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Remote Sensing Retrievals of Optical Properties in Inland Waters and the Coastal Ocean

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

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 19744

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Special Issue Editors

Dr. Chong Fang

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Guest Editor

Remote Sensing and Geographic Information Center, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
Interests: algal blooms; chlorophyll a; phycocyanin; suspended particulate matter; secchi disk depth; total nitrogen; total phosphorus; turbidity

Dr. Andrew Clive Banks

E-Mail Website
Guest Editor

Satellite Oceanography and Marine Optics, Institute of Oceanography, Hellenic Centre for Marine Research, Heraklion 71003, Crete, Greece
Interests: validation and vicarious calibration of satellite data; accuracy of satellite and in situ data (uncertainty and SI traceability); fiducial reference measurements; open ocean and coastal remote sensing of the Eastern Mediterranean; ocean color; sea surface temperature; albedo; BRDF; coastal zone; climate change
Special Issues, Collections and Topics in MDPI journals

Dr. Zhidan Wen

E-Mail Website
Guest Editor

Remote Sensing and Geographic Information Center, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
Interests: inland water carbon cycle; remote sensing of greenhouse gases
Special Issues, Collections and Topics in MDPI journals

Dr. Shaohua Lei

E-Mail Website1 Website2
Guest Editor

National Key Laboratory of Water Disaster Prevention, Nanjing Hydraulic Research Institute, Nanjing 210029, China
Interests: remote sensing of water environment; remote sensing of soil and water conservation; water quality; suspended particulate matter; water color; lake optics; remote sensing for water disaster prevention

Special Issue Information

Dear Colleagues,

Inland waters and the coastal ocean are important components of the biosphere that contribute to atmospheric processes and the regulation of regional climates through primary production, carbon storage and greenhouse gas emissions. Inland waters also have economical functions and have been exploited as sources of water for agriculture, aquaculture, recreation and drinking water. The coastal ocean is the critical area for material exchange between land and ocean, and thus, has an extremely important ecological value. In recent decades, the pollution from industrialization and urbanization has negatively impacted inland waters and the coastal ocean, water transparency has decreased, eutrophication has intensified, and cyanobacteria blooms have frequently occurred. Given the opportunity for large-scale, synchronous observations and the availability of long-term datasets, satellite images have become a mainstream data source to research global and regional aquatic environments.

Water color remote sensing is one of the main topics in this journal. Our Special Issue fits very well with this topic. The optical or non-optical parameters of inland waters and the coastal ocean are estimated by using multispectral or hyperspectral radiance data, with these derived products based on bio-optical models that consist of empirical/semi-empirical, analytical/semi-analytical, quasi-analytical, machine learning and other algorithms. Furthermore, their spatiotemporal variation trends are evaluated, and the driving factors of water quality evolution are explored in combination with anthropogenic activities or climatic change factors in the basin. This represents the main research content of water color remote sensing.

This Special Issue invites manuscripts addressing the challenges in remote sensing retrievals of optical properties of inland waters and the coastal ocean, with a broad outline of its scope including, but not limited to, the following:

Optical water quality parameter retrieval, such as for chlorophyll a, suspended particulate matter, phycocyanin, etc., in inland waters and the coastal ocean.
Non-optical water quality parameter retrieval, such as for total phosphorus, total nitrogen, etc., in inland waters and the coastal ocean.
Algal bloom detection, aquatic vegetation and water temperature retrieval in inland waters and the coastal ocean.
Vertical water quality parameter retrieval, such as chlorophyll a, suspended particulate matter, etc., in inland waters and the coastal ocean using active Lidar or passive remote sensing.
Vertical phytoplankton biomass and primary productivity retrieval in inland waters and the coastal ocean using active Lidar or passive remote sensing.
Application of artificial intelligence (AI) or machine learning in the remote estimation of water quality in inland waters and the coastal ocean.
Application of unmanned aerial vehicles (UAVs) in the remote estimation of water quality in inland waters and the coastal ocean.
Climate change and spatiotemporal variation of water quality in inland waters and the coastal ocean.
Anthropogenic factors and spatiotemporal variation of water quality in inland waters and the coastal ocean.
Novel or improved bio-optical models in the retrieval of optical properties in inland waters and the coastal ocean.

Dr. Chong Fang
Dr. Andrew Clive Banks
Dr. Zhidan Wen
Dr. Shaohua Lei
Guest Editors

Manuscript Submission Information

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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

optical water quality parameter retrieval
non-optical water quality parameter retrieval
algal bloom
aquatic vegetation
water temperature retrieval
machine learning

Published Papers (14 papers)

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Research

23 pages, 2964 KiB

Open AccessArticle

A Chlorophyll-a Concentration Inversion Model Based on Backpropagation Neural Network Optimized by an Improved Metaheuristic Algorithm

by Xichen Wang, Jianyong Cui and Mingming Xu

Remote Sens. 2024, 16(9), 1503; https://doi.org/10.3390/rs16091503 - 24 Apr 2024

Abstract

Chlorophyll-a (Chl-a) concentration monitoring is very important for managing water resources and ensuring the stability of marine ecosystems. Due to their high operating efficiency and high prediction accuracy, backpropagation (BP) neural networks are widely used in Chl-a concentration inversion. However, BP neural networks tend to become stuck in local optima, and their prediction accuracy fluctuates significantly, thus posing restrictions to their accuracy and stability in the inversion process. Studies have found that metaheuristic optimization algorithms can significantly improve these shortcomings by optimizing the initial parameters (weights and biases) of BP neural networks. In this paper, the adaptive nonlinear weight coefficient, the path search strategy “Levy flight” and the dynamic crossover mechanism are introduced to optimize the three main steps of the Artificial Ecosystem Optimization (AEO) algorithm to overcome the algorithm’s limitation in solving complex problems, improve its global search capability, and thereby improve its performance in optimizing BP neural networks. Relying on Google Earth Engine and Google Colaboratory (Colab), a model for the inversion of Chl-a concentration in the coastal waters of Hong Kong was built to verify the performance of the improved AEO algorithm in optimizing BP neural networks, and the improved AEO algorithm proposed herein was compared with 17 different metaheuristic optimization algorithms. The results show that the Chl-a concentration inversion model based on a BP neural network optimized using the improved AEO algorithm is significantly superior to other models in terms of prediction accuracy and stability, and the results obtained via the model through inversion with respect to Chl-a concentration in the coastal waters of Hong Kong during heavy precipitation events and red tides are highly consistent with the measured values of Chl-a concentration in both time and space domains. These conclusions can provide a new method for Chl-a concentration monitoring and water quality management for coastal waters. Full article

(This article belongs to the Special Issue Remote Sensing Retrievals of Optical Properties in Inland Waters and the Coastal Ocean)

22 pages, 21938 KiB

Open AccessArticle

Deep-Learning-Based Automatic Extraction of Aquatic Vegetation from Sentinel-2 Images—A Case Study of Lake Honghu

by Hangyu Gao, Ruren Li, Qian Shen, Yue Yao, Yifan Shao, Yuting Zhou, Wenxin Li, Jinzhi Li, Yuting Zhang and Mingxia Liu

Remote Sens. 2024, 16(5), 867; https://doi.org/10.3390/rs16050867 - 29 Feb 2024

Viewed by 536

Abstract

Aquatic vegetation is an important component of aquatic ecosystems; therefore, the classification and mapping of aquatic vegetation is an important aspect of lake management. Currently, the decision tree (DT) classification method based on spectral indices has been widely used in the extraction of aquatic vegetation data, but the disadvantage of this method is that it is difficult to fix the threshold value, which, in turn, affects the automatic classification effect. In this study, Sentinel-2 MSI data were used to produce a sample set (about 930 samples) of aquatic vegetation in four inland lakes (Lake Taihu, Lake Caohai, Lake Honghu, and Lake Dongtinghu) using the visual interpretation method, including emergent, floating-leaved, and submerged vegetation. Based on this sample set, a DL model (Res-U-Net) was used to train an automatic aquatic vegetation extraction model. The DL model achieved a higher overall accuracy, relevant error, and kappa coefficient (90%, 8.18%, and 0.86, respectively) compared to the DT method (79%, 23.07%, and 0.77) and random forest (78%,10.62% and 0.77) when utilizing visual interpretation results as the ground truth. When utilizing measured point data as the ground truth, the DL model exhibited accuracies of 59%, 78%, and 91% for submerged, floating-leaved, and emergent vegetation, respectively. In addition, the model still maintains good recognition in the presence of clouds with the influence of water bloom. When applying the model to Lake Honghu from January 2017 to October 2023, the obtained temporal variation patterns in the aquatic vegetation were consistent with other studies. The study in this paper shows that the proposed DL model has good application potential for extracting aquatic vegetation data. Full article

(This article belongs to the Special Issue Remote Sensing Retrievals of Optical Properties in Inland Waters and the Coastal Ocean)

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19 pages, 5868 KiB

Open AccessArticle

Remote Sensing Estimation of CDOM and DOC with the Environmental Implications for Lake Khanka

by Sining Qiang, Kaishan Song, Yingxin Shang, Fengfa Lai, Zhidan Wen, Ge Liu, Hui Tao and Yunfeng Lyu

Remote Sens. 2023, 15(24), 5707; https://doi.org/10.3390/rs15245707 - 13 Dec 2023

Viewed by 840

Abstract

Chromophoric dissolved organic matter (CDOM) is a significant contributor to the biogeochemical cycle and energy dynamics within aquatic ecosystems. Hence, the implementation of a systematic and comprehensive monitoring and governance framework for the CDOM in inland waters holds significant importance. This study conducted the retrieval of CDOM in Lake Khanka. Specifically, we use the GBDT (R² = 0.84) algorithm which performed best in retrieving CDOM levels and an empirical relationship based on the situ data between CDOM and dissolved organic carbon (DOC) to indicate the distribution of DOC indirectly. The performance of the CDOM-DOC retrieval scheme was reasonably good, achieving an R² value of 0.69. The empirical algorithms were utilized for the analysis of Sentinel-3 datasets from the period 2016 to 2020 in Lake Khanka. The potential factors that contributed to the sources of DOM were also analyzed with the humification index (HIX). The significant relationship between CDOM and DOC (HIX and chemical oxygen demand (COD)) indicated the potential remote sensing application of water quality monitoring for water management. An analysis of our findings suggests that the water quality of the Great Khanka is superior to that of the Small Khanka. Moreover, the distribution of diverse organic matter exhibits a pattern where concentrations are generally higher along the shoreline compared to the center of the lake. Efficient measures should be promptly implemented to safeguard the water resources in international boundary lakes such as Lake Khanka and comprehensive monitoring systems including DOM distribution, DOM sources, and water quality management would be essential for water resource protection and government management. Full article

(This article belongs to the Special Issue Remote Sensing Retrievals of Optical Properties in Inland Waters and the Coastal Ocean)

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19 pages, 11272 KiB

Open AccessArticle

Satellite Estimation of pCO₂ and Quantification of CO₂ Fluxes in China’s Chagan Lake in the Context of Climate Change

by Ruixue Zhao, Qian Yang, Zhidan Wen, Chong Fang, Sijia Li, Yingxin Shang, Ge Liu, Hui Tao, Lili Lyu and Kaishan Song

Remote Sens. 2023, 15(24), 5680; https://doi.org/10.3390/rs15245680 - 10 Dec 2023

Viewed by 898

Abstract

The massive increase in the amount of greenhouse gases in the atmosphere, especially carbon dioxide (CO₂), has had a significant impact on the global climate. Research has revealed that lakes play an important role in the global carbon cycle and that they can shift between the roles of carbon sources and sinks. This study used Landsat satellite images to analyze the spatiotemporal characteristics and factors influencing the CO₂ changes in Chagan Lake in China. We conducted six water sampling campaigns at Chagan Lake in 2020–2021 and determined the partial pressure of carbon dioxide (pCO₂) from 110 water samples. Landsat surface reflectance was matched with water sampling events within ±7 days of satellite overpasses, yielding 75 matched pairs. A regression analysis indicated strong associations between pCO₂ and both the band difference model of the near-infrared band and green band (Band 5-Band 3, R² = 0.83, RMSE = 27.55 μatm) and the exponential model [((exp(b3) − exp(b5))²/(exp(b3) + exp(b5))², R² = 0.82, RMSE = 27.99 μatm]. A comparison between the performances of a linear regression model and a machine learning model found that the XGBoost model had the highest fitting accuracy (R² = 0.94, RMSE = 16.86 μatm). We used Landsat/OLI images acquired mainly in 2021 to map pCO₂ in Chagan Lake during the ice-free period. The pCO₂ in the surface water of Chagan Lake showed considerable spatiotemporal variability within a range of 0–200 μatm. pCO₂ also showed significant seasonal variations, with the lowest and highest mean values in autumn (30–50 μatm) and summer (120–150 μatm), respectively. Spatially, the pCO₂ values in the southeast of Chagan Lake were higher than those in the northwest. The CO₂ fluxes were calculated based on the pCO₂ and ranged from −3.69 to −2.42 mmol/m²/d, indicating that Chagan Lake was absorbing CO₂ (i.e., it was a weak carbon sink). Temperature, chlorophyll a, total suspended matter, and turbidity were found to have reinforcing effects on the overall trend of pCO₂, while the Secchi disk depth was negatively correlated with pCO₂. The results of this study provide valuable insights for assessing the role of lakes in the carbon cycle in the context of climate change. Full article

(This article belongs to the Special Issue Remote Sensing Retrievals of Optical Properties in Inland Waters and the Coastal Ocean)

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26 pages, 6160 KiB

Open AccessArticle

Research on the Characteristic Spectral Band Determination for Water Quality Parameters Retrieval Based on Satellite Hyperspectral Data

by Xietian Xia, Hui Lu, Zenghui Xu, Xiang Li and Yu Tian

Remote Sens. 2023, 15(23), 5578; https://doi.org/10.3390/rs15235578 - 30 Nov 2023

Viewed by 1038

Abstract

Hyperspectral remote sensing technology has been widely used in water quality monitoring. However, while it provides more detailed spectral information for water quality monitoring, it also gives rise to issues such as data redundancy, complex data processing, and low spatial resolution. In this study, a novel approach was proposed to determine the characteristic spectral band of water quality parameters based on satellite hyperspectral data, aiming to improve data utilization of hyperspectral data and to achieve the same precision monitoring of multispectral data. This paper first introduces the data matching method of satellite hyperspectral data and water quality based on space–time information for guidance in collecting research data. Secondly, the customizable and fixed spectral bands of the existing multispectral camera products were studied and used for the preprocessing of hyperspectral data. Then, the determination approach of characteristic spectral bands of water quality parameters is proposed based on the correlation between the reflectance of different bands and regression modeling. Next, the model performance for retrieval of various water quality parameters was compared between the typical empirical method and artificial neural network (ANN) method of different spectral band sets with different band numbers. Finally, taking the adjusted determination coefficient

\bar{R^{2}}

as an evaluation index for the models, the results show that the ANN method has obvious advantages over the empirical method, and band set providing more band options improves the model performance. There is an optimal band number for the characteristic spectral bands of water quality parameters. For permanganate index (COD_Mn), dissolved oxygen (DO), and conductivity (EC), the

\bar{R^{2}}

of the optimal ANN model with three bands can reach about 0.68, 0.43, and 0.49, respectively, whose mean absolute percentage error (MAPE) values are 14.02%, 16.26%, and 17.52%, respectively. This paper provides technical guidance for efficient utilization of hyperspectral data by determination of characteristic spectral bands, the theoretical basis for customization of multispectral cameras, and the subsequent water quality monitoring through remote sensing using a multispectral drone. Full article

(This article belongs to the Special Issue Remote Sensing Retrievals of Optical Properties in Inland Waters and the Coastal Ocean)

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21 pages, 11473 KiB

Open AccessArticle

Retrievals of Chlorophyll-a from GOCI and GOCI-II Data in Optically Complex Lakes

by Yuyu Guo, Xiaoqi Wei, Zehui Huang, Hanhan Li, Ronghua Ma, Zhigang Cao, Ming Shen and Kun Xue

Remote Sens. 2023, 15(19), 4886; https://doi.org/10.3390/rs15194886 - 09 Oct 2023

Viewed by 872

Abstract

The chlorophyll-a (Chla) concentration is a key parameter to evaluate the eutrophication conditions of water, which is very important for monitoring algal blooms. Although Geostationary Ocean Color Imager (GOCI) has been widely used in Chla inversion, the consistency of the Rayleigh-corrected reflectance (R_rc) of GOCI and GOCI-II sensors still needs to be further evaluated, and a model suitable for lakes with complex optical properties needs to be constructed. The results show that (1) the derived Chla values of the GOCI and GOCI-II synchronous data were relatively consistent and continuous in three lakes in China. (2) The accuracy of the random forest (RF) model (R² = 0.84, root mean square error (RMSE) =11.77 μg/L) was higher than that of the empirical model (R² = 0.79, RMSE = 12.63 μg/L) based on the alternative floating algae index (AFAI). (3) The interannual variation trend fluctuated, with high Chla levels in Lake Chaohu in 2015 and 2019, while those in Lake Hongze were high in 2013, 2015, and 2022, and those in Lake Taihu reached their peak in 2017 and 2019. There were three types of diurnal variation patterns, namely, near-continuous increase (Class 1), near-continuous decrease (Class 2), and first an increase and then a decrease (Class 3), among which Lake Chaohu and Lake Taihu occupied the highest proportion in Class 3. The results analyzed the temporal and spatial variations of Chla in three lakes for 12 years and provided support for the use of GOCI and GOCI-II data and monitoring of Chla in optical complex inland waters. Full article

(This article belongs to the Special Issue Remote Sensing Retrievals of Optical Properties in Inland Waters and the Coastal Ocean)

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12 pages, 5421 KiB

Open AccessArticle

Spatiotemporal Evolutions of the Suspended Particulate Matter in the Yellow River Estuary, Bohai Sea and Characterized by Gaofen Imagery

by Zhifeng Yu, Jun Zhang, Zheyu Chen, Yuekai Hu, C. K. Shum, Chaofei Ma, Qingjun Song, Xiaohong Yuan, Ben Wang and Bin Zhou

Remote Sens. 2023, 15(19), 4769; https://doi.org/10.3390/rs15194769 - 29 Sep 2023

Cited by 1 | Viewed by 859

Abstract

Suspended particulate matter is a crucial component in estuaries and coastal oceans, and a key parameter for evaluating their water quality. The Bohai Sea, a huge marginal sea covering an expanse of 77,000 km² and constantly fed by numerous sediment-laden rivers, has maintained a high level of total suspended particulate matter (TSM). Despite the widespread development and application of TSM retrieval algorithms using commonly available satellite data like Landsat, Sentinel, and MODIS, developing TSM retrieval algorithms for China’s Gaofen (GF) series (GF-6 and GF-1) in the Bohai Sea is still a great challenge, mainly due to the limited applicability of empirical algorithms. In this study, 259 in situ measured-TSM samples were collected for algorithm development. The remote sensing reflectance (R_rs) curve demonstrates prominent peaks between 550 and 580 nm. Through conversion to remote sensing reflectance, it was found that single-band data had a weak correlation with TSM, reaching a maximum correlation of 0.44. However, by combining bands of band ratio calculations, the correlation was enhanced. Particularly, the blue and green band equivalent R_rs ratio had a correlation coefficient of 0.81 with TSM, and the proposed TSM inversion exponential algorithm developed based on this factor obtained an R-squared (R²) value of 0.76 and a mean relative error (MRE) of 32.24%. Analysis results indicated that: (1) there are spatial variations in the TSM within the Bohai Sea, Laizhou Bay, and the Yellow River estuary, with higher levels near the coast and lower levels in open waters. The Yellow River estuary experiences seasonal fluctuations higher TSM during spring and winter, and lower variations during summer and autumn, and (2) the dynamics of TSM are affected by Yellow River runoff, with increased runoff leads to higher TSM levels and expanded turbid zones. This study proposes a new algorithm to quantify TSM evolutions and distributions in the Bohai Sea and adjacent regions using China’s Gaofen imageries. Full article

(This article belongs to the Special Issue Remote Sensing Retrievals of Optical Properties in Inland Waters and the Coastal Ocean)

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18 pages, 3546 KiB

Open AccessArticle

Estimation of Total Phosphorus Concentration in Lakes in the Yangtze-Huaihe Region Based on Sentinel-3/OLCI Images

by Xiaoyang Wang, Youyi Jiang, Mingliang Jiang, Zhigang Cao, Xiao Li, Ronghua Ma, Ligang Xu and Junfeng Xiong

Remote Sens. 2023, 15(18), 4487; https://doi.org/10.3390/rs15184487 - 12 Sep 2023

Cited by 1 | Viewed by 1055

Abstract

Total phosphorus (TP) concentration is a crucial parameter to assess eutrophication in lakes. As one of the most concentrated regions for freshwater lakes, the Yangtze-Huaihe region plays a significant role in monitoring TP concentrations for the sustainable utilisation of China’s water resources. In this study, a TP concentration estimation model suitable for large-sized lake groups was developed using a combination of measured and remote sensing data powered by advanced machine learning algorithms. Compared to traditional empirical models, the model developed in this study demonstrates significant accuracy in fitting (R² = 0.53, RMSE = 0.08 mg/L, MAPE = 34.20%). Moreover, the application of this model to lakes in the Yangtze-Huaihe region from 2017 to 2022 has been conducted. The multi-year average TP concentration was 0.18 mg/L. Spatial distribution analyses showed that total phosphorus concentrations were higher in small lakes. In terms of temporal changes, the interannual decreases in total phosphorus concentrations were 0.02 mg/L, 0.01 mg/L, and 0.01 mg/L for small, medium, and large lakes, respectively. We also found that large lakes typically exhibited a “high in spring and summer, low in autumn and winter” pattern until 2020, but transitioned to a “high in summer and autumn, low in spring and winter” pattern after 2020 due to the removal of closed fish nets, which were having a significant impact on the lake ecosystem. Other lakes in the area consistently showed a pattern of “high in spring and summer, low in autumn and winter” during the six-year period. These findings may provide useful references and suggestions for the environmental protection and management of lakes in China. Full article

(This article belongs to the Special Issue Remote Sensing Retrievals of Optical Properties in Inland Waters and the Coastal Ocean)

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19 pages, 6727 KiB

Open AccessArticle

Combined Retrievals of Turbidity from Sentinel-2A/B and Landsat-8/9 in the Taihu Lake through Machine Learning

by Zhe Yang, Cailan Gong, Zhihua Lu, Enuo Wu, Hongyan Huai, Yong Hu, Lan Li and Lei Dong

Remote Sens. 2023, 15(17), 4333; https://doi.org/10.3390/rs15174333 - 02 Sep 2023

Viewed by 1253

Abstract

Lakes play a crucial role in the earth’s ecosystems and human activities. While turbidity is not a direct biochemical indicator of lake water quality, it is relatively easy to measure and indicates trophic status and lake health. Although ocean color satellites have been widely used to monitor water color parameters, their coarse spatial resolution makes it hard to capture the fine spatial variability of turbidity in lakes. The combination of Sentinel-2 and Landsat provides an opportunity to monitor lake turbidity with high spatial and temporal resolution. This study aims to generate consistent turbidity products in Taihu Lake from 2018 to 2022 using the Multispectral Instrument (MSI) on board Sentinel-2A/B and the Operational Land Imager (OLI) on board Landsat-8/9. We first tested the performance of three atmospheric correction methods to retrieve consistent reflectance from MSI and OLI images. We found that the Rayleigh correction and a subtraction of the SWIR band from Rayleigh-corrected reflectance can generate the most consistent reflectance (the coefficient of determination (R²) > 0.84, the mean absolution percentage error (MAPE) < 7%, the median error (ME) < 0.0035, and slope > 0.92). Machine learning models outperformed an existing semi-analytical retrieval algorithm in retrieving turbidity (MSI: R² = 0.92, MAPE = 18.78%, and OLI: R² = 0.93, MAPE = 16.20%). The consistency of turbidity from the same-day MSI and OLI images was also satisfactory (N = 3110 and MAPE = 26.48%). The distribution of turbidity exhibited obvious spatial and seasonal variability in Taihu Lake from 2018 to 2022. The results show the potential of MSI and OLI when combined to monitor inland lake water quality. Full article

(This article belongs to the Special Issue Remote Sensing Retrievals of Optical Properties in Inland Waters and the Coastal Ocean)

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20 pages, 11485 KiB

Open AccessArticle

Retrieval of Chla Concentrations in Lake Xingkai Using OLCI Images

by Li Fu, Yaming Zhou, Ge Liu, Kaishan Song, Hui Tao, Fangrui Zhao, Sijia Li, Shuqiong Shi and Yingxin Shang

Remote Sens. 2023, 15(15), 3809; https://doi.org/10.3390/rs15153809 - 31 Jul 2023

Cited by 2 | Viewed by 1072

Abstract

Lake Xingkai is a large turbid lake composed of two parts, Small Lake Xingkai and Big Lake Xingkai, on the border between Russia and China, where it represents a vital source of water, fishing, water transport, recreation, and tourism. Chlorophyll-a (Chla) is a prominent phytoplankton pigment and a proxy for phytoplankton biomass, reflecting the trophic status of waters. Regularly monitoring Chla concentrations is vital for issuing timely warnings of this lake’s eutrophication. Owing to its higher spatial and temporal coverages, remote sensing can provide a synoptic complement to traditional measurement methods by targeting the optical Chla absorption signals, especially for the lakes that lack regular in situ sampling cruises, like Lake Xingkai. This study calibrated and validated several commonly used remote sensing Chla retrieval algorithms (including the two-band ratio, three-band method, four-band method, and baseline methods) by applying them to Sentinel-3 Ocean and Land Colour Instrument (OLCI) images in Lake Xingkai. Among these algorithms, the four-band model (FBA), which removes the absorption signal of detritus and colored dissolved organic matter, was the best-performing model with an R² of 0.64 and a mean absolute percentage difference of 38.26%. With the FBA model applied to OLCI images, the monthly and spatial distributions of Chla in Lake Xingkai were studied from 2016 to 2022. The results showed that over the seven years, the Chla concentrations in Small Lake Xingkai were higher than in Big Lake Xingkai. Unlike other eutrophic lakes in China (e.g., Lake Taihu and Lake Chaohu), Lake Xingkai did not display a stable seasonal Chla variation pattern. We also found uncertainties and limitations of the Chla algorithm models when using a larger satellite zenith angle or applying it to an algal bloom area. Recent increases in anthropogenic nutrient loading, water clarity, and warming temperatures may lead to rising phytoplankton biomass in Lake Xingkai, and the results of this study can be applied for the satellite-based monitoring of its water quality. Full article

(This article belongs to the Special Issue Remote Sensing Retrievals of Optical Properties in Inland Waters and the Coastal Ocean)

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21 pages, 7886 KiB

Open AccessArticle

Mapping Irish Water Bodies: Comparison of Platforms, Indices and Water Body Type

by Minyan Zhao and Fiachra O’Loughlin

Remote Sens. 2023, 15(14), 3677; https://doi.org/10.3390/rs15143677 - 23 Jul 2023

Viewed by 1517

Abstract

Accurate monitoring of water bodies is essential for the management and regulation of water resources. Traditional methods for measuring water quality are always time-consuming and expensive; furthermore, it can be very difficult capture the full spatiotemporal variations across regions. Many studies have shown the possibility of remote-sensing-based water monitoring work in many areas, especially for water quality monitoring. However, the use of optical remotely sensed imagery depends on several factors, including weather, quality of images and the size of water bodies. Hence, in this study, the feasibility of optical remote sensing for water quality monitoring in the Republic of Ireland was investigated. To assess the value of remote sensing for water quality monitoring, it is critical to know how well water bodies and the existing in situ monitoring stations are mapped. In this study, two satellite platforms (Sentinel-2 MSI and Landsat-8 OLI) and four indices for separating water and land pixel (Normalized Difference Vegetation Index—NDVI; Normalized Difference Water Index—NDWI; Modified Normalized Difference Water Index—MNDWI; and Automated Water Extraction Index—AWEI) have been used to create water masks for two scenarios. In the first scenario (Scenario 1), we included all pixels classified as water, while for the second scenario (Scenario 2) accounts for potential land contamination and only used water pixels that were completed surround by other water pixels. The water masks for the different scenarios and combinations of platforms and indices were then compared with the existing water quality monitoring station and to the shapefile of the river network, lakes and coastal and transitional water bodies. We found that both platforms had potential for water quality monitoring in the Republic of Ireland, with Sentinel-2 outperforming Landsat due to its finer spatial resolution. Overall, Sentinel-2 was able to map ~25% of the existing monitoring station, while Landsat-8 could only map ~21%. These percentages were heavily impacted by the large number of river monitoring stations that were difficult to map with either satellite due to their location on smaller rivers. Our results showed the importance of testing several indices. No index performed the best across the different platforms. AWEI_nsh (Automated Water Extraction Index—no shadow) and Sentinel-2 outperformed all other combinations and was able to map over 80% of the area of all non-river water bodies across the Republic of Ireland. While MNDWI was the best index for Landsat-8, it was the worst performer for Sentinel-2. This study showed that optical remote sensing has potential for water monitoring in the Republic of Ireland, especially for larger rivers, lakes and transitional and coastal water bodies. Full article

(This article belongs to the Special Issue Remote Sensing Retrievals of Optical Properties in Inland Waters and the Coastal Ocean)

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19 pages, 10475 KiB

Open AccessArticle

A Novel Atmospheric Correction for Turbid Water Remote Sensing

by Dian Wang, Xiangyu Xiang, Ronghua Ma, Yongqin Guo, Wangyuan Zhu and Zhihao Wu

Remote Sens. 2023, 15(8), 2091; https://doi.org/10.3390/rs15082091 - 15 Apr 2023

Viewed by 1565

Abstract

For the remote sensing of turbid waters, the atmospheric correction (AC) is a key issue. The “black pixel” assumption helps to solve the AC for turbid waters. It has proved to be inaccurate to regard all water pixels in the SWIR (Short Wave Infrared) band as black pixels. It is necessary to perform atmospheric correction in the visible bands after removing the radiation contributions of water in the SWIR band. Here, the modified ACZI (m-ACZI) algorithm was developed. The m-ACZI assumes the spatial homogeneity of aerosol types and employs the BPI (Black Pixel Index) and PIFs (Pseudo-Invariant Features) to identify the “black pixel”. Then, the radiation contributions of waters in the SWIR band are removed to complete the atmospheric correction for turbid waters. The results showed that the m-ACZI had better performance than the SeaDAS (SeaWiFS Data Analysis System) -SWIR and the EXP (exponential extrapolation) algorithm in the visible band (sMAPE < 30.71%, RMSE < 0.0111 sr⁻¹) and is similar to the DSF (Dark Spectrum Fitting) algorithm in floating algae waters. The m-ACZI algorithm is suitable for turbid inland waters. Full article

(This article belongs to the Special Issue Remote Sensing Retrievals of Optical Properties in Inland Waters and the Coastal Ocean)

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21 pages, 4476 KiB

Open AccessArticle

Estimating Effects of Natural and Anthropogenic Activities on Trophic Level of Inland Water: Analysis of Poyang Lake Basin, China, with Landsat-8 Observations

by Jianzhong Li, Zhubin Zheng, Ge Liu, Na Chen, Shaohua Lei, Chao Du, Jie Xu, Yuan Li, Runfei Zhang and Chao Huang

Remote Sens. 2023, 15(6), 1618; https://doi.org/10.3390/rs15061618 - 16 Mar 2023

Cited by 4 | Viewed by 2357

Abstract

The intensification of anthropogenic activities has led to the infiltration of enormous quantities of pollutants into rivers and lakes, resulting in significant deterioration in water quality and a more prominent occurrence of eutrophication. Poyang Lake, the largest freshwater lake in China, is facing a severe challenge related to eutrophication, which seriously threatens the delivery of the ecosystem service and the safety of drinking water. To address this challenge, Landsat-8 Operational Land Imager (OLI) data for the Poyang Lake Basin (PLB) from May 2013 to December 2020 were used. Since inland water bodies with complex optical characteristics, we developed a semi-analytical algorithm to assess the trophic state of the water based on two cruise field measurements in 2016 and 2019. Combining the semi-analytical trophic level index (TLI) with an atmospheric correction model is the most suitable model for OLI images of the PLB, this model was then applied to Landsat-8 time series observations. The trends of the trophic state of water bodies in PLB were revealed, and the annual, quarterly and monthly percentages of eutrophic water bodies were calculated. Natural and anthropogenic factors were then used to explain the changes in the trophic state of the PLB waters. The main findings are as follows: (1) From the 8-year observation results, it can be seen that the variation of trophic level of water in PLB showed obviously spatial and temporal variations, characterized by higher in the north than in the south and higher in winter than in summer. (2) Temperature promoted the growth of harmful algae and plays an essential role in affecting changes in the trophic level of the water. (3) Changes in the trophic level of water bodies in PLB were mainly affected by human activities. The results of spatial and temporal variation of the trophic level of water and the driving factors in PLB can extend our knowledge of water quality degradation and provide essential references for relevant policy-making institutions. Full article

(This article belongs to the Special Issue Remote Sensing Retrievals of Optical Properties in Inland Waters and the Coastal Ocean)

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18 pages, 9727 KiB

Open AccessArticle

Airborne Drones for Water Quality Mapping in Inland, Transitional and Coastal Waters—MapEO Water Data Processing and Validation

by Liesbeth De Keukelaere, Robrecht Moelans, Els Knaeps, Sindy Sterckx, Ils Reusen, Dominique De Munck, Stefan G.H. Simis, Adriana Maria Constantinescu, Albert Scrieciu, Georgios Katsouras, Wim Mertens, Peter D. Hunter, Evangelos Spyrakos and Andrew Tyler

Remote Sens. 2023, 15(5), 1345; https://doi.org/10.3390/rs15051345 - 28 Feb 2023

Cited by 5 | Viewed by 3637

Abstract

Using airborne drones to monitor water quality in inland, transitional or coastal surface waters is an emerging research field. Airborne drones can fly under clouds at preferred times, capturing data at cm resolution, filling a significant gap between existing in situ, airborne and satellite remote sensing capabilities. Suitable drones and lightweight cameras are readily available on the market, whereas deriving water quality products from the captured image is not straightforward; vignetting effects, georeferencing, the dynamic nature and high light absorption efficiency of water, sun glint and sky glint effects require careful data processing. This paper presents the data processing workflow behind MapEO water, an end-to-end cloud-based solution that deals with the complexities of observing water surfaces and retrieves water-leaving reflectance and water quality products like turbidity and chlorophyll-a (Chl-a) concentration. MapEO water supports common camera types and performs a geometric and radiometric correction and subsequent conversion to reflectance and water quality products. This study shows validation results of water-leaving reflectance, turbidity and Chl-a maps derived using DJI Phantom 4 pro and MicaSense cameras for several lakes across Europe. Coefficients of determination values of 0.71 and 0.93 are obtained for turbidity and Chl-a, respectively. We conclude that airborne drone data has major potential to be embedded in operational monitoring programmes and can form useful links between satellite and in situ observations. Full article

(This article belongs to the Special Issue Remote Sensing Retrievals of Optical Properties in Inland Waters and the Coastal Ocean)

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