Remote Sensing of Turbidity in the Tennessee River Using Landsat 8 Satellite

Hossain, A. K. M. Azad; Mathias, Caleb; Blanton, Richard E.

doi:10.3390/rs13183785

Cited by 38 publications

(18 citation statements)

References 53 publications

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“…As shown in table 1&2, W QEI T and W QEI C performs better than most existing methods [53][54][55][56][57][58][59] &. [49][50][51][52] Even with limited data, our proposed neural network accurately detects water salinity and turbidity. Further research is needed to stabilize these results: the neural network proposed here primarily functions as validation of the feasibility of this idea.…”

Section: Results and Conclusionmentioning

confidence: 95%

“…And after that the next challenge is understanding of multispectral data. Index creation [49][50][51][52][53][54][55][56][57][58][59] is one way where people have done analysis of multispectral data from satellites using remote sensing. Index creation needs expertise in both the multispectral imagery from satellite and chemical properties of object of interest.…”

Section: Limitationsmentioning

confidence: 99%

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In-situ Water quality monitoring in Oil and Gas operations

Kumar¹,

Kou²,

Hill³

et al. 2023

Preprint

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From agriculture to mining, to energy, surface water quality monitoring is an essential task. As oil and gas operators work to reduce the consumption of freshwater, it is increasingly important to actively manage fresh and non-fresh water resources over the long term. For large-scale monitoring, manual sampling at many sites has become too time-consuming and unsustainable, given the sheer number of dispersed ponds, small lakes, playas, and wetlands over a large area. Therefore, satellite-based environmental monitoring presents great potential. Many existing satellite-based monitoring studies utilize index-based methods to monitor large water bodies such as rivers and oceans. However, these existing methods fail when monitoring small ponds-the reflectance signal received from small water bodies is too weak to detect. To address this challenge, we propose a new Water Quality Enhanced Index (WQEI) Model, which is designed to enable users to determine contamination levels in water bodies with weak reflectance patterns. Our results show that 1) WQEI is a good indicator of water turbidity validated with 1200 water samples measured in the laboratory, and 2) by applying our method to commonly available satellite data (e.g. LandSat8), one can achieve high accuracy water quality monitoring efficiently in large regions. This provides a tool for operators to optimize the quality of water stored within surface storage ponds and increasing the readiness and availability of non-fresh water.

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Section: Results and Conclusionmentioning

confidence: 95%

Section: Limitationsmentioning

confidence: 99%

In-situ Water quality monitoring in Oil and Gas operations

Kumar¹,

Kou²,

Hill³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Since the 1960s, remote sensing techniques have been used to monitor aquatic environments by analyzing ocean colors under the assumption that Chl-a (a quantified proxy for phytoplankton biomass) and surface temperature can be estimated remotely [5,10]. Based on this, many researchers have used satellite sensors to evaluate WQPs with optically active parameters, such as total suspended matter, Chl-a concentration, turbidity, phytoplankton pigments, and color-dissolved organic matter (CDOM) [11][12][13]. However, estimating TN, TP, and COD concentrations in inland waters presents a great challenge.…”

Section: Introductionmentioning

confidence: 99%

Retrieving Inland Reservoir Water Quality Parameters Using Landsat 8-9 OLI and Sentinel-2 MSI Sensors with Empirical Multivariate Regression

Meng

Zhang

Zhang³

2022

IJERPH

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Improving water quality is one of the top priorities in the global agenda endorsed by the United Nations. To ensure the achievement of this goal, governments have developed plans to continuously monitor the status of inland waters. Remote sensing provides a low-cost, high-frequency, and practical complement to monitoring systems that can cover a large area. However, it is crucial to evaluate the suitability of sensors for retrieving water quality parameters (WQPs), owing to differences in spatial and spectral sampling from different satellites. Taking Shanmei Reservoir in Fuzhou City, Fujian Province as a case study, this study collected and sorted the water quality data measured at the site in 2020 to 2022 and Landsat 8-9 OLI and Sentinel-2 MSI images, simulated the chlorophyll-a (Chl-a) concentration, algae density, and turbidity using empirical multivariate regression, and explored the relationship between different WQPs using correlation analysis and principal component analysis (PCA). The results showed that the fitting effect of Landsat OLI data was better than that of the Sentinel-2 MSI data. The coefficient of determination (R2) values of Chl-a, algal density, and turbidity simulated by Landsat OLI data were 0.70, 0.81, and 0.80, respectively. Furthermore, the parameters of its validation equation were also smaller than those of Sentinel MSI data. The spatial distribution of three key WQPs retrieved from Landsat OLI data shows their values were generally low, with the mean values of the Chl-a concentration, algal density, and turbidity being 4.25 μg/L, 4.11 × 106 cells/L, and 1.86 NTU, respectively. However, from the end of February 2022, the values of the Chl-a concentration and algae density in the reservoir gradually increase, and the risk of water eutrophication also increases. Therefore, it is still necessary to pay continuous attention and formulate corresponding water quality management measures. The correlation analysis shows that the three key WQPs in this study have a high correlation with pH, water temperature (WT), and dissolved oxygen (DO). The results of PCA showed that pH, DO, Chl-a concentration, WT, TN, and CODMn were dominant in PC1, explaining 35.57% of the total variation, and conductivity, algal density, and WT were dominant in PC2, explaining 13.34% of the total variation. Therefore, the water quality of the Shanmei Reservoir can be better evaluated by measuring pH, conductivity, and WT at the monitoring station, or by establishing the regression fitting equations between DO, CODMn, and TN. The regression algorithm used in this study can identify the most important water quality features in the Shanmei Reservoir, which can be used to monitor the nutritional status of the reservoir and provide a reference for other similar inland water bodies.

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“…While algal bloom last from a few days to months and cover an area from a few square meters to several square kilometers, technical and nical constraints can limit the applicability of eld and laboratory measurements for monitoring spatial and temporal changes of algae, especially in large water bodies (Hafeez et al, 2019). In contrast, satellite remote sensing can simultaneously cover a vast area and provide real-time data at a reasonable cost (Hossain et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Despite numerous studies on the estimation of Chl-a concentration in oceans and estuaries by remote sensing, inland water bodies have received less attention (Topp et al, 2020). Most researchers have found a high correlation between the eld data of Chl-a and the ones obtained by remote sensing in lakes, dam reservoirs, and coastal areas (Hossain et al, 2021). However, applying remote sensing still faces several challenges such as the effect of weather and air conditions (e.g cloud cover and air pollution) on the quality of received images, and limited depth of measurement (Hafeez et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Performance of Sentinel-2 and Landsat-8 satellites in estimating Chlorophyll-a concentration in a shallow freshwater lake

Karimi

Hashemi

Aghighi

2022

Preprint

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Remote sensing for monitoring chlorophyll-a (Chl-a) concentration, as a eutrophication indicator, is known as a proper and cost-effective way due to its high temporal and spatial coverage and providing historical data. However, its application in shallow small water bodies is challenging due to its limited spatial and temporal resolutions and effect of depth on accuracy of retrieval models and estimated Chl-a concentration. In this study, the performance of Landsat-8 and Sentinel-2 satellites was investigated for estimating Chl-a concentration in Chitgar Lake in Tehran. The models were constructed using 73 and 122 common data pairs between field observations and Landsat-8 and Sentinel-2 images, respectively. After preprocessing and processing of the satellites’ images, the two bands (2BAND) algorithm (green and red band ratio, R2 = 0.8, RMSE = 1.12 µg.L− 1 and NRMSE = 12.4%) was recognized as the best Landsat-8 based model, and both Normalized Difference Chlorophyll Index (NDCI) algorithm (red and red edge bands, R2 = 0.82, RMSE = 1.29 µg.L− 1, NRMSE = 7.8%) and 2BAND algorithm (red and red edge band ratio, R2 = 0.81, RMSE = 1.28 µg.L− 1 and NRMSE = 7.7%) were the best models for Sentinel-2, all with power = 0.8. Overall, both satellites demonstrated an appropriate performance and can estimate Chl-a concentration with proper accuracy in Chitgar lake as a shallow freshwater body.

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Remote Sensing of Turbidity in the Tennessee River Using Landsat 8 Satellite

Cited by 38 publications

References 53 publications

In-situ Water quality monitoring in Oil and Gas operations

In-situ Water quality monitoring in Oil and Gas operations

Retrieving Inland Reservoir Water Quality Parameters Using Landsat 8-9 OLI and Sentinel-2 MSI Sensors with Empirical Multivariate Regression

Performance of Sentinel-2 and Landsat-8 satellites in estimating Chlorophyll-a concentration in a shallow freshwater lake

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