scholarly journals Retrieval of three kinds of representative water quality parameters of Lake Taihu from hyperspectral remote sensing data

2009 ◽  
Vol 21 (2) ◽  
pp. 182-192 ◽  
Author(s):  
ZHANG Bing ◽  
◽  
SHEN Qian ◽  
LI Junsheng ◽  
ZHANG Hao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Lake Taihu ◽  
Remote Sensing Data ◽  
Quality Parameters ◽  
Hyperspectral Remote Sensing ◽  
Water Quality Parameters ◽  
Sensing Data

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 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.

Retrieval of water quality parameters by hyperspectral remote sensing in Lake TaiHu, China

2005 ◽  
Author(s):  
Dingtian Yang ◽  
Delu Pan ◽  
Xiaoyu Zhang ◽  
Xiaofeng Zhang ◽  
Xianqiang He ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Lake Taihu ◽  
Quality Parameters ◽  
Hyperspectral Remote Sensing ◽  
Water Quality Parameters

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 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.

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