scholarly journals CoastColour Round Robin datasets: a database to evaluate the performance of algorithms for the retrieval of water quality parameters in coastal waters

2015 ◽  
Vol 8 (1) ◽  
pp. 173-258 ◽  
Author(s):  
B. Nechad ◽  
K. Ruddick ◽  
T. Schroeder ◽  
K. Oubelkheir ◽  
D. Blondeau-Patissier ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Coastal Waters ◽  
Quality Parameters ◽  
Round Robin ◽  
Water Quality Parameters ◽  
Quality Data ◽  
Ocean Colour ◽  
Water Quality Data

Abstract. The use of in situ measurements is essential in the validation and evaluation of the algorithms that provide coastal water quality data products from ocean colour satellite remote sensing. Over the past decade, various types of ocean colour algorithms have been developed to deal with the optical complexity of coastal waters. Yet there is a lack of a comprehensive inter-comparison due to the availability of quality checked in situ databases. The CoastColour project Round Robin (CCRR) project funded by the European Space Agency (ESA) was designed to bring together a variety of reference datasets and to use these to test algorithms and assess their accuracy for retrieving water quality parameters. This information was then developed to help end-users of remote sensing products to select the most accurate algorithms for their coastal region. To facilitate this, an inter-comparison of the performance of algorithms for the retrieval of in-water properties over coastal waters was carried out. The comparison used three types of datasets on which ocean colour algorithms were tested. The description and comparison of the three datasets are the focus of this paper, and include the Medium Resolution Imaging Spectrometer (MERIS) Level 2 match-ups, in situ reflectance measurements and data generated by a radiative transfer model (HydroLight). These datasets are available from doi.pangaea.de/10.1594/PANGAEA.841950. The datasets mainly consisted of 6484 marine reflectance associated with various geometrical (sensor viewing and solar angles) and sky conditions and water constituents: Total Suspended Matter (TSM) and Chlorophyll a (CHL) concentrations, and the absorption of Coloured Dissolved Organic Matter (CDOM). Inherent optical properties were also provided in the simulated datasets (5000 simulations) and from 3054 match-up locations. The distributions of reflectance at selected MERIS bands and band ratios, CHL and TSM as a function of reflectance, from the three datasets are compared. Match-up and in situ sites where deviations occur are identified. The distribution of the three reflectance datasets are also compared to the simulated and in situ reflectances used previously by the International Ocean Colour Coordinating Group (IOCCG, 2006) for algorithm testing, showing a clear extension of the CCRR data which covers more turbid waters.

scholarly journals CoastColour Round Robin data sets: a database to evaluate the performance of algorithms for the retrieval of water quality parameters in coastal waters

2015 ◽  
Vol 7 (2) ◽  
pp. 319-348 ◽  
Author(s):  
B. Nechad ◽  
K. Ruddick ◽  
T. Schroeder ◽  
K. Oubelkheir ◽  
D. Blondeau-Patissier ◽  
...  
Keyword(s):  
Water Quality ◽  
Coastal Waters ◽  
Reference Data ◽  
Quality Parameters ◽  
Round Robin ◽  
Water Quality Parameters ◽  
Quality Data ◽  
Data Sets ◽  
Ocean Colour

Abstract. The use of in situ measurements is essential in the validation and evaluation of the algorithms that provide coastal water quality data products from ocean colour satellite remote sensing. Over the past decade, various types of ocean colour algorithms have been developed to deal with the optical complexity of coastal waters. Yet there is a lack of a comprehensive intercomparison due to the availability of quality checked in situ databases. The CoastColour Round Robin (CCRR) project, funded by the European Space Agency (ESA), was designed to bring together three reference data sets using these to test algorithms and to assess their accuracy for retrieving water quality parameters. This paper provides a detailed description of these reference data sets, which include the Medium Resolution Imaging Spectrometer (MERIS) level 2 match-ups, in situ reflectance measurements, and synthetic data generated by a radiative transfer model (HydroLight). These data sets, representing mainly coastal waters, are available from doi:10.1594/PANGAEA.841950. The data sets mainly consist of 6484 marine reflectance (either multispectral or hyperspectral) associated with various geometrical (sensor viewing and solar angles) and sky conditions and water constituents: total suspended matter (TSM) and chlorophyll a (CHL) concentrations, and the absorption of coloured dissolved organic matter (CDOM). Inherent optical properties are also provided in the simulated data sets (5000 simulations) and from 3054 match-up locations. The distributions of reflectance at selected MERIS bands and band ratios, CHL and TSM as a function of reflectance, from the three data sets are compared. Match-up and in situ sites where deviations occur are identified. The distributions of the three reflectance data sets are also compared to the simulated and in situ reflectances used previously by the International Ocean Colour Coordinating Group (IOCCG, 2006) for algorithm testing, showing a clear extension of the CCRR data which covers more turbid waters.

Generation of geolocated and radiometrically corrected true reflectance surfaces in the visible portion of the electromagnetic spectrum over large bodies of water using images from a sUAS

2020 ◽  
Vol 8 (3) ◽  
pp. 172-185
Author(s):  
Juan G. Arango ◽  
Brandon K. Holzbauer-Schweitzer ◽  
Robert W. Nairn ◽  
Robert C. Knox
Keyword(s):  
Water Quality ◽  
Near Infrared ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Data ◽  
Electromagnetic Spectrum ◽  
Water Quality Data ◽  
Ground Control Points ◽  
Bodies Of Water

The focus of this study was to develop true reflectance surfaces in the visible portion of the electromagnetic spectrum from small unmanned aerial system (sUAS) images obtained over large bodies of water when no ground control points were available. The goal of the research was to produce true reflectance surfaces from which reflectance values could be extracted and used to estimate optical water quality parameters utilizing limited in-situ water quality analyses. Multispectral imagery was collected using a sUAS equipped with a multispectral sensor, capable of obtaining information in the blue (0.475 μm), green (0.560 μm), red (0.668 μm), red edge (0.717 μm), and near infrared (0.840 μm) portions of the electromagnetic spectrum. To develop a reliable and repeatable protocol, a five-step methodology was implemented: (i) image and water quality data collection, (ii) image processing, (iii) reflectance extraction, (iv) statistical interpolation, and (v) data validation. Results indicate that the created protocol generates geolocated and radiometrically corrected true reflectance surfaces from sUAS missions flown over large bodies of water. Subsequently, relationships between true reflectance values and in-situ water quality parameters were developed.

scholarly journals Remote sensing-based algorithms for water quality monitoring in Olushandja Dam, north-Central Namibia

2020 ◽  
Author(s):  
Taimi S. Kapalanga ◽  
Zvikomborero Hoko ◽  
Webster Gumindoga ◽  
Loyd Chikwiramakomo
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Linear Relationship ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Monitoring ◽  
Quality Data ◽  
Water Quality Data ◽  
Measured Water

Abstract Frequent and continuous water quality monitoring of Olushandja Dam in Namibia is needed to inform timely decision making. This study was carried out from November 2014 to June 2015 with Landsat 8 reflectance values and field measured water quality data that were used to develop regression analysis-based retrieval algorithms. Water quality parameters considered included turbidity, total suspended solids (TSS), nitrates, ammonia, total nitrogen (TN), total phosphorus (TP) and total algae counts. Results show that turbidity levels exceeded the recommended limits for raw water for potable water treatment while TN and TP values are within acceptable values. Turbidity, TN, and TP and total algae count showed a medium to strong positive linear relationship between Landsat predicted and measured water quality data while TSS showed a weak linear relationship. The regression coefficients between predicted and measured values were: turbidity (R2 = 0.767); TN (R2 = 0.798,); TP (R2 = 0.907); TSS (R2 = 0.284,) and total algae count (R2 = 0.851). Prediction algorithms are generally best fit to derive water quality parameters. Remote sensing is recommended for frequent and continuous monitoring of Olushandja Dam as it has the ability to provide rapid information on the spatio-temporal variability of surface water quality.

Assessement of Water Quality Parameters in Some Potential Pollutant Areas from Romania

2018 ◽  
Vol 69 (8) ◽  
pp. 2045-2049
Author(s):  
Catalina Gabriela Gheorghe ◽  
Andreea Bondarev ◽  
Ion Onutu
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Surface Water Quality ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Data ◽  
Chemical Parameters ◽  
Water Quality Data ◽  
Pollutant Sources ◽  
Water Quality Forecasting

Monitoring of environmental factors allows the achievement of some important objectives regarding water quality, forecasting, warning and intervention. The aim of this paper is to investigate water quality parameters in some potential pollutant sources from northern, southern and east-southern areas of Romania. Surface water quality data for some selected chemical parameters were collected and analyzed at different points from March to May 2017.

Effects of sampling frequency on estimation accuracies of annual loadings for water quality parameters in different sized watersheds

2020 ◽  
Vol 55 (3) ◽  
pp. 261-277
Author(s):  
Lin Gao ◽  
Junyu Qi ◽  
Sheng Li ◽  
Glenn Benoy ◽  
Zisheng Xing ◽  
...  
Keyword(s):  
Water Quality ◽  
Suspended Solids ◽  
Nitrate Nitrogen ◽  
Sampling Frequency ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Data ◽  
Estimation Errors ◽  
Water Quality Data ◽  
Novel Approach

Abstract Potential errors or uncertainties of annual loading estimations for water quality parameters such as suspended solids (SS), nitrate-nitrogen (NO3-N), ortho-phosphorus (Ortho-P), potassium (K), calcium (Ca), and magnesium (Mg) can be greatly affected by sampling frequencies. In this study, annual loading estimation errors were assessed in terms of the coefficient of variation, relative bias, and probability of potential errors that were estimated with statistical samples taken at a series of sampling frequencies for a watershed in northwestern New Brunswick, Canada, and one of its sub-watersheds. Results indicate that annual loading estimation errors increased with decreasing sampling frequency for all water quality parameters. At the same sampling frequencies, the estimation errors were several times greater for the smaller watershed than those for the larger watershed, possibly due to the flushing nature of streamflows in the smaller watershed. We also found that low sampling frequency tended to underestimate the annual loadings of water quality parameters dominated by stormflow events (SS and K) and overestimate water quality parameters dominated by baseflow (Mg and Ca). These results can be used by hydrologists and water quality managers to determine sampling frequencies that minimize costs while providing acceptable estimation errors. This study also demonstrates a novel approach to assess potential errors when analyzing existing water quality data.

WQTA — A Water Quality Trend Analysis Program

1993 ◽  
Vol 28 (2) ◽  
pp. 311-336 ◽  
Author(s):  
I.K. Tsanis
Keyword(s):  
Water Quality ◽  
Trend Analysis ◽  
Rank Correlation ◽  
Data Bank ◽  
Quality Parameters ◽  
Specific Conductance ◽  
Water Quality Parameters ◽  
Downward Trend ◽  
Quality Data ◽  
Water Quality Data

Abstract A series of programs have been developed using the statistical package Minitab to evaluate trends of water quality parameters over a time period. These programs are included in an interactive program with graphic capabilities called Water Quality Trend Analysis (WQTA). The output files from the retrieval and year programs of the National Water Quality Data Bank (NAQUADAT) are used as input files to the program. The graphic output is obtained using the graphical package Axum. Twelve-month moving averages and the Spearman’s rank correlation are applied for trend assessments. The components of variability (seasonal, trend and random) of the water quality parameters are modelled using linear regression. The methods are applied successfully to selected physical and chemical water quality parameters collected at the mouth of Niagara River, at Niagara-on-the-Lake, during the period 1976–89. The specific conductance was decreasing for the period as the discharge was increasing, due to higher dilution effects. A modest downward trend for total phosphorus was observed for the period 1976–84, and there is no trend between 1984-89. A strong decreasing trend for chloride was observed during the 1977–84 period but this has levelled off since then. A strong upward trend for iron and a weak downward trend for lead was evident over the study period.

scholarly journals Evaluation of RapidEye-3 Satellite Data for Assessing Water Turbidity of Lake Borabey

Proceedings ◽  
2019 ◽  
Vol 48 (1) ◽  
pp. 14
Author(s):  
Gordana Kaplan ◽  
Zehra Yigit Avdan ◽  
Serdar Goncu ◽  
Ugur Avdan
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Quality Parameters ◽  
In Situ Measurements ◽  
Water Quality Parameters ◽  
Water Turbidity ◽  
Sensing Data

In water resources management, remote sensing data and techniques are essential in watershed characterization and monitoring, especially when no data are available. Water quality is usually assessed through in-situ measurements that require high cost and time. Water quality parameters help in decision making regarding the further use of water-based on its quality. Turbidity is an important water quality parameter and an indicator of water pollution. In the past few decades, remote sensing has been widely used in water quality research. In this study, we compare turbidity parameters retrieved from a high-resolution image with in-situ measurements collected from Borabey Lake, Turkey. Here, the use of RapidEye-3 images (5 m-resolution) allows for detailed assessment of spatio-temporal evaluation of turbidity, through the normalized difference turbidity index (NDTI). The turbidity results were then compared with data from 21 in-situ measurements collected in the same period. The actual water turbidity measurements showed high correlation with the estimated NDTI mean values with an R2 of 0.84. The research findings support the use of remote sensing data of RadipEye-3 to estimate water quality parameters in small water areas. For future studies, we recommend investigating different water quality parameters using high-resolution remote sensing data.

scholarly journals Water Quality Prediction in the Luan River Based on 1-DRCNN and BiGRU Hybrid Neural Network Model

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1273
Author(s):  
Jianzhuo Yan ◽  
Jiaxue Liu ◽  
Yongchuan Yu ◽  
Hongxia Xu
Keyword(s):  
Water Quality ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Data ◽  
Prediction Algorithm ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Quality Prediction ◽  
Water Quality Data ◽  
Water Quality Prediction

The current global water environment has been seriously damaged. The prediction of water quality parameters can provide effective reference materials for future water conditions and water quality improvement. In order to further improve the accuracy of water quality prediction and the stability and generalization ability of the model, we propose a new comprehensive deep learning water quality prediction algorithm. Firstly, the water quality data are cleaned and pretreated by isolation forest, the Lagrange interpolation method, sliding window average, and principal component analysis (PCA). Then, one-dimensional residual convolutional neural networks (1-DRCNN) and bi-directional gated recurrent units (BiGRU) are used to extract the potential local features among water quality parameters and integrate information before and after time series. Finally, a full connection layer is used to obtain the final prediction results of total nitrogen (TN), total phosphorus (TP), and potassium permanganate index (COD-Mn). Our prediction experiment was carried out according to the actual water quality data of Daheiting Reservoir, Luanxian Bridge, and Jianggezhuang at the three control sections of the Luan River in Tangshan City, Hebei Province, from 5 July 2018 to 26 March 2019. The minimum mean absolute percentage error (MAPE) of this method was 2.4866, and the coefficient of determination (R2) was able to reach 0.9431. The experimental results showed that the model proposed in this paper has higher prediction accuracy and generalization than the existing LSTM, GRU, and BiGRU models.

scholarly journals WATER QUALITY ASSESSMENT OF SAIGON RIVER FOR PUBLIC WATER SUPPLY BASED ON WATER QUALITY INDEX

2020 ◽  
Vol 58 (5A) ◽  
pp. 85
Author(s):  
Thuy Chau To
Keyword(s):  
Water Quality ◽  
Water Supply ◽  
Water Quality Index ◽  
Quality Index ◽  
Water Quality Management ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Data ◽  
Public Water Supply ◽  
Water Quality Data

Water Quality Index (WQI) is a dimensional number that aggregates information from many water quality parameters according to a defined method. WQI is accepted as an efficient tool for water quality management. In this study, WQI of Saigon river for public water supply was calculated from nine water quality parameters including pH, suspended solids (SS), dissolved oxygen (DO), chemical oxygen demand (COD), nitrite, ammonia, phosphate, total dissolved iron and total coliform based on water quality data obtained monthly from January 2016 to December 2019 at three sampling sites. The results showed that most of WQI values belonged to class III (medium water quality with the WQIs of 35 – 64) and class IV (poor water quality with the WQIs of 11 – 34) and a deteriorating trend was observed from upstream to downstream of Saigon river. The river water quality could not be used for public water supply.

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

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<sup>-1</sup> and 24.858mgL<sup>-1</sup>, Turbidity varied between 16.583mgL<sup>-1</sup> and 48.867mgL<sup>-1</sup> and Secchi depth varied between 0.104mgL<sup>-1</sup> and 0.375mgL<sup>-1</sup> 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 R<sup>2</sup> values. The model versus in situ analysis results demonstrated R<sup>2</sup>= 0.81% for Chlorophyll-a, R<sup>2</sup>= 0.81%  for Turbidity and R<sup>2</sup>= 0.78% for Secchi depth correlation and that of model versus satellite data exhibited R<sup>2</sup>= 0.60% for Chlorophyll-a, R<sup>2</sup>= 0.66% for Turbidity and R<sup>2</sup>= 0.65 %  for Secchi depth mean efficiency.

Sign in / Sign up

Export Citation Format

Share Document