scholarly journals Assessment of Water Quality Parameters Using Temporal Remote Sensing Spectral Reflectance in Arid Environments, Saudi Arabia

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 556 ◽  
Author(s):  
Mohamed Elhag ◽  
Ioannis Gitas ◽  
Anas Othman ◽  
Jarbou Bahrawi ◽  
Petros Gikas
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Remote Sensing Data ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Arid Environments ◽  
Water Turbidity ◽  
Mean Values ◽  
Sensing Data ◽  
Sentinel 2

Remote sensing applications in water resources management are quite essential in watershed characterization, particularly when mega basins are under investigation. Water quality parameters help in decision making regarding the further use of water based on its quality. Water quality parameters of chlorophyll a concentration, nitrate concentration, and water turbidity were used in the current study to estimate the water quality parameters in the dam lake of Wadi Baysh, Saudi Arabia. Water quality parameters were collected daily over 2 years (2017–2018) from the water treatment station located within the dam vicinity and were correspondingly tested against remotely sensed water quality parameters. Remote sensing data were collected from Sentinel-2 sensor, European Space Agency (ESA) on a satellite temporal resolution basis. Data were pre-processed then processed to estimate the maximum chlorophyll index (MCI), green normalized difference vegetation index (GNDVI) and normalized difference turbidity index (NDTI). Zonal statistics were used to improve the regression analysis between the spatial data estimated from the remote sensing images and the nonspatial data collected from the water treatment plant. Results showed different correlation coefficients between the ground truth collected data and the corresponding indices conducted from remote sensing data. Actual chlorophyll a concentration showed high correlation with estimated MCI mean values with an R2 of 0.96, actual nitrate concentration showed high correlation with the estimated GNDVI mean values with an R2 of 0.94, and the actual water turbidity measurements showed high correlation with the estimated NDTI mean values with an R2 of 0.94. The research findings support the use of remote sensing data of Sentinel-2 to estimate water quality parameters in arid environments.

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.

Adaptive ML-Models for analysis of TSS, Chlorophyll-a, in mixed water type scenarios in Ca Mau peninsula, Vietnam

2021 ◽  
Author(s):  
Long Vu Huu ◽  
Andreas Schenk ◽  
Stefan Hinz
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Surface Water ◽  
Remote Sensing Data ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Water Type ◽  
Water Types ◽  
Chl A ◽  
Sentinel 2

<p>The multispectral mission of Sentinel-2 enables reliable, affordable and continuous environmental monitoring systems in fields like agriculture, biodiversity, environmental hazards and surface water. Several studies have proven that main water quality parameters like total suspended solids (TSS) and chlorophyll (Chl-a) can be estimated from multispectral data using different methods and algorithms. However, independently of the specific approach, these algorithms are selected and optimized to work primarily for one of the main water types i.e. open water, coastal water or inland water. This is also shown by the fact that there is not a single universal algorithm, which can be applied to all water types with consistent and reliable performance at the same time.</p><p>Ca Mau peninsula is a spacious area located in the southern part of the Mekong Delta, with an area of around 1.6 million hectares. This area has high growth rates of agricultural and aquaculture production, hence diverse water demands and water use types. In this study we use Sentinel-2 remote sensing data to monitor surface water quality using adaptive ML models to account for the different surface water types which occur in this area. Through using remote sensing data, we can provide a synoptic and sufficient view in spatial aspects about water quality parameters in the Ca Mau peninsula. Adapting the ML model will address the bio-optical model for a mixed water scenario.</p><p>The study is based on Sentinel-2 satellite images acquired in 2019 and 2020, supplemented by field data, i.e. hyperspectral measurements using close range observations, in-situ measurements and water samples, with the aim to collect a comprehensive reference data set as biophysical parameters are closely connected with spectral parameters at close range as well as at high spectral resolution. Therefore, surface hyperspectral measurement has been used to simulate Sentinel 2 multispectral image data at the respective bands.</p><p>We automatically assign the water type classes to observed surface water by integrating GIS data and remote sensing as the pre-processing step. For each class, the ML models are trained based on the experimental measurements with the multispectral and the simulated multispectral images on the respective water types. We devote special attention to water type boundaries to provide a smooth transition of estimated parameters.</p><p>The outputs of this model are surface water quality distribution maps with turbidity, TSS, and Chl-a parameters for all areas in Ca Mau peninsula, independent of the actual water type. Through the acceptable accuracy of model testing, the consolidation model will contribute water quality parameters that are crucial and meaningful to the planning and use of water for domestic use and production, besides, it also supports the decision-making of sustainable water use.</p>

Retrieval of Water Quality Parameters in Lake Ontario Based on Hyperspectral Remote Sensing Data and Intelligent Algorithms

2020 ◽  
Author(s):  
Yu Li ◽  
Youyue Sun ◽  
Jinhui Jeanne Huang ◽  
Edward McBean
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Water Quality ◽  
Remote Sensing Data ◽  
Quality Parameters ◽  
Hyperspectral Remote Sensing ◽  
Optically Active ◽  
Water Quality Parameters ◽  
Support Vector ◽  
Sensing Data

<p>With the increasingly prominent ecological and environmental problems in lakes, the monitoring water quality in lakes by satellite remote sensing is becoming more and more high demanding. Traditional water quality sampling is normally conducted manually and are time-consuming and labor-costly. It could not provide a full picture of the waterbodies over time due to limited sampling points and low sampling frequency. A novel attempt is proposed to use hyperspectral remote sensing in conjunction with machine learning technologies to retrieve water quality parameters and provide mapping for these parameters in a lake. The retrieval of both optically active parameters: Chlorophyll-a (CHLA) and dissolved oxygen concentration (DO), as well as non-optically active parameters: total phosphorous (TP), total nitrogen (TN), turbidity (TB), pH were studied in this research. A comparison of three machine learning algorithms including Random Forests (RF), Support Vector Regression (SVR) and Artificial Neural Networks were conducted. These water parameters collected by the Environment and Climate Change Canada agency for 20 years were used as the ground truth for model training and validation. Two set of remote sensing data from MODIS and Sentinel-2 were utilized and evaluated. This research proposed a new approach to retrieve both optically active parameters and non-optically active parameters for water body and provide new strategy for water quality monitoring.</p>

scholarly journals Detecting spatiotemporal variability in the physicochemical properties of water in the Lower Mearim using remote sensing data

2020 ◽  
Vol 42 ◽  
pp. e32
Author(s):  
George Colares Silva Filho ◽  
Juliana Martins dos Santos ◽  
Paulo Cesar Mendes Villis ◽  
Ingrid Santos Gonçalves ◽  
Isael Coelho Correia ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Physicochemical Properties ◽  
Monitoring Program ◽  
Remote Sensing Data ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Spatiotemporal Variability ◽  
Landsat 8 ◽  
Solids Content

Natural or anthropogenic chemical compounds of different origins often accumulate in estuarine regions. These compounds may alter the water quality. Therefore, It is important to constantly monitor the quality of estuarine regions. A combination of remote sensing and traditional sampling can lead to a better monitoring program for water quality parameters. The objective of this work is to assess the spatiotemporal variability of the physicochemical properties of water in the lower region of the Mearim River and estimate water quality parameters via remote sensing. Samples were collected at 16 points, from Baixo Arari to the mouth of the watershed, using a multiparameter meter and Landsat 8 satellite images. The physicochemical parameters of the water had high salinity levels, between 2.30 and 20.10 parts per trillion; a high total dissolved solids content, between 2.77 and 19.70 g/L; and minimum dissolved oxygen values. Estimating the physicochemical properties of the water via remote sensing proved feasible, particularly in the dry season when there is less cloud cover.

scholarly journals Monitoring the Water Quality of Small Water Bodies Using High-Resolution Remote Sensing Data

2019 ◽  
Vol 8 (12) ◽  
pp. 553 ◽  
Author(s):  
Zehra Yigit Avdan ◽  
Gordana Kaplan ◽  
Serdar Goncu ◽  
Ugur Avdan
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
High Resolution ◽  
Quality Parameters ◽  
Water Bodies ◽  
Water Quality Parameters ◽  
Water Turbidity ◽  
Small Water ◽  
Red Edge ◽  
Small Water Bodies

Remotely sensed data can reinforce the abilities of water resources researchers and decision-makers to monitor water quality more effectively. In the past few decades, remote sensing techniques have been widely used to measure qualitative water quality parameters. However, the use of moderate resolution sensors may not meet the requirements for monitoring small water bodies. Water quality in a small dam was assessed using high-resolution satellite data from RapidEye and in situ measurements collected a few days apart. The satellite carries a five-band multispectral optical imager with a ground sampling distance of 5 m at its nadir and a swath width of 80 km. Several different algorithms were evaluated using Pearson correlation coefficients for electrical conductivity (EC), total dissolved soils (TDS), water transparency, water turbidity, depth, suspended particular matter (SPM), and chlorophyll-a. The results indicate strong correlation between the investigated parameters and RapidEye reflectance, especially in the red and red-edge portion with highest correlation between red-edge band and water turbidity (r2 = 0.92). Two of the investigated indices showed good correlation in almost all of the water quality parameters with correlation higher than 0.80. The findings of this study emphasize the use of both high-resolution remote sensing imagery and red-edge portion of the electromagnetic spectrum for monitoring several water quality parameters in small water areas.

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.

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