A Study on Algae Bloom Pigment in the Eutrophic Lake Using Bio-Optical Modelling: Hyperspectral Remote Sensing Approach

Prasanth, B R Vishnu; Sivakumar, R.; Ramaraj, M.

doi:10.1007/s00128-022-03511-9

Cited by 4 publications

(1 citation statement)

References 18 publications

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“…For many decades, Chl-a measurements in lakes have been a considerable issue since traditional sampling is a time-consuming and labor-intensive process and in situ data cannot represent the entire lake. Satellite remote sensing is a helpful instrument for monitoring inland waters with several benefits [17], including its low cost and continuous monitoring. Remote sensing can make up for the deficiencies of traditional sampling.…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing of Chlorophyll-a in Xinkai Lake Using Machine Learning and GF-6 WFV Images

Tao

et al. 2022

Remote Sensing

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Lake ecosystem eutrophication is a crucial water quality issue that can be efficiently monitored with remote sensing. GF-6 WFV with a high spatial and temporal resolution provides a comprehensive record of the dynamic changes in water quality parameters in a lake. In this study, based on GF-6 WFV images and the field sampling data of Xingkai Lake from 2020 to 2021, the accuracy of three machine learning models (RF: random forest; SVR: support vector regression; and BPNN: back propagation neural network) was compared by considering 11 combinations of surface reflectance in different wavebands as input variables for machine learning. We mapped the spatiotemporal variations of Chl-a concentrations in Xingkai Lake from 20192021 and integrated machine learning algorithms to demonstrate that RF obtained a better degree of derived-fitting (Calibration: N = 82, RMSE = 0.82 μg/L, MAE = 0.57 μg/L, slope = 0.94, and R2 = 0.98; Validation: N = 40, RMSE = 2.12 μg/L, MAE = 1.58 μg/L, slope = 0.91, R2 = 0.89, and RPD = 2.98). The interannual variation from 2019 to 2021 showed that the Chl-a concentration in Xingkai Lake was low from June to July, while maximum values were observed from October to November, thus showing significant seasonal differences. Spatial distribution showed that Chl-a concentrations were higher in Xiao Xingkai Lake than in Da Xingkai Lake. Nutrient inputs (N, P) and other environmental factors such as high temperature could have an impact on the spatial and temporal distribution characteristics of Chl-a, therefore, combining GF-6 WFV satellite images with RF could realize large-scale monitoring and be more effective. Our results showed that remote-sensing-based machine learning algorithms provided an effective method to monitor lake eutrophication as well as technical support and methodological reference for inland lake water quality parameter inversion.

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Section: Introductionmentioning

confidence: 99%

Remote Sensing of Chlorophyll-a in Xinkai Lake Using Machine Learning and GF-6 WFV Images

Tao

et al. 2022

Remote Sensing

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Remote Sensing and Nonlinear Auto-regressive Neural Network (NARNET) Based Surface Water Chemical Quality Study: A Spatio-Temporal Hybrid Novel Technique (STHNT)

Ramaraj

Sivakumar

2022

Bull Environ Contam Toxicol

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Spatio-temporal assessment and monitoring of chlorophyll-a in lake water through geo-informatics algorithm approach (GAA)

Rajkumar

Sivakumar²

2022

Arab J Geosci

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A Study on Algae Bloom Pigment in the Eutrophic Lake Using Bio-Optical Modelling: Hyperspectral Remote Sensing Approach

Cited by 4 publications

References 18 publications

Remote Sensing of Chlorophyll-a in Xinkai Lake Using Machine Learning and GF-6 WFV Images

Remote Sensing of Chlorophyll-a in Xinkai Lake Using Machine Learning and GF-6 WFV Images

Remote Sensing and Nonlinear Auto-regressive Neural Network (NARNET) Based Surface Water Chemical Quality Study: A Spatio-Temporal Hybrid Novel Technique (STHNT)

Spatio-temporal assessment and monitoring of chlorophyll-a in lake water through geo-informatics algorithm approach (GAA)

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