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Applied Sciences
Special Issues
Optical Satellites-Based Water Quality Estimation
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Optical Satellites-Based Water Quality Estimation

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

Deadline for manuscript submissions: closed (30 August 2022) | Viewed by 2190

Special Issue Editor

Dr. Yibo Zhang

E-Mail Website
Guest Editor
1. State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
2. University of the Chinese Academy of Sciences, Beijing 100049, China
Interests: inland water; transparency; chlorophyll-a; water quality; estimation; optical satellites; machine learning algorithms; deep learning algorithms

Special Issue Information

Dear Colleagues,

Lakes are of vital importance due to providing multiple socioeconomic and ecosystem services, including drinking water, irrigation and generating tourism to the fast-growing human world population. The strong relationships between optically active constituents and remote sensing signals provide a possibility to capture optical parameter information through a remote sensing approach. In this Special Issue of Applied Sciences regarding inland water quality estimation based on optical satellites, we welcome articles centered around the model development and/or application of remote sensing approaches to understand water quality patterns, trends and reasons behind drivers. 

Dr. Yibo Zhang
Guest Editor

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. 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

  • lake
  • water color
  • water quality
  • estimation
  • remote sensing
  • climate change
  • anthropogenic activities

Published Papers (1 paper)

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Research

16 pages, 5182 KiB  
Article
Sparse Representing Denoising of Hyperspectral Data for Water Color Remote Sensing
by Yulong Guo, Qingsheng Bi, Yuan Li, Chenggong Du, Junchang Huang, Weiqiang Chen, Lingfei Shi and Guangxing Ji
Appl. Sci. 2022, 12(15), 7501; https://doi.org/10.3390/app12157501 - 26 Jul 2022
Cited by 3 | Viewed by 1395
Abstract
Hyperspectral data are important for water color remote sensing. The inevitable noise will devalue its application. In this study, we developed a 1-D denoising method for water hyperspectral data, based on sparse representing. The denoising performance was compared with three commonly used methods [...] Read more.
Hyperspectral data are important for water color remote sensing. The inevitable noise will devalue its application. In this study, we developed a 1-D denoising method for water hyperspectral data, based on sparse representing. The denoising performance was compared with three commonly used methods in simulated and real datasets. The results indicate that: (1) sparse representing can successfully decompose the hyperspectral water-surface reflectance signal from random noises; (2) the proposed method exhibited better performance compared with the other three methods in different input signal-to-noise ratio (SNR) levels; (3) the proposed method effectively erased abnormal spectral vibrations of field-measured and remote-sensing hyperspectral data; (4) whilst the method is built in 1-D, it can still control the salt-and-pepper noise of PRISMA hyperspectral image. In conclusion, the proposed denoising method can improve the hyperspectral data of an optically complex water body and offer a better data source for the remote monitoring of water color. Full article
(This article belongs to the Special Issue Optical Satellites-Based Water Quality Estimation)
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