Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.5
2.5
Journals
Applied Sciences
Special Issues
Remote Sensing Technologies in Hydrology and Water Resource Analysis
applsci-logo
Submit to Applied Sciences Review for Applied Sciences Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Remote Sensing Technologies in Hydrology and Water Resource Analysis

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".

Deadline for manuscript submissions: closed (10 December 2025) | Viewed by 267

Special Issue Editors

Dr. Santiago Yepez

E-Mail Website
Guest Editor
Departamento Manejo de Bosques y Medio Ambiente, Facultad de Ciencias Forestales, Universidad de Concepción, Concepción 4030000, Chile
Interests: remote sensing; hydrology; water resources
Dr. Jordi Cristóbal Rosselló

E-Mail Website
Guest Editor
Department of Geography, Universitat Autònoma de Barcelona, Campus de Bellaterra, Edifici B, Carrer de la Fortuna, s/n, 08193 Bellaterra, Spain
Interests: hydrology; remote sensing; ecohydrology; water resources; hyperspectral
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing technologies are transforming the way we observe and analyze hydrological systems, providing detailed insights into river morphology, sediment transport, water quality, and discharge dynamics at multiple spatial and temporal scales. The aim of this Special Issue is to bring together cutting-edge research that integrates remote sensing, hydrological modeling, and geomorphological analysis to support sustainable water resource management.

Contributions focusing on the use of satellite imagery, UAV-based multispectral and hyperspectral sensors, LiDAR, and structure-from-motion (SfM) photogrammetry to map and monitor river corridors are welcome. Special emphasis is placed on the characterization of planform evolution, fluvial dynamics, and sediment transport using Earth observation data, including the application of object-based image analysis (OBIA) and artificial intelligence for feature extraction.

This Special Issue also encourages the integration of hydroacoustic methods, such as acoustic Doppler current profilers (ADCPs), with remote sensing approaches to improve quantification of suspended sediment concentrations and sediment fluxes in rivers. In addition, we welcome studies that incorporate remotely sensed data into hydrological and river flow modeling frameworks to improve flow estimation, flood forecasting, and water resource assessment at the basin scale.

By promoting interdisciplinary contributions, this Special Issue aims to advance the use of remote sensing technologies in hydrology, river science, and environmental monitoring.

Dr. Santiago Yepez
Dr. Jordi Cristóbal Rosselló
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. Applied Sciences 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 2400 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

  • fluvial geomorphology
  • sediment transport
  • hydroacoustic methods (ADCP)
  • streamflow and discharge modeling
  • UAV multispectral and hyperspectral imagery
  • object-based image analysis (OBIA)
  • river planform change
  • artificial intelligence in remote sensing
  • water quality monitoring
  • hydrological modeling

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

21 pages, 10595 KB  
Article
Hyperspectral Remote Sensing of TN:TP Ratio Using CNN-SVR: Unveiling Nutrient Limitation in Eutrophic Lakes
by Fazhi Xie, Lanlan Huang, Wuyiming Liu, Qianfeng Gao, Jiwei Zhou and Banglong Pan
Appl. Sci. 2026, 16(2), 1098; https://doi.org/10.3390/app16021098 - 21 Jan 2026
Abstract
The nitrogen-to-phosphorus ratio (TN:TP) is a key indicator influencing phytoplankton nutrient limitation and growth dynamics, directly regulating algal growth rates, abundance, and community structure, thereby affecting the process of water eutrophication. This study aims to evaluate the modeling performance of integrated machine learning [...] Read more.
The nitrogen-to-phosphorus ratio (TN:TP) is a key indicator influencing phytoplankton nutrient limitation and growth dynamics, directly regulating algal growth rates, abundance, and community structure, thereby affecting the process of water eutrophication. This study aims to evaluate the modeling performance of integrated machine learning approaches for lake total nitrogen to total phosphorus ratios (TN:TP), utilizing Zhuhai-1 hyperspectral satellite imagery to develop a CNN-SVR ensemble model integrating convolutional neural networks and support vector regression for remote sensing inversion of lake TN:TP ratios. Performance is evaluated against random forest (RF) and convolutional neural network (CNN) models, systematically analyzing spatial distribution patterns and primary drivers. Results indicate that the CNN-SVR model demonstrated superior performance among the tested models, with R2, RMSE, MAPD, and RPD values of 0.856, 2.675, 9.516%, and 2.390, respectively. Spatially, the nitrogen-to-phosphorus ratio in lakes during the growing season exhibits an increasing trend from the western to the eastern half of the lake, progressing from northwest to southeast. When TN:TP falls below 9, algal growth becomes nitrogen-limited, indicating a higher degree of eutrophication; when TN:TP exceeds 22.6, phosphorus becomes the limiting factor, indicating lower eutrophication levels. A similar distribution pattern is observed during the non-growing season. Regarding driving mechanisms, the nitrogen-to-phosphorus ratio during the growing season is primarily influenced by TN accumulation and shows significant correlations with dissolved oxygen (DO) and pH. During the non-growing season, while still affected by TN input, its association with other water quality parameters is weaker. The results indicate that the combined use of CNN and SVR improves feature extraction and model fitting in nitrogen-to-phosphorus ratio inversion and helps clarify its ecological significance as an indicator of algal growth. This provides methodologies and evidence for precise diagnosis and ecological management of lake eutrophication. Full article
(This article belongs to the Special Issue Remote Sensing Technologies in Hydrology and Water Resource Analysis)
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop