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Remote Sensing
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Advanced Studies in Monitoring Inland Waters through Remote Sensing Techniques
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Advanced Studies in Monitoring Inland Waters through Remote Sensing Techniques

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 4204

Special Issue Editors

Prof. Dr. Liping Zhu

E-Mail Website
Guest Editor
State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: physical geography; global change and the quaternary environment; limnology and lake sediment
Prof. Dr. Chunqiao Song

E-Mail Website
Guest Editor
Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
Interests: hydrological remote sensing; remote sensing of resources and the environment; surface water resources and global change; impact of climate change on Tibet Plateau
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xingong Li

E-Mail Website
Guest Editor
Department of Geography & Atmospheric Science, 215 Lindley Hall, 1475 Jayhawk Blvd, Lawrence, KS 66045, USA
Interests: surface water mapping and analysis; terrain analysis; generic spatiotemporal analysis methods

Special Issue Information

Dear Colleagues,

Both climate change and human activity impact the quantity and quality of inland waters, which significantly affect regional and global water and carbon cycles. Although some field monitoring has been carried out for typical inland water bodies, very little is known about large-scale changes in water quantity and quality in most inland water bodies; moreover, there is little understanding of the characteristics of these changes and their relationships with climate change and human activity. Through various remote sensing techniques and their combination with filed monitoring data, inversion models can be established to detect changes in inland water quantity and quality at different spatial and temporal scales, and to analyze the causes and mechanisms of these changes; these are key to further understanding changes in inland water bodies with regard to regional and global water and carbon cycles. This Special Issue invites authors to contribute research results on the mapping and monitoring of inland water quantity and quality, remote sensing spectral analysis and inversion models for inland waters, the laws of spatial and temporal variation in water quality, and analyses of water balance change and its causes.

Prof. Dr. Liping Zhu
Prof. Dr. Chunqiao Song
Prof. Dr. Xingong 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

  • water area and water storage
  • water quality
  • water balance
  • lake evaporation
  • climatic change
  • hydrological process
  • multi-remote sensing methodology
  • inversion model

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Published Papers (2 papers)

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Research

19 pages, 5202 KiB  
Article
Estimation of Non-Optically Active Water Quality Parameters in Zhejiang Province Based on Machine Learning
by Lingfang Gao, Yulin Shangguan, Zhong Sun, Qiaohui Shen and Zhou Shi
Remote Sens. 2024, 16(3), 514; https://doi.org/10.3390/rs16030514 - 29 Jan 2024
Cited by 5 | Viewed by 1619
Abstract
Water parameter estimation based on remote sensing is one of the common water quality evaluation methods. However, it is difficult to describe the relationship between the reflectance and the concentration of non-optically active substances due to their weak optical characteristics, and machine learning [...] Read more.
Water parameter estimation based on remote sensing is one of the common water quality evaluation methods. However, it is difficult to describe the relationship between the reflectance and the concentration of non-optically active substances due to their weak optical characteristics, and machine learning has become a viable solution for this problem. Therefore, based on machine learning methods, this study estimated four non-optically active water quality parameters including the permanganate index (CODMn), dissolved oxygen (DO), total nitrogen (TN), and total phosphorus (TP). Specifically, four machine learning models including Support Vector Machine Regression (SVR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and K-Nearest Neighbor (KNN) were constructed for each parameter and their performances were assessed. The results showed that the optimal models of CODMn, DO, TN, and TP were RF (R2 = 0.52), SVR (R2 = 0.36), XGBoost (R2 = 0.45), and RF (R2 = 0.39), respectively. The seasonal 10 m water quality over the Zhejiang Province was measured using these optimal models based on Sentinel-2 images, and the spatiotemporal distribution was analyzed. The results indicated that the annual mean values of CODMn, DO, TN, and TP in 2022 were 2.3 mg/L, 6.6 mg/L, 1.85 mg/L, and 0.063 mg/L, respectively, and the water quality in the western Zhejiang region was better than that in the northeastern Zhejiang region. The seasonal variations in water quality and possible causes were further discussed with some regions as examples. It was found that DO would decrease and CODMn would increase in summer due to the higher temperature and other factors. The results of this study helped understand the water quality in Zhejiang Province and can also be applied to the integrated management of the water environment. The models constructed in this study can also provide references for related research. Full article
(This article belongs to the Special Issue Advanced Studies in Monitoring Inland Waters through Remote Sensing Techniques)
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Graphical abstract

22 pages, 8060 KiB  
Article
A Hybrid Chlorophyll a Estimation Method for Oligotrophic and Mesotrophic Reservoirs Based on Optical Water Classification
by Xiaoyan Dang, Jun Du, Chao Wang, Fangfang Zhang, Lin Wu, Jiping Liu, Zheng Wang, Xu Yang and Jingxu Wang
Remote Sens. 2023, 15(8), 2209; https://doi.org/10.3390/rs15082209 - 21 Apr 2023
Cited by 1 | Viewed by 2073
Abstract
Low- and medium-resolution satellites have been a relatively mature platform for inland eutrophic water classification and chlorophyll a concentration (Chl-a) retrieval algorithms. However, for oligotrophic and mesotrophic waters in small- and medium-sized reservoirs, problems of low satellite resolution, insufficient water sampling, and higher [...] Read more.
Low- and medium-resolution satellites have been a relatively mature platform for inland eutrophic water classification and chlorophyll a concentration (Chl-a) retrieval algorithms. However, for oligotrophic and mesotrophic waters in small- and medium-sized reservoirs, problems of low satellite resolution, insufficient water sampling, and higher uncertainty in retrieval accuracy exist. In this paper, a hybrid Chl-a estimation method based on spectral characteristics (i.e., remote sensing reflectance (Rrs)) classification was developed for oligotrophic and mesotrophic waters using high-resolution satellite Sentinel-2 (A and B) data. First, 99 samples and quasi-synchronous Sentinel-2 satellite data were collected from four small- and medium-sized reservoirs in central China, and the usability of the Sentinel-2 Rrs data in inland oligotrophic and mesotrophic waters was verified by accurate atmospheric correction. Second, a new optical classification method was constructed based on different water characteristics to classify waters into clear water, phytoplankton-dominated water, and water dominated by phytoplankton and suspended matter together using the thresholds of Rrs490/Rrs560 and Rrs665/Rrs560. The proposed method has a higher classification accuracy compared to other classification methods, and the band-ratio algorithm is simpler and more effective for satellite sensors without NIR bands. Third, given the sensitivity of the empirical method to water variability and the ease of development and implementation, a nonlinear least squares fitted one-dimensional nonlinear function was established based on the selection of the best-fitting spectral indices for different optical water types (OWTs) and compared with other Chl-a estimation algorithms. The validation results showed that the hybrid two-band method had the highest accuracy with squared correlation coefficient, root mean squared difference, mean absolute percentage error, and bias of 0.85, 2.93, 32.42%, and −0.75 mg/m3, respectively, and the results of the residual values further validated the applicability and reliability of the model. Finally, the performance of the classification and estimation algorithms on the four reservoirs was evaluated to obtain images mapping the Chl-a in the reservoirs. In conclusion, this study improves the accuracy of Chl-a estimation for oligotrophic and mesotrophic waters by combining a new classification algorithm with a two-band hybrid model, which is an important contribution to solving the problem of low resolution and high uncertainty in the retrieval of Chl-a in oligotrophic and mesotrophic waters in small- and medium-sized reservoirs and has the potential to be applied to other optically similar oligotrophic and mesotrophic lakes and reservoirs using similar spectrally satellite sensors. Full article
(This article belongs to the Special Issue Advanced Studies in Monitoring Inland Waters through Remote Sensing Techniques)
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