Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.3
4.2
Journals
Remote Sensing
Special Issues
Recent Advances in Water Resources and Water Environmental Monitoring with...
remotesensing-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Recent Advances in Water Resources and Water Environmental Monitoring with Remote Sensing Techniques

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: 15 September 2024 | Viewed by 1423

Special Issue Editors

Prof. Dr. Xuchu Yu

E-Mail Website
Guest Editor
College of Surveying and Geo-informatics, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
Interests: Photogrammetry; remote sensing; pattern recognition
Dr. Bing Liu

E-Mail Website
Guest Editor
Department of Big Data Analysis, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
Interests: hyperspectral remote sensing; change detection; water extraction from remote sensing images
Dr. Qingyu Li

E-Mail Website
Guest Editor
Data Science in Earth Observation, Technische Universität München (TUM), Munich, Germany
Interests: remote sensing image understanding; remote sensing application; urban analysis; deep learning algorithms

Special Issue Information

Dear Colleagues,

Human beings live by water, and civilization thrives on water. The monitoring of water resources and the water environment is of great significance regarding the scientific use of water resources. Remote sensing data can provide rich spectral and spatial information for ground objects, making it possible to quantitatively monitor surface water resources and the water environment on a large scale. In this context, the processing and analysis techniques employed to obtain massive remote sensing images are crucial for monitoring water resources and the water environment.

Water resource and water environment monitoring includes the monitoring of water quality, water quantity and hydrology. With the rapid development of remote sensing technology and artificial intelligence, many novel technologies and methods have emerged regarding the application of water quality, water quantity and hydrology monitoring. This Special Issue aims to present studies that address the various uses of remote sensing data and techniques in water quality, water quantity and hydrology monitoring.

Water resources and water environment monitoring is one of the typical application scenarios of remote sensing technology. The research in this direction will promote the development and application of remote sensing technology. Therefore, the subject is suitable for the scope of Remote Sensing.

The scope of this Special Issue includes, but is not limited to, the following:

  • Intelligent estimation method of water level and water volume.
  • Deep learning for hydrology
  • Data-driven hydrologic process learning.
  • Intelligent extraction of waters with remote sensing images.
  • Inversion models of water quality parameters.
  • Detection and analysis of water changes with remote sensing images.
  • Water pollution identification with remote sensing images.
  • Novel application of remote sensing techniques in water resources and water environment monitoring.
  • Deep learning and large model applied to water resources and water environment monitoring.
  • Novel application of geographic information systems in water resources and environmental monitoring.

Prof. Dr. Xuchu Yu
Dr. Bing Liu
Dr. Qingyu Li
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

  • remote sensing hydrological model
  • hydrologic process
  • water level monitoring
  • water volume monitoring
  • water body extraction
  • inversion models
  • change detection
  • deep learning
  • large model
  • water pollution identification
  • geographic information systems

Published Papers (2 papers)

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

Research

18 pages, 5080 KiB  
Article
SPT-UNet: A Superpixel-Level Feature Fusion Network for Water Extraction from SAR Imagery
by Teng Zhao, Xiaoping Du, Chen Xu, Hongdeng Jian, Zhipeng Pei, Junjie Zhu, Zhenzhen Yan and Xiangtao Fan
Remote Sens. 2024, 16(14), 2636; https://doi.org/10.3390/rs16142636 - 18 Jul 2024
Viewed by 353
Abstract
Extracting water bodies from synthetic aperture radar (SAR) images plays a crucial role in the management of water resources, flood monitoring, and other applications. Recently, transformer-based models have been extensively utilized in the remote sensing domain. However, due to regular patch-partition and weak [...] Read more.
Extracting water bodies from synthetic aperture radar (SAR) images plays a crucial role in the management of water resources, flood monitoring, and other applications. Recently, transformer-based models have been extensively utilized in the remote sensing domain. However, due to regular patch-partition and weak inductive bias, transformer-based models face challenges such as edge serration and high data dependency when used for water body extraction from SAR images. To address these challenges, we introduce a new model, the Superpixel-based Transformer (SPT), based on the adaptive characteristic of superpixels and knowledge constraints of the adjacency matrix. (1) To mitigate edge serration, the SPT replaces regular patch partition with superpixel segmentation to fully utilize the internal homogeneity of superpixels. (2) To reduce data dependency, the SPT incorporates a normalized adjacency matrix between superpixels into the Multi-Layer Perceptron (MLP) to impose knowledge constraints. (3) Additionally, to integrate superpixel-level learning from the SPT with pixel-level learning from the CNN, we combine these two deep networks to form SPT-UNet for water body extraction. The results show that our SPT-UNet is competitive compared with other state-of-the-art extraction models, both in terms of quantitative metrics and visual effects. Full article
(This article belongs to the Special Issue Recent Advances in Water Resources and Water Environmental Monitoring with Remote Sensing Techniques)
►▼ Show Figures

Figure 1

25 pages, 19977 KiB  
Article
Different Vegetation Covers Leading to the Uncertainty and Consistency of ET Estimation: A Case Study Assessment with Extended Triple Collocation
by Xiaoxiao Li, Huaiwei Sun, Yong Yang, Xunlai Sun, Ming Xiong, Shuo Ouyang, Haichen Li, Hui Qin and Wenxin Zhang
Remote Sens. 2024, 16(13), 2484; https://doi.org/10.3390/rs16132484 - 6 Jul 2024
Viewed by 524
Abstract
Accurate and reliable estimation of actual evapotranspiration (AET) is essential for various hydrological studies, including drought prediction, water resource management, and the analysis of atmospheric–terrestrial carbon exchanges. Gridded AET products offer potential for application in ungauged areas, but their uncertainties may be significant, [...] Read more.
Accurate and reliable estimation of actual evapotranspiration (AET) is essential for various hydrological studies, including drought prediction, water resource management, and the analysis of atmospheric–terrestrial carbon exchanges. Gridded AET products offer potential for application in ungauged areas, but their uncertainties may be significant, making it difficult to identify the best products for specific regions. While in situ data directly estimate gridded ET products, their applicability is limited in ungauged areas that require FLUXNET data. This paper employs an Extended Triple Collocation (ETC) method to estimate the uncertainty of Global Land Evaporation Amsterdam Model (GLEAM), Famine Early Warning Systems Network (FLDAS), and Maximum Entropy Production (MEP) AET product without requiring prior information. Subsequently, a merged ET product is generated by combining ET estimates from three original products. Furthermore, the study quantifies the uncertainty of each individual product across different vegetation covers and then compares three original products and the Merged ET with data from 645 in situ sites. The results indicate that GLEAM covers the largest area, accounting for 39.1% based on the correlation coefficient criterion and 39.9% based on the error variation criterion. Meanwhile, FLDAS and MEP exhibit similar performance characteristics. The merged ET derived from the ETC method demonstrates the ability to mitigate uncertainty in ET estimates in North American (NA) and European (EU) regions, as well as tundra, forest, grassland, and shrubland areas. This merged ET could be effectively utilized to reduce uncertainty in AET estimates from multiple products for ungauged areas. Full article
(This article belongs to the Special Issue Recent Advances in Water Resources and Water Environmental Monitoring with Remote Sensing Techniques)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop