Studies that expand the perspective of the use of remote sensing in hydrologic modeling, water resources management, validation with in situ measurements, early warning systems for extreme events, and dissemination of information in hydroinformatic web applications are highly encouraged.

Original research articles, review articles, or technical are welcome on the following research lines:

  • Use of remote sensing products in hydrologic modeling;
  • Remote sensing for water resource management applications;
  • Comparison and validation of remote sensing data with in situ monitoring networks;
  • Hydroinformatic web applications and data dissemination;
  • Integration of remote sensing products in disaster response and early warning systems;
  • Assessment of extreme events on water resources.

Dr. Gonzalo E. Espinoza-Dávalos
Dr. Mhd. Suhyb Salama
Dr. Marouane Temimi
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

  • water resources
  • water fluxes
  • hydrologic modeling
  • Earth observations
  • environmental monitoring
  • hydroinformatics

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

27 pages, 3395 KB  
Article
Probabilistic Water Quality Monitoring Using Multi-Temporal Sentinel-2 Data: A Situational Awareness Framework for Harmful Algal Bloom Forecasting
by Muhammad Zaid Qamar, Cristiano Ciccarelli, Mohammed Ajaoud and Massimiliano Lega
Remote Sens. 2026, 18(6), 959; https://doi.org/10.3390/rs18060959 - 23 Mar 2026
Viewed by 496
Abstract
Environmental monitoring systems require robust uncertainty quantification for effective decision-making in complex ecological processes. Harmful algal blooms represent a critical challenge where prediction uncertainty directly impacts resource allocation and response timing, yet current remote sensing-based prediction systems provide only deterministic classifications without confidence [...] Read more.
Environmental monitoring systems require robust uncertainty quantification for effective decision-making in complex ecological processes. Harmful algal blooms represent a critical challenge where prediction uncertainty directly impacts resource allocation and response timing, yet current remote sensing-based prediction systems provide only deterministic classifications without confidence measures. This gap between algorithmic predictions and actionable risk assessment limits operational utility for stakeholders managing water quality under varying risk tolerances. This study developed a transferable probabilistic forecasting framework integrating Sentinel-2 multispectral imagery with quantile regression and ensemble machine learning to generate continuous confidence indicators for cyanobacteria density prediction, demonstrated through its application to Lake Okeechobee, Florida. The methodology combines spectral indices extracted from Sentinel-2 data with XGBoost for quantile regression at 0.05, 0.50, and 0.95 probability levels, and LightGBM for multi-horizon temporal forecasting. Sentinel-2’s 13 spectral bands spanning visible to shortwave infrared wavelengths, combined with its 5-day revisit frequency provide a spectrally rich and temporally dense input space that is well-suited to gradient boosting methods such as XGBoost, which can exploit complex nonlinear interactions among spectral features to distinguish cyanobacterial signatures from background water constituents. LightGBM achieved mean absolute percentage errors of 2.9% for 10-day forecasts and 5.7% for 20-day forecasts, outperforming conventional regression models. The framework generates 90% prediction intervals that enable reliable risk classifications for operational bloom management. This approach bridges the gap between satellite-based algal bloom detection and actionable decision-making by quantifying predictive uncertainty, representing a shift from binary classifications to probability-based environmental monitoring systems that accommodate varying stakeholder risk tolerances in water quality management applications. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing Products for Water and Environment Monitoring)
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29 pages, 10745 KB  
Article
Assessing the Feasibility of Satellite-Based Machine Learning for Turbidity Estimation in the Dynamic Mersey Estuary (Case Study: River Mersey, UK)
by Deelaram Nangir, Manolia Andredaki and Iacopo Carnacina
Remote Sens. 2025, 17(21), 3617; https://doi.org/10.3390/rs17213617 - 31 Oct 2025
Cited by 1 | Viewed by 1284
Abstract
The monitoring of turbidity in estuarine environments is a challenging essential task for managing water quality and ecosystem health. This study focuses on the lower reaches of the River Mersey, Liverpool. Harmonized Sentinel-2 MSI Level-2A imagery was integrated with in situ measurements from [...] Read more.
The monitoring of turbidity in estuarine environments is a challenging essential task for managing water quality and ecosystem health. This study focuses on the lower reaches of the River Mersey, Liverpool. Harmonized Sentinel-2 MSI Level-2A imagery was integrated with in situ measurements from seven Environment Agency monitoring stations for two consecutive years (January 2023–January 2025). The workflow included image preprocessing, spectral index calculation, and the application of four machine learning algorithms: Gradient Boosting Regressor, XGBoost, Support Vector Regressor, and K-Nearest Neighbors. Among these, Gradient Boosting Regressor achieved the highest predictive accuracy (R2 = 0.84; RMSE = 15.0 FTU), demonstrating the suitability of ensemble tree-based methods for capturing non-linear interactions between spectral indices and water quality parameters. Residual analysis revealed systematic errors linked to tidal cycles, depth variation, and salinity-driven stratification, underscoring the limitations of purely data-driven approaches. The novelty of this study lies in demonstrating the feasibility and proof-of-concept of using machine learning to derive spatially explicit turbidity estimates under data-limited estuarine conditions. These results open opportunities for future integration with Computational Fluid Dynamics models to enhance temporal forecasting and physical realism in estuarine monitoring systems. The proposed methodology contributes to sustainable coastal management, pollution monitoring, and climate resilience, while offering a transferable framework for other estuaries worldwide. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing Products for Water and Environment Monitoring)
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