Mapping the chlorophyll‐<i>a</i> concentrations in hypereutrophic Krishnagiri Reservoir (India) using Landsat 8 Operational Land Imager

Arunbabu, Elangovan; Murali, Vidya

doi:10.1111/lre.12346

Cited by 5 publications

(2 citation statements)

References 27 publications

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“…These bands were all highly correlated (r > 0.80) with in situ chlorophylla values, which likely made it easier for this model to generalize a linear relationship between surface reflectance and chlorophyll-a measurements. The metrics of the calibrated C2 model outperform the metrics published in previous studies (0.74 ± 0.12) [91].…”

Section: Empirical Modelsmentioning

confidence: 44%

“…Performance for both empirical and ML models was assessed using the NSE coefficient, correlation coefficient (r), and normalized root mean square error (NRMSE). These measures were used to evaluate the performance of similar models worldwide [15,91,100,113]. The standard variation in the 50 ML model simulations was used to quantify errors in each performance metric.…”

Section: Model Performance Measuresmentioning

confidence: 99%

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Monitoring Water Quality Indicators over Matagorda Bay, Texas, Using Landsat-8

Bygate,

Ahmed

2024

Remote Sensing

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Remote sensing datasets offer a unique opportunity to observe spatial and temporal trends in water quality indicators (WQIs), such as chlorophyll-a, salinity, and turbidity, across various aquatic ecosystems. In this study, we used available in situ WQI measurements (chlorophyll-a: 17, salinity: 478, and turbidity: 173) along with Landsat-8 surface reflectance data to examine the capability of empirical and machine learning (ML) models in retrieving these indicators over Matagorda Bay, Texas, between 2014 and 2023. We employed 36 empirical models to retrieve chlorophyll-a (12 models), salinity (2 models), and turbidity (22 models) and 4 ML families—deep neural network (DNN), distributed random forest, gradient boosting machine, and generalized linear model—to retrieve salinity and turbidity. We used the Nash–Sutcliffe efficiency coefficient (NSE), correlation coefficient (r), and normalized root mean square error (NRMSE) to assess the performance of empirical and ML models. The results indicate that (1) the empirical models displayed minimal effectiveness when applied over Matagorda Bay without calibration; (2) once calibrated over Matagorda Bay, the performance of the empirical models experienced significant improvements (chlorophyll-a—NRMSE: 0.91 ± 0.03, r: 0.94 ± 0.04, NSE: 0.89 ± 0.06; salinity—NRMSE: 0.24 ± 0, r: 0.24 ± 0, NSE: 0.06 ± 0; turbidity—NRMSE: 0.15 ± 0.10, r: 0.13 ± 0.09, NSE: 0.03 ± 0.03); (3) ML models outperformed calibrated empirical models when used to retrieve turbidity and salinity, and (4) the DNN family outperformed all other ML families when used to retrieve salinity (NRMSE: 0.87 ± 0.09, r: 0.49 ± 0.09, NSE: 0.23 ± 0.12) and turbidity (NRMSE: 0.63± 0.11, r: 0.79 ± 0.11, NSE: 0.60 ± 0.20). The developed approach provides a reference context, a structured framework, and valuable insights for using empirical and ML models and Landsat-8 data to retrieve WQIs over aquatic ecosystems. The modeled WQI data could be used to expand the footprint of in situ observations and improve current efforts to conserve, enhance, and restore important habitats in aquatic ecosystems.

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Section: Empirical Modelsmentioning

confidence: 44%

Section: Model Performance Measuresmentioning

confidence: 99%

Monitoring Water Quality Indicators over Matagorda Bay, Texas, Using Landsat-8

Bygate,

Ahmed

2024

Remote Sensing

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Phosphorus fractions in the sediment of a tropical reservoir, India: Implications for pollution source identification and eutrophication

Saha

Jesna

Ramya

et al. 2021

Environ Geochem Health

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Forecasting water quality using seasonal ARIMA model by integrating in-situ measurements and remote sensing techniques in Krishnagiri reservoir, India

Wahid

Arunbabu

2022

Water Practice and Technology

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The Krishnagiri reservoir is the main source of irrigation in Tamil Nadu, India. It has been reported to be hypereutrophic for over a decade with sediment and nutrient load sources responsible for the degradation of water quality. Remotely sensed satellite imagery analysis plays a significant role in assessing the water quality for developing a management strategy for reservoirs. The present study is an attempt to demonstrate the improvement in the chlorophyll-a (chl-a) estimation in the Krishnagiri reservoir by integrating remote sensing and in-situ measurements. Multiple regression equations were developed with the reflectance of Green, Red, NIR and SWIR1 bands of the Operational Land Imager (OLI) sensor of Landsat 8 satellite yielded the coefficient of determination for chlorophyll-a (chl-a) as 0.812, Total Dissolved Solids (TDS) as 0.945 and Electrical Conductivity (EC) as 0.960 respectively. The developed regression model was further utilised to forecast chl-a and EC of the reservoir through the Seasonal Auto Regressive Integrated Moving Average (SARIMA) model. It is found that chl-a prediction showed that the reservoir continued to be hypereutrophic and EC significantly changed from a class C3 (high salinity) to class C4 (very high salinity). These results are alarming and an immediate reduction of the external load from the catchment through effective watershed management programs should be implemented.

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Mapping the chlorophyll‐a concentrations in hypereutrophic Krishnagiri Reservoir (India) using Landsat 8 Operational Land Imager

Cited by 5 publications

References 27 publications

Monitoring Water Quality Indicators over Matagorda Bay, Texas, Using Landsat-8

Monitoring Water Quality Indicators over Matagorda Bay, Texas, Using Landsat-8

Phosphorus fractions in the sediment of a tropical reservoir, India: Implications for pollution source identification and eutrophication

Forecasting water quality using seasonal ARIMA model by integrating in-situ measurements and remote sensing techniques in Krishnagiri reservoir, India

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