Estimation of chlorophyll-a Concentration of lakes based on SVM algorithm and Landsat 8 OLI images

Environmental Science and Pollution Research International
Teng ZhangZhongjing Wang

Abstract

Chlorophyll-a (Chl-a) is the main component of phytoplankton and an important index of water quality. Pearson correlation analysis is conducted on measured Chl-a concentration and band reflectance to determine the sensitive bands or multiband combinations of the Chl-a to input to a support vector machine (SVM) model. An indicator β is defined to evaluate the model performance of fitting and prediction. The model performs well with the lowest β (decision coefficient, (R2) = 0.774; root mean square error (RMSE) = 22.636 μg/L) of the validation set. The model test results prove that the model performs well. We analyze the impact factors of the model. The seasonal factor affects the model performance significantly; thus, samples from different seasons should be combined to train the model and inverse the water quality. Noise points reduce the model accuracy significantly; therefore, obvious outliers must be excluded at first. Additionally, the sampling method affects model accuracy, and systematic sampling in the descending order of Chl-a concentration is recommended. The combination of SVM algorithm and remote sensing technology provides a convenient, scientific, and real-time method to monitor and control water quality.

References

Dec 16, 2006·Environmental Monitoring and Assessment·Jian Guo Jiang, Yun-Fen Shen
Aug 13, 2011·Environmental Science and Pollution Research International·Thomas HildebrandtJürgen W Einax
Sep 1, 2015·International Journal of Environmental Research and Public Health·Fernanda Sayuri Yoshino WatanabeLuiz Henrique da Silva Rotta
May 6, 2017·Environmental Pollution·Jechan LeeEilhann E Kwon
May 17, 2019·Environmental Science and Pollution Research International·Lei LiHui Wu

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Citations

Jun 21, 2021·Environmental Science and Pollution Research International·Huiling FuXuesong Wang

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