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

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2699 ◽  
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.

scholarly journals 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

2021 ◽  
Author(s):  
Aakash De ◽  
Ismail Mondal ◽  
Subhanil Nandi ◽  
Sandeep Thakur ◽  
Mini Raman ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Chlorophyll A ◽  
Monsoon Season ◽  
Quality Analysis ◽  
Quality Model ◽  
Near Surface ◽  
Chl A ◽  
North East

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.

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

Water ◽  
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.

scholarly journals Monitoring Water Quality of Valle de Bravo Reservoir, Mexico, Using Entire Lifespan of MERIS Data and Machine Learning Approaches

2020 ◽  
Vol 12 (10) ◽  
pp. 1586
Author(s):  
Leonardo F. Arias-Rodriguez ◽  
Zheng Duan ◽  
Rodrigo Sepúlveda ◽  
Sergio I. Martinez-Martinez ◽  
Markus Disse
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Water Quality ◽  
Water Quality Monitoring ◽  
Temporal Variations ◽  
Quality Parameters ◽  
Parameters Estimation ◽  
Learning Approaches ◽  
Effects Of Drought

Remote-sensing-based machine learning approaches for water quality parameters estimation, Secchi Disk Depth (SDD) and Turbidity, were developed for the Valle de Bravo reservoir in central Mexico. This waterbody is a multipurpose reservoir, which provides drinking water to the metropolitan area of Mexico City. To reveal the water quality status of inland waters in the last decade, evaluation of MERIS imagery is a substantial approach. This study incorporated in-situ collected measurements across the reservoir and remote sensing reflectance data from the Medium Resolution Imaging Spectrometer (MERIS). Machine learning approaches with varying complexities were tested, and the optimal model for SDD and Turbidity was determined. Cross-validation demonstrated that the satellite-based estimates are consistent with the in-situ measurements for both SDD and Turbidity, with R2 values of 0.81 to 0.86 and RMSE of 0.15 m and 0.95 nephelometric turbidity units (NTU). The best model was applied to time series of MERIS images to analyze the spatial and temporal variations of the reservoir’s water quality from 2002 to 2012. Derived analysis revealed yearly patterns caused by dry and rainy seasons and several disruptions were identified. The reservoir varied from trophic to intermittent hypertrophic status, while SDD ranged from 0–1.93 m and Turbidity up to 23.70 NTU. Results suggest the effects of drought events in the years 2006 and 2009 on water quality were correlated with water quality detriment. The water quality displayed slow recovery through 2011–2012. This study demonstrates the usefulness of satellite observations for supporting inland water quality monitoring and water management in this region.

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

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.

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

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.

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

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.

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

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

<p>A decline in Lake Chad’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.</p><p>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.</p><p>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.</p><p>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.</p>

Spatiotemporal Variation of Chlorophyll a and its Relationship with other Water Quality Factors in the Tai Lake

2011 ◽  
Vol 183-185 ◽  
pp. 783-789
Author(s):  
Xing Cai Liu ◽  
Zong Xue Xu ◽  
Guo Qiang Wang
Keyword(s):  
Water Quality ◽  
Chlorophyll A ◽  
Temporal Variations ◽  
Quality Factors ◽  
Mean Values ◽  
Chl A ◽  
Four Seasons ◽  
Tai Lake ◽  
Sample Data ◽  
Meiliang Bay

Algae bloom in the Tai Lake is a major issue and affects the water supply to the surrounding cities greatly. Chlorophyll a (Chl-a) is a common indicator that represent the trophic status in lakes. Spatial and temporal variations of Chl-a concentration are analyzed on the basis of sample data at 21 sites during the period of 2001 to 2005. Data at the sites located in the Meiliang Bay, Zhushan and Wulihu show greater fluctuations than that at other sites. A general trend showing that high values in northern part and low values in southern part of the Tai Lake is observed in seasonal mean values of Chl-a concentration for four seasons. Most high Chl-a concentrations occurred in summer (June to August) and autumn (September to November). Quantitative relationships between Chl-a and other water quality factors are investigated at all sites. Relative good relationships are obtained between Chl-a concentration and other water quality factors during 2001 to 2004 by using partial least squared regression. Prediction of Chl-a concentration in 2005 with above models produce worse results, which may be due to the occurrence of some extreme high values of Chl-a concentration in that year. Even though, acceptable predictions are obtained at several sites. Since the water quality in the lake is affected greatly by the inflow of nutrients from rivers, these relationships will be helpful for monitoring Chl-a variation with the combination of hydrological models that is able to simulate the inflow of nutrients.

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

Electronics ◽  
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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