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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: 28 February 2026 | Viewed by 1845

Special Issue Editors


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Guest Editor
Environmental Systems Research Institute (ESRI), Redlands, CA 92374, USA
Interests: water resource engineering; hydroinformatics; water resource management; geospatial and temporal analysis

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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

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Guest Editor

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 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

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

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Published Papers (1 paper)

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Research

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
Viewed by 402
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
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