Estimation of Chlorophyll-a, SPM and Salinity in Mangrove Dominated Tropical Estuarine Areas of Hooghly River, North East Coast of Bay of Bengal, India Using Sentinel-3 Data

Abstract This study aims to explore the variations in spatial/Spatio-temporal characteristics of water quality parameters of three estuaries in the western part of the Indian Sundarbans. Reliable retrieval of near surface concentrations of parameters such as Chlorophyll-a, SST & TSM in various aquatic ecosystems with broad ranges of trophic needs has long been a complex issue. In this study the C2RCC processor has been applied that has been tested for its accuracy across different bio optical regimes in inland & coastal waters. Satellite images for the same period were also collected and analysed using the C2RCC processing sequence to retrieve values of parameters such as the depth of water, surface reflectance, water temperature, inherent optical properties (IOPs), salinity, chlorophyll-a and total suspended matter (TSM) using the SNAP software. During the 2017-2020 season, in situ sampling from specific locations and laboratory water quality analysis were carried out. The OLCI retrieved results were then trained and validated using the in situ datasets. It was observed that the highest amount of TSM was recorded in Diamond Harbour during the pre-monsoon, in the year 2018 (301.40 mgL-1 in-situ value, and 308.54 mg L-1 estimated value). Similarly, chlorophyll-a had higher concentrations during the monsoon season (3.03 mg m-3, in-situ, and 2.96 mg m-3, estimated) in Fraserganj and Sagar south points. Very good fitted correlation results for all seasons between Chl-a, r = 0.829 and TSM, r = 0.924 were found during the comparisons of OLCI and in situ results. The high level of correlation highlights the importance of both primary and secondary data in understanding any dynamic system properly. Finally, the result shows that the water quality model outperforms conventional techniques and OLCI chl-a and TSM products. This paper empirically investigates a reliable remote sensing method for estimating coastal TSM and chl-a concentrations and supports the use of OLCI data in ocean colour remote sensing.

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Temporal Variation of Chlorophyll-a Concentrations in Highly Dynamic Waters from Unattended Sensors and Remote Sensing Observations

Sensors ◽

10.3390/s18082699 ◽

2018 ◽

Vol 18 (8) ◽

pp. 2699 ◽

Cited By ~ 2

Author(s):

Jian Li ◽

Liqiao Tian ◽

Qingjun Song ◽

Zhaohua Sun ◽

Hongjing Yu ◽

...

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Chlorophyll Fluorescence ◽

Chlorophyll A ◽

Temporal Variations ◽

Lake Area ◽

Short Term ◽

Chl A ◽

Spatio Temporal

Monitoring of water quality changes in highly dynamic inland lakes is frequently impeded by insufficient spatial and temporal coverage, for both field surveys and remote sensing methods. To track short-term variations of chlorophyll fluorescence and chlorophyll-a concentrations in Poyang Lake, the largest freshwater lake in China, high-frequency, in-situ, measurements were collected from two fixed stations. The K-mean clustering method was also applied to identify clusters with similar spatio-temporal variations, using remote sensing Chl-a data products from the MERIS satellite, taken from 2003 to 2012. Four lake area classes were obtained with distinct spatio-temporal patterns, two of which were selected for in situ measurement. Distinct daily periodic variations were observed, with peaks at approximately 3:00 PM and troughs at night or early morning. Short-term variations of chlorophyll fluorescence and Chl-a levels were revealed, with a maximum intra-diurnal ratio of 5.1 and inter-diurnal ratio of 7.4, respectively. Using geostatistical analysis, the temporal range of chlorophyll fluorescence and corresponding Chl-a variations was determined to be 9.6 h, which indicates that there is a temporal discrepancy between Chl-a variations and the sampling frequency of current satellite missions. An analysis of the optimal sampling strategies demonstrated that the influence of the sampling time on the mean Chl-a concentrations observed was higher than 25%, and the uncertainty of any single Terra/MODIS or Aqua/MODIS observation was approximately 15%. Therefore, sampling twice a day is essential to resolve Chl-a variations with a bias level of 10% or less. The results highlight short-term variations of critical water quality parameters in freshwater, and they help identify specific design requirements for geostationary earth observation missions, so that they can better address the challenges of monitoring complex coastal and inland environments around the world.

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Temporal and Spatial Variations of Chlorophyll a Concentration and Eutrophication Assessment (1987–2018) of Donghu Lake in Wuhan Using Landsat Images

Water ◽

10.3390/w12082192 ◽

2020 ◽

Vol 12 (8) ◽

pp. 2192

Author(s):

Xujie Yang ◽

Yan Jiang ◽

Xuwei Deng ◽

Ying Zheng ◽

Zhiying Yue

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Chlorophyll A ◽

Dynamic Monitoring ◽

Water Quality Parameter ◽

Landsat Images ◽

Chl A ◽

Donghu Lake ◽

Cubic Model

Chlorophyll a (Chl-a) concentration, which reflects the biomass and primary productivity of phytoplankton in water, is an important water quality parameter to assess the eutrophication status of water. The band combinations shown in the images of Donghu Lake (Wuhan City, China) captured by Landsat satellites from 1987 to 2018 were analyzed. The (B4 − B3)/(B4 + B3) [(Green − Red)/(Green + Red)] band combination was employed to construct linear, power, exponential, logarithmic and cubic polynomial models based on Chl-a values in Donghu Lake in April 2016. The correlation coefficient (R2), the relative error (RE) and the root mean square error (RMSE) of the cubic model were 0.859, 9.175% and 11.194 μg/L, respectively and those of the validation model were 0.831, 6.509% and 19.846μg/L, respectively. Remote sensing images from 1987 to 2018 were applied to the model and the spatial distribution of Chl-a concentrations in spring and autumn of these years was obtained. At the same time, the eutrophication status of Donghu Lake was monitored and evaluated based on the comprehensive trophic level index (TLI). The results showed that the TLI (∑) of Donghu Lake in April 2016 was 63.49 and the historical data on Chl-a concentration showed that Donghu Lake had been eutrophic. The distribution of Chl-a concentration in Donghu Lake was affected by factors such as construction of bridges and dams, commercial activities and enclosure culture in the lake. The overall distribution of Chl-a concentration in each sub-lake was higher than that in the main lake region and Chl-a concentration was highest in summer, followed by spring, autumn and winter. Based on the data of three long-term (2005–2018) monitoring points in Donghu Lake, the matching patterns between meteorological data and Chl-a concentration were analyzed. It revealed that the Chl-a concentration was relatively high in warmer years or rainy years. The long-term measured data also verified the accuracy of the cubic model for Chl-a concentration. The R2, RE and RMSE of the validation model were 0.641, 2.518% and 22.606 μg/L, respectively, which indicated that it was feasible to use Landsat images to retrieve long-term Chl-a concentrations. Based on longitudinal remote sensing data from 1987 to 2018, long-term and large-scale dynamic monitoring of Chl-a concentrations in Donghu Lake was carried out in this study, providing reference and guidance for lake water quality management in the future.

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A Deep Multitask Semisupervised Learning Approach for Chlorophyll-a Retrieval from Remote Sensing Images

Remote Sensing ◽

10.3390/rs14010018 ◽

2021 ◽

Vol 14 (1) ◽

pp. 18

Author(s):

Melike Ilteralp ◽

Sema Ariman ◽

Erchan Aptoula

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Image Analysis ◽

Chlorophyll A ◽

Water Quality Assessment ◽

Remote Sensing Image ◽

Semisupervised Learning ◽

Main Task ◽

Chl A ◽

Multitemporal Remote Sensing

This article addresses the scarcity of labeled data in multitemporal remote sensing image analysis, and especially in the context of Chlorophyll-a (Chl-a) estimation for inland water quality assessment. We propose a multitask CNN architecture that can exploit unlabeled satellite imagery and that can be generalized to other multitemporal remote sensing image analysis contexts where the target parameter exhibits seasonal fluctuations. Specifically, Chl-a estimation is set as the main task, and an unlabeled sample’s month classification is set as an auxiliary network task. The proposed approach is validated with multitemporal/spectral Sentinel-2 images of Lake Balik in Turkey using in situ measurements acquired during 2017–2019. We show that harnessing unlabeled data through multitask learning improves water quality estimation performance.

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Simulation and Assessment of the Capabilities of Orbita Hyperspectral (OHS) Imagery for Remotely Monitoring Chlorophyll-a in Eutrophic Plateau Lakes

Remote Sensing ◽

10.3390/rs13142821 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2821

Author(s):

Runfei Zhang ◽

Zhubin Zheng ◽

Ge Liu ◽

Chenggong Du ◽

Chao Du ◽

...

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Chlorophyll A ◽

Spatial Resolution ◽

Atmospheric Correction ◽

Dianchi Lake ◽

Inland Waters ◽

Eutrophic Lakes ◽

Correction Model ◽

Chl A

The chlorophyll-a (Chl-a) concentration of eutrophic lakes fluctuates significantly due to the disturbance of wind and anthropogenic activities on the water body. Consequently, estimation of the Chl-a concentration has become an immense challenge. Due to urgent demand and rapid development in high-resolution earth observation systems, it has become crucial to assess hyperspectral satellite imagery capabilities on inland water monitoring. The Orbita hyperspectral (OHS) satellite is the latest hyperspectral sensor with both high spectral and spatial resolution (2.5 nm and 10 m, respectively), which could provide great potential for remotely estimating the concentration of Chl-a for inland waters. However, there are still some deficiencies that are mainly manifested in the Chl-a concentration remote sensing retrieval model assessment and accuracy validation, as well as signal-to-noise ratio (SNR) estimation of OHS imagery for inland waters. Therefore, the radiometric performance of OHS imagery for water quality monitoring is evaluated in this study by comparing different atmospheric correction models and the SNR with several remote sensing images. Several crucial findings can be drawn: (1) the three-band model ((1/B15-1/B17)B19) developed by OHS imagery is most suitable for estimating the Chl-a concentration in Dianchi Lake, with the root-mean-square error (RMSE) and the mean absolute percentage error (MAPE) of 15.55 µg/L and 16.31%, respectively; (2) the applicability of the FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) atmospheric correction model for OHS imagery in a eutrophic plateau lake (Dianchi Lake) was better than the 6S (Second Simulation of Satellite Signal in the Solar Spectrum) model, and QUAC (Quick Atmospheric Correction) model, as well as the dark pixel method; (3) the SNR of the OHS imagery was similar to that of Hyperion imagery and was significantly higher than SNR of the HSI imagery; (4) the spatial resolution showed slight influence on the SNR of the OHS imagery. The results show that OHS imagery could be applied to remote sensing retrieval of Chl-a in eutrophic plateau lakes and presents a new tool for dynamic hyperspectral monitoring of water quality.

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ESTIMATION OF CHLOROPHYLL-A CONCENTRATION IN SAMPALOC LAKE USING UAS MULTISPECTRAL REMOTE SENSING AND REGRESSION ANALYSIS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w19-297-2019 ◽

2019 ◽

Vol XLII-4/W19 ◽

pp. 297-303

Author(s):

R. M. G. Maravilla ◽

J. P. Quinalayo ◽

A. C. Blanco ◽

C. G. Candido ◽

E. V. Gubatanga ◽

...

Keyword(s):

Water Quality ◽

Regression Analysis ◽

Chlorophyll A ◽

Linear Regression Analysis ◽

Quality Data ◽

Water Quality Data ◽

Microalgal Biomass ◽

Chlorophyll A Concentration ◽

Chl A

Abstract. Sampaloc Lake is providing livelihood for the residents through aquaculture. An increase in the quantity of fish pens inside the lake threatens its water quality condition. One parameter being monitored is microalgal biomass by measuring Chlorophyll-a concentration. This study aims to generate a chlorophyll-a concentration model for easier monitoring of the lake. In-situ water quality data were collected using chl-a data logger and water quality meter at 357 and 12 locations, respectively. Using Parrot Sequoia+ Multispectral Camera, 1496 of 2148 images were acquired and calibrated, producing 18x18cm resolution Green (G), Red(R), Red Edge (RE) and Near Infrared (NIR) reflectance images. NIR was used to mask out non-water features, and to correct sun glint. The in-situ data and the pixel values extracted were used for Simple Linear Regression Analysis. A model with 5 variables – R/NIR, RE2, NIR2, R/NIR2, and NIR/RE2, was generated, yielding an R2 of 0.586 and RMSE of 0.958 μg/l. A chlorophyll-a concentration map was produced, showing that chl-a is higher where fish pens are located and lowers as it moves away from the pens. Although there are apparent fish pens on certain areas of the lake, it still yields low chlorophyll-a because of little amount of residential area or establishments adjacent to it. Also, not all fish pens have the same concentration of Chlorophyll-a due to inconsistent population per fish pen. The center of the lake has low chlorophyll-a as it is far from human activities. The only outlet, Sabang Creek, also indicates high concentration of Chlorophyll-a.

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The Use of Landsat 8 and Sentinel-2 Time Series Data for Monitoring Drought, Chlorophyll-a and Turbidity in Lake Chad

10.5194/egusphere-egu2020-1886 ◽

2020 ◽

Author(s):

Sang Il Lee ◽

Willibroad Gabila Buma

Keyword(s):

Remote Sensing ◽

Chlorophyll A ◽

Time Series Data ◽

Series Data ◽

Landsat 8 ◽

Monitoring Networks ◽

Lake Chad ◽

Chl A ◽

Drought Conditions

A decline in Lake Chad&#8217;s water level has been observed for over two decades. With millions of people relying on the lake, and considering its dynamic behavior, methods for the continuous and spatially distributed retrieval of water quantity and quality parameters are vital for proper monitoring and management initiatives. Here, we propose an integrated approach for drought, chlorophyll-a (Chl-a) and turbidity monitoring in Lake Chad using satellite datasets.First, we used remote sensing information to constrain drought patterns over the immediate lake environment. Vegetation conditions within and around the lake was used to assess drought conditions in this area. Using Landsat multispectral images obtained between 1999 and 2018, Vegetation Temperature Condition Index (VTCI) was derived and used as an indicator for drought monitoring. Vegetation proportion from WorldView-03 images was used to evaluate the accuracy of methods used to derive VTCI. Obtained results showed that most areas experienced mild drought conditions.Secondly, we assessed the performance of band algorithms in estimating Chl-a concentrations and turbidity levels from Landsat-8 and Sentinel-2A and 2B images. A two-band semi-analytical Chl-a and turbidity retrieval model was used for estimating the Chl-a concentrations and turbidity levels between 2015 and 2019. Due to the absence of in-situ data, estimates from the extraction models were statistically compared with datasets obtained from WorldView-03. Further inter-comparison of Chl-a and turbidity retrieved from the two sensors was carried out.This study shows how satellite observations can be used to complement sparse and declining in situ drought, Chl-a and turbidity monitoring networks in this area. Solidifying the importance of remote sensing in areas that are difficult to access or with poor availability of conventional data sources.

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Mapping Chlorophyll-a Concentrations in the Kaštela Bay and Brač Channel Using Ridge Regression and Sentinel-2 Satellite Images

Electronics ◽

10.3390/electronics10233004 ◽

2021 ◽

Vol 10 (23) ◽

pp. 3004

Author(s):

Antonia Ivanda ◽

Ljiljana Šerić ◽

Marin Bugarić ◽

Maja Braović

Keyword(s):

Remote Sensing ◽

Chlorophyll A ◽

Satellite Data ◽

Coastal Waters ◽

Optical Parameter ◽

Data Set ◽

Chl A ◽

Sentinel 2

In this paper, we describe a method for the prediction of concentration of chlorophyll-a (Chl-a) from satellite data in the coastal waters of Kaštela Bay and the Brač Channel (our case study areas) in the Republic of Croatia. Chl-a is one of the parameters that indicates water quality and that can be measured by in situ measurements or approximated as an optical parameter with remote sensing. Remote sensing products for monitoring Chl-a are mostly based on the ocean and open sea monitoring and are not accurate for coastal waters. In this paper, we propose a method for remote sensing monitoring that is locally tailored to suit the focused area. This method is based on a data set constructed by merging Sentinel 2 Level-2A satellite data with in situ Chl-a measurements. We augmented the data set horizontally by transforming the original feature set, and vertically by adding synthesized zero measurements for locations without Chl-a. By transforming features, we were able to achieve a sophisticated model that predicts Chl-a from combinations of features representing transformed bands. Multiple Linear Regression equation was derived to calculate Chl-a concentration and evaluated quantitatively and qualitatively. Quantitative evaluation resulted in R2 scores 0.685 and 0.659 for train and test part of data set, respectively. A map of Chl-a of the case study area was generated with our model for the dates of the known incidents of algae blooms. The results that we obtained are discussed in this paper.

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Hyper-spectral Remote Sensing of Water Quality Parameters in Lakes: A Case Study of Hyderabad City, Telangana State, India

IRA-International Journal of Technology & Engineering (ISSN 2455-4480) ◽

10.21013/jte.v8.n2.p1 ◽

2017 ◽

Vol 8 (2) ◽

pp. 5

Author(s):

V. Hema Sailaja ◽

P. Suman Babu ◽

M. Anji Reddy

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Chlorophyll A ◽

Secchi Depth ◽

Quality Parameters ◽

Water Quality Modeling ◽

Hyperspectral Data ◽

Water Quality Parameters ◽

Model Versus

This paper is a research work intended to present a comprehensive water quality modeling for predicting three water quality parameters (Chlorophyll (a), Turbidity and Secchi Depth) in typical Inland lake environments (Hussain sagar and Umda sagar) using Hyperspectral Remote sensing technique. They are estimated through regression models by combining the field Spectro-radiometer reflectance values with concurrent in situ ground data (Analytical) collected in the study area and correlated and validated with the available Hyperspectral data (Hyperion). A total of 180 in situ water sample and 900 spectral signatures were analysed during campaigns from 2010 to 2014 study period. The mean values of Chlorophyll-a varied between 6.983mgL-1 and 24.858mgL-1, Turbidity varied between 16.583mgL-1 and 48.867mgL-1 and Secchi depth varied between 0.104mgL-1 and 0.375mgL-1 over the study period considering the two lakes during pre and post monsoon seasons. The band ratios of the reflected spectra at R670/R710, R710/R740 and R710/R550 are used for the development of the mathematical model of chlorophyll-a, Turbidity and Secchi depth respectively. The trained sets of the pixels extracted from the hyperspectral data for pure spectra are processed for preparing the water quality distribution maps. When subjected to multi-variant statistical tests of significance, the models have yielded satisfactory R2 values. The model versus in situ analysis results demonstrated R2= 0.81% for Chlorophyll-a, R2= 0.81% for Turbidity and R2= 0.78% for Secchi depth correlation and that of model versus satellite data exhibited R2= 0.60% for Chlorophyll-a, R2= 0.66% for Turbidity and R2= 0.65 % for Secchi depth mean efficiency.

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WaterNet: A Convolutional Neural Network for Chlorophyll-a Concentration Retrieval

Remote Sensing ◽

10.3390/rs12121966 ◽

2020 ◽

Vol 12 (12) ◽

pp. 1966 ◽

Cited By ~ 2

Author(s):

Muhammad Aldila Syariz ◽

Chao-Hung Lin ◽

Manh Van Nguyen ◽

Lalu Muhamad Jaelani ◽

Ariel C. Blanco

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Neural Networks ◽

Convolutional Neural Network ◽

Chlorophyll A ◽

Remote Sensing Images ◽

Two Stage ◽

Chl A ◽

The Neural Network

The retrieval of chlorophyll-a (Chl-a) concentrations relies on empirical or analytical analyses, which generally experience difficulties from the diversity of inland waters in statistical analyses and the complexity of radiative transfer equations in analytical analyses, respectively. Previous studies proposed the utilization of artificial neural networks (ANNs) to alleviate these problems. However, ANNs do not consider the problem of insufficient in situ samples during model training, and they do not fully utilize the spatial and spectral information of remote sensing images in neural networks. In this study, a two-stage training is introduced to address the problem regarding sample insufficiency. The neural network is pretrained using the samples derived from an existing Chl-a concentration model in the first stage, and the pretrained model is refined with in situ samples in the second stage. A novel convolutional neural network for Chl-a concentration retrieval called WaterNet is proposed which utilizes both spectral and spatial information of remote sensing images. In addition, an end-to-end structure that integrates feature extraction, band expansion, and Chl-a estimation into the neural network leads to an efficient and effective Chl-a concentration retrieval. In experiments, Sentinel-3 images with the same acquisition days of in situ measurements over Laguna Lake in the Philippines were used to train and evaluate WaterNet. The quantitative analyses show that the two-stage training is more likely than the one-stage training to reach the global optimum in the optimization, and WaterNet with two-stage training outperforms, in terms of estimation accuracy, related ANN-based and band-combination-based Chl-a concentration models.

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Multiplatform Earth Observation Systems for Monitoring Water Quality in Vulnerable Inland Ecosystems: Maspalomas Water Lagoon

Remote Sensing ◽

10.3390/rs12020284 ◽

2020 ◽

Vol 12 (2) ◽

pp. 284

Author(s):

Francisco Eugenio ◽

Javier Marcello ◽

Javier Martín

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Chlorophyll Concentration ◽

Quality Parameters ◽

Quality Parameter ◽

Water Quality Parameter ◽

Atmospheric Effects ◽

Chl A ◽

Inorganic Matter

The accurate monitoring of water quality indicators, bathymetry and distribution of benthic habitats in vulnerable ecosystems is key to assessing the effects of climate change, the quality of natural areas and to guide appropriate biodiversity, tourism or fisheries policies. Coastal and inland water ecosystems are very complex but crucial due to their richness and primary production. In this context, remote sensing can be a reliable way to monitor these areas, mainly thanks to satellite sensors’ improved spatial and spectral capabilities and airborne or drone instruments. In general, mapping bodies of water is challenging due to low signal-to-noise (SNR) at sensor level, due to the very low reflectance of water surfaces as well as atmospheric effects. Therefore, the main objective of this work is to provide a robust processing framework to estimate water quality parameters in inland shallow waters using multiplatform data. More specifically, we measured chlorophyll concentrations (Chl-a) from multispectral and hyperspectral sensors on board satellites, aircrafts and drones. The Natural Reserve of Maspalomas, Canary Island (Spain), was chosen for the study because of its complexity as well as being an inner lagoon with considerable organic and inorganic matter and chlorophyll concentration. This area can also be considered a well-known coastal-dune ecosystem attracting a large amount of tourists. The water quality parameter estimated by the remote sensing platforms has been validated using co-temporal in situ measurements collected during field campaigns, and quite satisfactory results have been achieved for this complex ecosystem. In particular, for the drone hyperspectral instrument, the root mean square error, computed to quantify the differences between the estimated and in situ chlorophyll-a concentrations, was 3.45 with a bias of 2.96.

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