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Remote Sensing of Water Quality in Relatively Small and Medium-Sized Inland Waters

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

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 30924

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Special Issue Editors

Prof. Dr. Kaishan Song

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Guest Editor

Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
Interests: optical remote sensing; bio-optical modeling; water quality; climate change; carbon storage

Prof. Dr. Lin Li

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Guest Editor

Department of Earth Sciences, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA
Interests: remote sensing; lunar and planetary geology; environmental sciences; data processing; radiative transfer models
Special Issues, Collections and Topics in MDPI journals

Dr. Ge Liu

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Guest Editor

Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
Interests: wetland remote sensing; water color remote sensing; atmospheric correction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Inland waters play an important role in global biogeochemical cycling and provide key ecosystem services. The intersection of excessive nutrient loading and global warming has led to a globally increased deterioration of inland water quality. With higher spatial and temporal coverages than conventional sampling methods, ground-, airborne-, and satellite-based remote sensing has been used to assess and monitor inland water quality. Numerous studies in this regard have been dedicated to relatively large lake or reservoirs (usually the area >100 km²), whereas relatively small and medium-sized inland waters draw less attention despite they are more sensitive to climate change and human activities. Thus, this Special Issue invites manuscripts addressing the challenges to remote sensing of water quality of small and medium-sized inland waters, and topical areas include, but not limited to, the following:

Atmospheric correction, adjacency effect correction of relatively small and medium-sized inland waters
Harmful algal bloom detection and eutrophication inversion in relatively small and medium-sized inland waters
Black and odorous water detection in relatively small and medium-sized inland waters
Cloud and shadow detection in relatively small and medium-sized inland waters
Correlation between water quality and basin land use of relatively small and medium-sized inland waters
Correlation between water quality and climate change of relatively small and medium-sized inland waters
The application of artificial intelligence (AI) and machine learning in the water color parameters retrieval of relatively small and medium-sized inland waters
Novel or improved bio-optical models in relatively small and medium-sized inland waters
Characteristic of inherent optical properties of relatively small and medium-sized inland waters
The application of unmanned aerial vehicles (UAV) in the relatively small and medium-sized inland waters
Climate effects on the distribution and vegetation of wetland in the relatively small and medium-sized inland waters

Prof. Dr. Kaishan Song
Prof. Dr. Lin Li
Dr. Ge Liu
Guest Editors

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Keywords

Optical remote sensing
UAV remote sensing
Atmospheric correction
Inherent optical properties
Bio-optical models
Artificial intelligence (AI) and machine learning
Black and odorous water detection
Water quality and land use
Water eutrophication
Water quality and climate change
Harmful algal blooms
Optically shallow waters
Cloud and shadow detection
Inland wetland and climate change

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Published Papers (9 papers)

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Research

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17 pages, 5909 KiB

Open AccessArticle

Monitoring Optical Variability in Complex Inland Waters Using Satellite Remote Sensing Data

by Yunxia Du, Kaishan Song and Ge Liu

Remote Sens. 2022, 14(8), 1910; https://doi.org/10.3390/rs14081910 - 15 Apr 2022

Cited by 4 | Viewed by 2162

Abstract

Optical classification for water bodies was carried out based on satellite remote sensing data, which avoided the limitation of having a limited amount of in situ measured spectral data. Unsupervised cluster analysis was performed on 53,815 reflectance spectra extracted at 500-m intervals based on the same season or quasi-same season Landsat 8 SR data using the algorithm of fuzzy c-means. Lakes and reservoirs in the study area were comprehensively identified as three optical types representing different limnological features. The shape and amplitude characteristics of the reflectance spectra for the three optical water types indicated that one corresponds to the clearest water, one corresponds to turbid water, and the other is moderate clear water. The novelty detection technique was further used to label the match-ups of the in situ data set collected during 2006 to 2019 in 12 field surveys based on mathematical rules of the three optical water types. The results confirmed that each optical water type was associated with different bio-optical properties, and the total suspended matter of the clearest, moderate clear and turbid water types were 14.99 mg/L, 41.06 mg/L and 83.81 mg/L, respectively. Overall, the clearest, moderate clear and turbid waters in the study area accounted for 49.3%, 36.7% and 14.0%, respectively. The spatial distribution of optical water types in the study area was seamlessly mapped. Results showed that the bio-optical conditions of the water distributed across the southeast region were roughly homogeneous, but in most of other regions and within some water bodies, they showed a patchy distribution and heterogeneity. This study is useful for monitoring water quality and provides a useful foundation to develop or tuning algorithms to retrieve water quality parameters. Full article

(This article belongs to the Special Issue Remote Sensing of Water Quality in Relatively Small and Medium-Sized Inland Waters)

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16 pages, 5084 KiB

Open AccessArticle

Smartphone–Camera–Based Water Reflectance Measurement and Typical Water Quality Parameter Inversion

by Min Gao, Junsheng Li, Shenglei Wang, Fangfang Zhang, Kai Yan, Ziyao Yin, Ya Xie and Wei Shen

Remote Sens. 2022, 14(6), 1371; https://doi.org/10.3390/rs14061371 - 11 Mar 2022

Cited by 7 | Viewed by 3119

Abstract

Crowdsourced data from smart devices play an increasingly important role in water quality monitoring. However, guaranteeing and evaluating crowdsourced data quality is a key issue. This study aims to extract more accurate water reflectance data from smartphone photographs with variable exposure parameters, and to test the usability of these data in deriving water quality parameters. A set of low–cost reference cards was designed to be placed in the center of the photograph near the water surface, and a calculation model was proposed to convert the photograph digital numbers (DNs) to water reflectance. A nonlinear DN–to–reflectance model was constructed using the inherent reflectance and DN of the reference card in the photograph. Then, the reflectance of the water surface in the same photograph was estimated. During the evaluation of this scheme in seven different waterbodies with 112 sampling sites, small differences were observed between the estimated and measured remote sensing reflectance; the average unbiased relative errors (AUREs) for the red, green, and blue bands were 25.7%, 29.5%, and 35.2%, respectively, while the RMSEs for the three bands were 0.0032, 0.0051, 0.0031, respectively. The derived water reflectance data were used to retrieve the Secchi–disk depth (Z_sd₎ and turbidity, with accuracies of 72.4% and 60.2%, respectively. The results demonstrate that the proposed method based on the smartphone camera can be used to derive the remote sensing reflectance and water quality parameters effectively with acceptable accuracy. Full article

(This article belongs to the Special Issue Remote Sensing of Water Quality in Relatively Small and Medium-Sized Inland Waters)

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23 pages, 65593 KiB

Open AccessArticle

Spectral and Spatial Feature Integrated Ensemble Learning Method for Grading Urban River Network Water Quality

by Xiaoteng Zhou, Chun Liu, Akram Akbar, Yun Xue and Yuan Zhou

Remote Sens. 2021, 13(22), 4591; https://doi.org/10.3390/rs13224591 - 15 Nov 2021

Cited by 16 | Viewed by 2902

Abstract

Urban river networks have the characteristics of medium and micro scales, complex water quality, rapid change, and time–space incoherence. Aiming to monitor the water quality accurately, it is necessary to extract suitable features and establish a universal inversion model for key water quality parameters. In this paper, we describe a spectral- and spatial-feature-integrated ensemble learning method for urban river network water quality grading. We proposed an in situ sampling method for urban river networks. Factor and correlation analyses were applied to extract the spectral features. Moreover, we analyzed the maximum allowed bandwidth for feature bands. We demonstrated that spatial features can improve the accuracy of water quality grading using kernel canonical correlation analysis (KCCA). Based on the spectral and spatial features, an ensemble learning model was established for total phosphorus (TP) and ammonia nitrogen (NH₃-N). Both models were evaluated by means of fivefold validation. Furthermore, we proposed an unmanned aerial vehicle (UAV)-borne water quality multispectral remote sensing application process for urban river networks. Based on the process, we tested the model in practice. The experiment confirmed that our model can improve the grading accuracy by 30% compared to other machine learning models that use only spectral features. Our research can extend the application field of water quality remote sensing to complex urban river networks. Full article

(This article belongs to the Special Issue Remote Sensing of Water Quality in Relatively Small and Medium-Sized Inland Waters)

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18 pages, 5091 KiB

Open AccessArticle

Sentinel-2 and Landsat-8 Observations for Harmful Algae Blooms in a Small Eutrophic Lake

by Miao Liu, Hong Ling, Dan Wu, Xiaomei Su and Zhigang Cao

Remote Sens. 2021, 13(21), 4479; https://doi.org/10.3390/rs13214479 - 8 Nov 2021

Cited by 22 | Viewed by 4971

Abstract

Widespread harmful cyanobacterial bloom is one of the most pressing concerns in lakes and reservoirs, resulting in a lot of negative ecological consequences and threatening public health. Ocean color instruments with low spatial resolution have been used to monitor cyanobacterial bloom in large lakes; however, they cannot be applied to small water bodies well. Here, the Multi-Spectral Instrument (MSI) onboard Sentinel-2A and -2B and the Operational Landsat Imager (OLI) onboard Landsat-8 were employed to assemble the virtual constellation and to track spatial and seasonal variations in floating algae blooms from 2016 to 2020 in a small eutrophic plateau lake: Lake Xingyun in China. The floating algae index (FAI) was calculated using Rayleigh-corrected reflectance in the red, near-infrared, and short-wave infrared bands. The MSI-derived FAI had a similar pattern to the OLI-derived FAI, with a mean absolute percentage error of 19.98% and unbiased percentage difference of 17.05%. Then, an FAI threshold, 0.0693, was determined using bimodal histograms of FAI images for floating algae extraction. The floating algae had a higher occurrence in the northern region than the southern region in this lake, whilst the occurrence of floating algae in summer and autumn was higher than that in spring and winter. Such a spatial and seasonal pattern was related to the variability in air temperature, wind speed and direction, and nutrients. The climatological annual mean occurrence of floating algae from 2016 to 2020 in Lake Xingyun exhibited a significant decrease, which was related to decreases in nutrients, resulting from efficient ecological restoration by the local government. This research highlighted the application of OLI-MSI virtual constellation on monitoring floating algae in a small lake, providing a practical and theoretical reference to monitor aquatic environments in small water bodies. Full article

(This article belongs to the Special Issue Remote Sensing of Water Quality in Relatively Small and Medium-Sized Inland Waters)

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20 pages, 5548 KiB

Open AccessArticle

Nineteen Years of Trophic State Variation in Large Lakes of the Yangtze River Delta Region Derived from MODIS Images

by Yingchun Bian, Ying Zhao, Heng Lyu, Fei Guo, Yunmei Li, Jiafeng Xu, Huaiqing Liu and Shang Ni

Remote Sens. 2021, 13(21), 4322; https://doi.org/10.3390/rs13214322 - 27 Oct 2021

Cited by 7 | Viewed by 2325

Abstract

The Yangtze River Delta (YRD) is one of the regions with the most intensive human activities. The eutrophication of lakes in this area is becoming increasingly serious with consequent negative impacts on the water supply of the surrounding cities. But the spatial-temporal characteristics and driving factors of the trophic state of the lake in this region are still not clearly addressed. In this study, a semi-analytical algorithm for estimating the trophic index (TSI) using particle absorption at 645 nm based on MODIS images is proposed to monitor and evaluate the trophic state of 41 large lakes (larger than 10 km²) in the YRD from 2002 to 2020. The performance of the proposed algorithm is evaluated using an independent dataset. Results showed that the root-mean-square error (RMSE) of the algorithm is less than 6 and the mean absolute percentage error (MAPE) does not exceed 8%, indicating that it can be applied for remotely deriving the TSI in the YRD. The spatial-temporal patterns revealed that there were significantly more lakes with moderate eutrophication in the Lower Yangtze River (LYR) than in the Lower Huaihe River (LHR). The overall average value of the TSI reaches a maximum in summer and a minimum in winter. The TSI value in the YRD over the period 2002–2020 showed a downward trend, especially after 2013. Individually, 33 lakes showed a downward trend and 8 lakes showed an upward trend. Furthermore, marked seasonal and interannual temporal variations can be clearly observed in the LYR and LHR and the sum of the variance contributions of seasonal and interannual components is more than 50%. Multiple linear regression analysis showed that human activities can explain 65% of the variation in the lake TSI in the YRD. Full article

(This article belongs to the Special Issue Remote Sensing of Water Quality in Relatively Small and Medium-Sized Inland Waters)

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20 pages, 58972 KiB

Open AccessArticle

Urban Water Quality Assessment Based on Remote Sensing Reflectance Optical Classification

by Xiaolan Cai, Yunmei Li, Shun Bi, Shaohua Lei, Jie Xu, Huaijing Wang, Xianzhang Dong, Junda Li, Shuai Zeng and Heng Lyu

Remote Sens. 2021, 13(20), 4047; https://doi.org/10.3390/rs13204047 - 10 Oct 2021

Cited by 15 | Viewed by 3478

Abstract

With the acceleration of urbanization, increasing water pollution means that monitoring and evaluating urban water quality are of great importance. Although highly accurate, traditional evaluation methods are time consuming, laborious, and vastly insufficient in terms of the continuity of spatiotemporal coverage. In this study, a water quality assessment method based on remote sensing reflectance optical classification and the traditional grading principle is proposed. In this method, an optical water type (OWT) library was first constructed using the measured in situ remote sensing reflectance dataset based on fuzzy clustering technology. Then, comprehensive scoring rules were established by combining OWTs and 12 water quality parameters, and water quality was graded into different urban water quality levels (UWQLs) based on the scoring results. Using the proposed method, the relative water quality of urban waterbodies was qualitatively evaluated at the macro level based on images from the multispectral imager of Sentinel-2. In addition, there was a significant positive correlation between the UWQLs and the water quality index (WQI). These results indicate the potential of this method for quantitative assessment of urban water quality, providing a new way to evaluate water quality using remote sensing algorithms in the future. Full article

(This article belongs to the Special Issue Remote Sensing of Water Quality in Relatively Small and Medium-Sized Inland Waters)

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11 pages, 3111 KiB

Open AccessCommunication

Monitoring Cyanobacteria Bloom in Dianchi Lake Based on Ground-Based Multispectral Remote-Sensing Imaging: Preliminary Results

by Huan Zhao, Junsheng Li, Xiang Yan, Shengzhong Fang, Yichen Du, Bin Xue, Kai Yu and Chen Wang

Remote Sens. 2021, 13(19), 3970; https://doi.org/10.3390/rs13193970 - 3 Oct 2021

Cited by 11 | Viewed by 2688

Abstract

Some lakes in China have undergone serious eutrophication, with cyanobacterial blooms occurring frequently. Dynamic monitoring of cyanobacterial blooms is important. At present, the traditional lake-survey-based cyanobacterial bloom monitoring is spatiotemporally limited and requires considerable human and material resources. Although satellite remote sensing can rapidly monitor large-scale cyanobacterial blooms, clouds and other factors often mean that effective images cannot be obtained. It is also difficult to use this method to dynamically monitor and manage aquatic environments and provide early warnings of cyanobacterial blooms in lakes and reservoirs. In contrast, ground-based remote sensing can operate under cloud cover and thus act as a new technical method to dynamically monitor cyanobacterial blooms. In this study, ground-based remote-sensing technology was applied to multitemporal, multidirectional, and multiscene monitoring of cyanobacterial blooms in Dianchi Lake via an area array multispectral camera mounted on a rotatable cloud platform at a fixed station. Results indicate that ground-based imaging remote sensing can accurately reflect the spatiotemporal distribution characteristics of cyanobacterial blooms and provide timely and accurate data for salvage treatment and early warnings. Thus, ground-based multispectral remote-sensing data can operationalize the dynamic monitoring of cyanobacterial blooms. The methods and results from this study can provide references for monitoring such blooms in other lakes. Full article

(This article belongs to the Special Issue Remote Sensing of Water Quality in Relatively Small and Medium-Sized Inland Waters)

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Review

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15 pages, 11084 KiB

Open AccessReview

A Review of Quantifying pCO₂ in Inland Waters with a Global Perspective: Challenges and Prospects of Implementing Remote Sensing Technology

by Zhidan Wen, Yingxin Shang, Lili Lyu, Sijia Li, Hui Tao and Kaishan Song

Remote Sens. 2021, 13(23), 4916; https://doi.org/10.3390/rs13234916 - 3 Dec 2021

Cited by 11 | Viewed by 3132

Abstract

The traditional field-based measurements of carbon dioxide (pCO₂) for inland waters are a snapshot of the conditions on a particular site, which might not adequately represent the pCO₂ variation of the entire lake. However, these field measurements can be used in the pCO₂ remote sensing modeling and verification. By focusing on inland waters (including lakes, reservoirs, rivers, and streams), this paper reviews the temporal and spatial variability of pCO₂ based on published data. The results indicate the significant daily and seasonal variations in pCO₂ in lakes. Rivers and streams contain higher pCO₂ than lakes and reservoirs in the same climatic zone, and tropical waters typically exhibit higher pCO₂ than temperate, boreal, and arctic waters. Due to the temporal and spatial variations of pCO₂, it can differ in different inland water types in the same space-time. The estimation of CO₂ fluxes in global inland waters showed large uncertainties with a range of 1.40–3.28 Pg C y⁻¹. This paper also reviews existing remote sensing models/algorithms used for estimating pCO₂ in sea and coastal waters and presents some perspectives and challenges of pCO₂ estimation in inland waters using remote sensing for future studies. To overcome the uncertainties of pCO₂ and CO₂ emissions from inland waters at the global scale, more reliable and universal pCO₂ remote sensing models/algorithms will be needed for mapping the long-term and large-scale pCO₂ variations for inland waters. The development of inverse models based on dissolved biogeochemical processes and the machine learning algorithm based on measurement data might be more applicable over longer periods and across larger spatial scales. In addition, it should be noted that the remote sensing-retrieved pCO₂/the CO₂ concentration values are the instantaneous values at the satellite transit time. A major technical challenge is in the methodology to transform the retrieved pCO₂ values on time scales from instant to days/months, which will need further investigations. Understanding the interrelated control and influence processes closely related to pCO₂ in the inland waters (including the biological activities, physical mixing, a thermodynamic process, and the air–water gas exchange) is the key to achieving remote sensing models/algorithms of pCO₂ in inland waters. This review should be useful for a general understanding of the role of inland waters in the global carbon cycle. Full article

(This article belongs to the Special Issue Remote Sensing of Water Quality in Relatively Small and Medium-Sized Inland Waters)

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20 pages, 3768 KiB

Open AccessReview

Research Trends in the Remote Sensing of Phytoplankton Blooms: Results from Bibliometrics

by Yuanrui Li, Qichao Zhou, Yun Zhang, Jingyi Li and Kun Shi

Remote Sens. 2021, 13(21), 4414; https://doi.org/10.3390/rs13214414 - 2 Nov 2021

Cited by 11 | Viewed by 4079

Abstract

Phytoplankton blooms have caused many serious public safety incidents and eco-environmental problems worldwide and became a focus issue for research. Accurate and rapid monitoring of phytoplankton blooms is critical for forecasting, treating, and management. With the advantages of large spatial coverage and high temporal resolution, remote sensing has been widely used to monitor phytoplankton blooms. Numerous advances have been made in the remote sensing of phytoplankton blooms, biomass, and phenology over the past several decades. To fully understand the development history, research hotspots, and future trends of remote-sensing technology in the study of phytoplankton blooms, we conducted a comprehensive review to systematically analyze the research trends in the remote sensing of phytoplankton blooms through bibliometrics. Our findings showed that research on the use of remote-sensing technology in this field increased substantially in the past 30 years. “Oceanography,” “Environmental Sciences,” and “Remote Sensing” are the most popular subject categories. Remote Sensing of Environment, Journal of Geophysical Research: Oceans, and International Journal of Remote Sensing were the journals with the most published articles. The results of the analysis of international influence and cooperation showed that the United States had the greatest influence in this field and that the cooperation between China and the United States was the closest. The Chinese Academy of Sciences published the largest number of papers, reaching 542 articles. Keyword and topic analysis results showed that “phytoplankton,” “chlorophyll,” and “ocean” were the most frequently occurring keywords, while “eutrophication management and monitoring,” “climate change,” “lakes,” and “remote-sensing algorithms” were the most popular research topics in recent years. Researchers are now paying increasing attention to the phenological response of phytoplankton under the conditions of climate change and the application of new remote-sensing methods. With the development of new remote-sensing technology and the expansion of phytoplankton research, future research should focus on (1) accurate observation of phytoplankton blooms; (2) the traits of phytoplankton blooms; and (3) the drivers, early warning, and management of phytoplankton blooms. In addition, we discuss the future challenges and opportunities in the use of remote sensing in phytoplankton blooms. Our review will promote a deeper and wider understanding of the field. Full article

(This article belongs to the Special Issue Remote Sensing of Water Quality in Relatively Small and Medium-Sized Inland Waters)

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