scholarly journals Remote Estimation of Water Quality Parameters of Medium- and Small-Sized Inland Rivers Using Sentinel-2 Imagery

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3124
Author(s):  
Kuan Huangfu ◽  
Jian Li ◽  
Xinjia Zhang ◽  
Jinping Zhang ◽  
Hao Cui ◽  
...  
Keyword(s):  
Water Quality ◽  
Super Resolution ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Monitoring ◽  
Mixed Pixels ◽  
Resolution Algorithm ◽  
Inland Rivers ◽  
Sentinel 2

In the application of quantitative remote sensing in water quality monitoring, the existence of mixed pixels greatly affects the accuracy of water quality parameter inversion, especially for narrow inland rivers. Improving the image spatial resolution and weakening the interference of mixed pixels in the image are some of the urgent problems to be solved in the study of water quality monitoring of medium- and small-sized inland rivers. We processed Sentinel-2 multispectral images using the super-resolution algorithm and generated a set of 10 m spatial resolution images with basically unchanged reflection characteristics. Both qualitative and quantitative evaluation results show that the super-resolution algorithm can weaken the influence of mixed pixels while maintaining spectral invariance. Before the application of the super-resolution algorithm, the inversion accuracy of water quality parameters in this study were as follows: for NH3-N, the R2 was 0.61, the root mean squared error (RMSE) was 0.177 and the mean absolute percentage error (MAPE) was 29.33%; for Chemical Oxygen Demand (COD), the R2 was 0.26, the RMSE was 0.756 and the MAPE was 4.62%; for Total Phosphorus (TP), the R2 was 0.69, the RMSE was 0.032 and the MAPE was 30.58%. After the application of the super-resolution algorithm, the inversion accuracy of water quality parameters in this study were as follows: for NH3-N, the R2 was 0.67, the RMSE was 0.161 and the MAPE was 25.88%; for COD, the R2 was 0.53, the RMSE was 0.546 and the MAPE was 3.36%; for TP, the R2 was 0.60, the RMSE was 0.034 and the MAPE was 24.28%. Finally, the spatial distribution of NH3-N, COD and TP was obtained by using a machine learning model. The results showed that the application of the super-resolution algorithm can effectively improve the retrieval accuracy of NH3-N, COD and TP, which illustrates the application potential of the super-resolution algorithm in water quality remote sensing quantitative monitoring.

scholarly journals Comparing PlanetScope to Landsat-8 and Sentinel-2 for Sensing Water Quality in Reservoirs in Agricultural Watersheds

2021 ◽  
Vol 13 (9) ◽  
pp. 1847
Author(s):  
Abubakarr S. Mansaray ◽  
Andrew R. Dzialowski ◽  
Meghan E. Martin ◽  
Kevin L. Wagner ◽  
Hamed Gholizadeh ◽  
...  
Keyword(s):  
Water Quality ◽  
Chlorophyll A ◽  
Water Quality Monitoring ◽  
Agricultural Runoff ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Monitoring ◽  
Landsat 8 ◽  
Spectral Bands ◽  
Sentinel 2

Agricultural runoff transports sediments and nutrients that deteriorate water quality erratically, posing a challenge to ground-based monitoring. Satellites provide data at spatial-temporal scales that can be used for water quality monitoring. PlanetScope nanosatellites have spatial (3 m) and temporal (daily) resolutions that may help improve water quality monitoring compared to coarser-resolution satellites. This work compared PlanetScope to Landsat-8 and Sentinel-2 in their ability to detect key water quality parameters. Spectral bands of each satellite were regressed against chlorophyll a, turbidity, and Secchi depth data from 13 reservoirs in Oklahoma over three years (2017–2020). We developed significant regression models for each satellite. Landsat-8 and Sentinel-2 explained more variation in chlorophyll a than PlanetScope, likely because they have more spectral bands. PlanetScope and Sentinel-2 explained relatively similar amounts of variations in turbidity and Secchi Disk data, while Landsat-8 explained less variation in these parameters. Since PlanetScope is a commercial satellite, its application may be limited to cases where the application of coarser-resolution satellites is not feasible. We identified scenarios where PS may be more beneficial than Landsat-8 and Sentinel-2. These include measuring water quality parameters that vary daily, in small ponds and narrow coves of reservoirs, and at reservoir edges.

scholarly journals Deriving Water Quality Parameters Using Sentinel-2 Imagery: A Case Study in the Sado Estuary, Portugal

2021 ◽  
Vol 13 (5) ◽  
pp. 1043
Author(s):  
Giulia Sent ◽  
Beatriz Biguino ◽  
Luciane Favareto ◽  
Joana Cruz ◽  
Carolina Sá ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Monitoring ◽  
Chl A ◽  
Area Of Interest ◽  
Sado Estuary ◽  
Sentinel 2

Monitoring water quality parameters and their ecological effects in transitional waters is usually performed through in situ sampling programs. These are expensive and time-consuming, and often do not represent the total area of interest. Remote sensing techniques offer enormous advantages by providing cost-effective systematic observations of a large water system. This study evaluates the potential of water quality monitoring using Sentinel-2 observations for the period 2018–2020 for the Sado estuary (Portugal), through an algorithm intercomparison exercise and time-series analysis of different water quality parameters (i.e., colored dissolved organic matter (CDOM), chlorophyll-a (Chl-a), suspended particulate matter (SPM), and turbidity). Results suggest that Sentinel-2 is useful for monitoring these parameters in a highly dynamic system, however, with challenges in retrieving accurate data for some of the variables, such as Chl-a. Spatio-temporal variability results were consistent with historical data, presenting the highest values of CDOM, Chl-a, SPM and turbidity during Spring and Summer. This work is the first study providing annual and seasonal coverage with high spatial resolution (10 m) for the Sado estuary, being a key contribution for the definition of effective monitoring programs. Moreover, the potential of remote sensing methodologies for continuous water quality monitoring in transitional systems under the scope of the European Water Framework Directive is briefly discussed.

scholarly journals Use of Artificial Neural Networks and Multiple Linear Regression Model for the Prediction of Dissolved Oxygen in Rivers: Case Study of Hydrographic Basin of River Nyando, Kenya

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-23 ◽  
Author(s):  
Yashon O. Ouma ◽  
Clinton O. Okuku ◽  
Evalyne N. Njau
Keyword(s):  
Water Quality ◽  
Linear Regression ◽  
Dissolved Oxygen ◽  
Multiple Linear Regression ◽  
Pearson Correlation ◽  
Multiple Linear Regression Model ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Monitoring

The process of predicting water quality over a catchment area is complex due to the inherently nonlinear interactions between the water quality parameters and their temporal and spatial variability. The empirical, conceptual, and physical distributed models for the simulation of hydrological interactions may not adequately represent the nonlinear dynamics in the process of water quality prediction, especially in watersheds with scarce water quality monitoring networks. To overcome the lack of data in water quality monitoring and prediction, this paper presents an approach based on the feedforward neural network (FNN) model for the simulation and prediction of dissolved oxygen (DO) in the Nyando River basin in Kenya. To understand the influence of the contributing factors to the DO variations, the model considered the inputs from the available water quality parameters (WQPs) including discharge, electrical conductivity (EC), pH, turbidity, temperature, total phosphates (TPs), and total nitrates (TNs) as the basin land-use and land-cover (LULC) percentages. The performance of the FNN model is compared with the multiple linear regression (MLR) model. For both FNN and MLR models, the use of the eight water quality parameters yielded the best DO prediction results with respective Pearson correlation coefficient R values of 0.8546 and 0.6199. In the model optimization, EC, TP, TN, pH, and temperature were most significant contributing water quality parameters with 85.5% in DO prediction. For both models, LULC gave the best results with successful prediction of DO at nearly 98% degree of accuracy, with the combination of LULC and the water quality parameters presenting the same degree of accuracy for both FNN and MLR models.

scholarly journals Monitoring Water Quality of the Haihe River Based on Ground-Based Hyperspectral Remote Sensing

Water ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 22
Author(s):  
Qi Cao ◽  
Gongliang Yu ◽  
Shengjie Sun ◽  
Yong Dou ◽  
Hua Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Real Time ◽  
Training Model ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Hyperspectral Remote Sensing ◽  
Water Quality Parameters ◽  
Quality Monitoring ◽  
Haihe River

The Haihe River is a typical sluice-controlled river in the north of China. The construction and operation of sluice dams change the flow and other hydrological factors of rivers, which have adverse effects on water, making it difficult to study the characteristics of water quality change and water environment control in northern rivers. In recent years, remote sensing has been widely used in water quality monitoring. However, due to the low signal-to-noise ratio (SNR) and the limitation of instrument resolution, satellite remote sensing is still a challenge to inland water quality monitoring. Ground-based hyperspectral remote sensing has a high temporal-spatial resolution and can be simply fixed in the water edge to achieve real-time continuous detection. A combination of hyperspectral remote sensing devices and BP neural networks is used in the current research to invert water quality parameters. The measured values and remote sensing reflectance of eight water quality parameters (chlorophyll-a (Chl-a), phycocyanin (PC), total suspended sediments (TSS), total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH4-N), nitrate-nitrogen (NO3-N), and pH) were modeled and verified. The results show that the performance R2 of the training model is above 80%, and the performance R2 of the verification model is above 70%. In the training model, the highest fitting degree is TN (R2 = 1, RMSE = 0.0012 mg/L), and the lowest fitting degree is PC (R2 = 0.87, RMSE = 0.0011 mg/L). Therefore, the application of hyperspectral remote sensing technology to water quality detection in the Haihe River is a feasible method. The model built in the hyperspectral remote sensing equipment can help decision-makers to easily understand the real-time changes of water quality parameters.

scholarly journals Near real time water quality monitoring of Chivero and Manyame lakes of Zimbabwe

2018 ◽  
Vol 378 ◽  
pp. 85-92 ◽  
Author(s):  
Ronald Muchini ◽  
Webster Gumindoga ◽  
Sydney Togarepi ◽  
Tarirai Pinias Masarira ◽  
Timothy Dube
Keyword(s):  
Water Quality ◽  
Water Quality Monitoring ◽  
Remotely Sensed ◽  
Quality Parameters ◽  
Point Sources ◽  
Water Quality Parameters ◽  
Quality Monitoring ◽  
Quality Information ◽  
Web Portal

Abstract. Zimbabwe's water resources are under pressure from both point and non-point sources of pollution hence the need for regular and synoptic assessment. In-situ and laboratory based methods of water quality monitoring are point based and do not provide a synoptic coverage of the lakes. This paper presents novel methods for retrieving water quality parameters in Chivero and Manyame lakes, Zimbabwe, from remotely sensed imagery. Remotely sensed derived water quality parameters are further validated using in-situ data. It also presents an application for automated retrieval of those parameters developed in VB6, as well as a web portal for disseminating the water quality information to relevant stakeholders. The web portal is developed, using Geoserver, open layers and HTML. Results show the spatial variation of water quality and an automated remote sensing and GIS system with a web front end to disseminate water quality information.

scholarly journals Raspberry Pi based Smart Sensing Platform for Drinking Water Quality Monitoring System: A Python Framework Approach

2019 ◽  
Author(s):  
Punit Khatri ◽  
Karunesh Kumar Gupta ◽  
Raj Kumar Gupta
Keyword(s):  
Water Quality ◽  
Potable Water ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Monitoring ◽  
Computing Technique ◽  
Sensing Platform ◽  
Smart Sensing ◽  
Potable Water Quality

Abstract. Drinking or potable water quality monitoring is essential for mankind as it affects the human health directly or indirectly. This work reports a smart sensing platform for potable water quality monitoring. Five water quality parameters (pH, Dissolved Oxygen, Oxidation Reduction Potential, Electrical Conductivity, and Temperature) have been selected to monitor the water quality. The selection of water quality parameters is made based on guidelines of the Central Pollution and Control Board, New Delhi, India. A Graphical User Interface (GUI) is developed to provide an interactive Human Machine Interface for the end user. Python programming language is used for GUI development, data acquisition and for data analysis. Fuzzy computing technique is employed for decision making to categorize the water quality in different classes like bad, poor, satisfactory, good and excellent. The system has been tested for various water resources and results have been displayed.

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.

Study on Automation Control with Water Quality Monitoring System Based on GPRS Technology

2013 ◽  
Vol 738 ◽  
pp. 239-242 ◽  
Author(s):  
Shi Wei Lin ◽  
Yu Wen Zhai
Keyword(s):  
Water Quality ◽  
Monitoring System ◽  
Production Efficiency ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
System Structure ◽  
Quality Monitoring ◽  
Automation Control ◽  
Economy Benefit

The method of water quality monitoring applied by reservoirs is sampling in the scene and analyzing at the laboratory at present. Based on analyzing key problem of water quality monitoring, automatic water quality monitoring system based on GPRS is provided in this paper. The system structure and principle are introduced. The system collects, transmits and processes water quality parameters automatically, so the production efficiency and the economy benefit are improved greatly.

scholarly journals IDENTIFICATION OF WATER QUALITY SIGNIFICANT PARAMETER WITH TWO TRANSFORMATION/STANDARDIZATION METHODS ON PRINCIPAL COMPONENT ANALYSIS AND SCILAB SOFTWARE

2017 ◽  
Vol 2 (3) ◽  
pp. 223 ◽  
Author(s):  
Jovan Putranda ◽  
Sri Puji Saraswati
Keyword(s):  
Water Quality ◽  
Principal Component ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Monitoring ◽  
Standardization Method ◽  
Validity And Reliability ◽  
Significant Parameter ◽  
Standardization Methods

Water quality monitoring is prone to encounter error on its recording or measuring process. The monitoring on river water quality not only aims to recognize the water quality dynamic, but also to evaluate the data to create river management policy and water pollution in order to maintain the continuity of human health/sanitation requirement, and biodiversity preservation. Evaluation on water quality monitoring needs to be started by identifying the important water quality parameter. This research objected to identify the significant parameters by using two transformation/standardization methods on water quality data, which are the river Water Quality Index, WQI (Indeks Kualitas Air, Sungai, IKAs) transformation/standardization method and transformation/standardization method with mean 0 and variance 1; so that the variability of water quality parameters could be aggregated with one another. Both of the methods were applied on the water quality monitoring data which its validity and reliability have been tested. The PCA, Principal Component Analysis (Analisa Komponen Utama, AKU), with the help of Scilab software, has been used to process the secondary data on water quality parameters of Gadjah Wong river in 2004-2013, with its validity and reliability has been tested. The Scilab result was cross examined with the result from the Excel-based Biplot Add In software. The research result showed that only 18 from total 35 water quality parameters that have passable data quality. The two transformation/standardization data methods gave different significant parameter type and amount result. On the transformation/standardization mean 0 variances 1, there were water quality significant parameter dynamic to mean concentration of each water quality parameters, which are TDS, SO4, EC, TSS, NO3N, COD, BOD5, Grease Oil and NH3N. On the river WQI transformation/standardization, the water quality significant parameter showed the level of Gadjah Wong River pollution, which are EC, DO, BOD5, COD, NH3N, Fecal Coliform, and Total Coliform. These seven parameters is the minimal amount of water quality parameters that has to be consistently measured on predetermined time and location, and also become the indicator of human health and environment health quality. The result of Scilab multivariate analysis was not different with the result from Biplot Add In multivariate analysis, in which the results of water quality significant parameter has been verified with bio-monitoring.

scholarly journals Water monitoring and analytic based thingspeak

2020 ◽  
Vol 10 (4) ◽  
pp. 3588
Author(s):  
Abbas Hussien Miry ◽  
Gregor Alexander Aramice
Keyword(s):  
Water Quality ◽  
Normal Values ◽  
Water Quality Monitoring ◽  
Lag Time ◽  
Quality Parameters ◽  
Quality Monitoring ◽  
Proposed Model ◽  
Response To Water ◽  
Matlab Programming ◽  
The Internet Of Things

Diseases associated with bad water have largely reported cases annually leading to deaths, therefore the water quality monitoring become necessary to provide safe water. Traditional monitoring includes manual gathering of samples from different points on the distributed site, and then testing in laboratory. This procedure has proven that it is ineffective because it is laborious, lag time and lacks online results to enhance proactive response to water pollution. Emergence of the Internet of Things (IoT) and step towards the smart life poses the successful using of IoT. This paper presents a water quality monitoring using IoT based ThingSpeak platform that provides analytic tools and visualization using MATLAB programming. The proposed model is used to test water samples using sensor fusion technique such as TDS and Turbidity, and then uploading data online to ThingSpeak platform to monitor and analyze. The system notifies authorities when there are water quality parameters out of a predefined set of normal values. A warning will be notified to user by IFTTT protocol.

Sign in / Sign up

Export Citation Format

Share Document