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Remote Sensing
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Water Optics and Water Colour Remote Sensing 2.0
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Water Optics and Water Colour Remote Sensing 2.0

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

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 3217

Special Issue Editors

Prof. Dr. Yunlin Zhang

E-Mail Website
Guest Editor
Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, 73 East Beijing Road, Nanjing 210008, China
Interests: lake optics and water color remote sensing; chromophoric dissolved organic matter (CDOM) biogeochemistry cycle; UV-B radiation environmental effect; physical limnology; lake eutrophication; lake thermodynamics
Special Issues, Collections and Topics in MDPI journals
Dr. Chengfeng Le

E-Mail Website
Guest Editor
Ocean College, Zhejiang University, Hangzhou 310027, China
Interests: ocean optics; ocean color remote sensing; algal bloom; coastal and estuarine waters
Dr. Lin Qi

E-Mail Website
Guest Editor
School of Marine Sciences, Sun Yat-Sen University, Guangzhou 510275, China
Interests: ocean optics; ocean color remote sensing; algal bloom; coastal and estuarine waters

Special Issue Information

Dear Colleagues,

Climate change and human activities are exerting extensive effects on aquatic ecosystems, which results in the loss of many ecological goods and services. Thus, long-term and high-resolution monitoring of the physical, chemical, and biological status and evolution processes of aquatic ecosystems are vital to improving our understanding of the responses and feedbacks of aquatic ecosystems to climate change and human activities. Water color remote sensing has the potential to provide long-term observation and high temporal–spatial resolution monitoring from local through to regional and global scales. However, the development of accurate, cost-effective, frequent, and synoptic retrieval algorithms for acquiring in-water optical and biogeochemical parameters as well as information on the biophysical properties is facing several challenges.

This Special Issue “Water Optics and Water Colour Remote Sensing 2.0” is a continuation of the first volume, and also aims to address the following: (1) issues on water optics including characterizing optical properties covering diverse aquatic ecosystems, modeling bio-optical and radiative transfer processes and (2) challenges regarding retrieval algorithm development, validation, and applications. This Special Issue, which serves as an update of our previous issue published in 2017, will include the recent progress in this rapidly advancing research area.

The topics, examined at local, regional, or global scales, may include, but are not limited to the following:

  • Water optics
  • Bio-optical properties
  • Remote sensing
  • Water color satellite
  • Phytoplankton dynamics
  • Algal bloom
  • Chromophoric dissolved organic matter, dissolved organic carbon, particulate organic carbon

Prof. Dr. Yunlin Zhang
Dr. Chengfeng Le
Dr. Lin Qi
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 optics
  • Bio-optical properties
  • Remote sensing
  • Water color satellite
  • Phytoplankton dynamics
  • Algal bloom
  • Chromophoric dissolved organic matter, dissolved organic carbon, particulate organic carbon

Published Papers (1 paper)

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Research

17 pages, 6464 KiB  
Article
Machine Learning Algorithms for Chromophoric Dissolved Organic Matter (CDOM) Estimation Based on Landsat 8 Images
by Xiao Sun, Yunlin Zhang, Yibo Zhang, Kun Shi, Yongqiang Zhou and Na Li
Remote Sens. 2021, 13(18), 3560; https://doi.org/10.3390/rs13183560 - 07 Sep 2021
Cited by 16 | Viewed by 2746
Abstract
Chromophoric dissolved organic matter (CDOM) is crucial in the biogeochemical cycle and carbon cycle of aquatic environments. However, in inland waters, remotely sensed estimates of CDOM remain challenging due to the low optical signal of CDOM and complex optical conditions. Therefore, developing efficient, [...] Read more.
Chromophoric dissolved organic matter (CDOM) is crucial in the biogeochemical cycle and carbon cycle of aquatic environments. However, in inland waters, remotely sensed estimates of CDOM remain challenging due to the low optical signal of CDOM and complex optical conditions. Therefore, developing efficient, practical and robust models to estimate CDOM absorption coefficient in inland waters is essential for successful water environment monitoring and management. We examined and improved different machine learning algorithms using extensive CDOM measurements and Landsat 8 images covering different trophic states to develop the robust CDOM estimation model. The algorithms were evaluated via 111 Landsat 8 images and 1708 field measurements covering CDOM light absorption coefficient a(254) from 2.64 to 34.04 m−1. Overall, the four machine learning algorithms achieved more than 70% accuracy for CDOM absorption coefficient estimation. Based on model training, validation and the application on Landsat 8 OLI images, we found that the Gaussian process regression (GPR) had higher stability and estimation accuracy (R2 = 0.74, mean relative error (MRE) = 22.2%) than the other models. The estimation accuracy and MRE were R2 = 0.75 and MRE = 22.5% for backpropagation (BP) neural network, R2 = 0.71 and MRE = 24.4% for random forest regression (RFR) and R2 = 0.71 and MRE = 24.4% for support vector regression (SVR). In contrast, the best three empirical models had estimation accuracies of R2 less than 0.56. The model accuracies applied to Landsat images of Lake Qiandaohu (oligo-mesotrophic state) were better than those of Lake Taihu (eutrophic state) because of the more complex optical conditions in eutrophic lakes. Therefore, machine learning algorithms have great potential for CDOM monitoring in inland waters based on large datasets. Our study demonstrates that machine learning algorithms are available to map CDOM spatial-temporal patterns in inland waters. Full article
(This article belongs to the Special Issue Water Optics and Water Colour Remote Sensing 2.0)
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