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Article

Spectral Feature Selection Optimization for Water Quality Estimation

1
Department of Geomatics, National Cheng Kung University, Tainan City 701, Taiwan
2
Institute of Geography, Vietnam Academy of Science and Technology, 18 Hoang Quoc Viet, Vietnam
3
Department of Geomatics Engineering, Institut Teknologi Sepuluh Nopember, Jawa Timur 60111, Indonesia
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(1), 272; https://doi.org/10.3390/ijerph17010272
Received: 6 December 2019 / Revised: 26 December 2019 / Accepted: 27 December 2019 / Published: 30 December 2019
(This article belongs to the Special Issue Spatio-Temporal Environmental Monitoring and Social Sensing)
The spatial heterogeneity and nonlinearity exhibited by bio-optical relationships in turbid inland waters complicate the retrieval of chlorophyll-a (Chl-a) concentration from multispectral satellite images. Most studies achieved satisfactory Chl-a estimation and focused solely on the spectral regions from near-infrared (NIR) to red spectral bands. However, the optical complexity of turbid waters may vary with locations and seasons, which renders the selection of spectral bands challenging. Accordingly, this study proposes an optimization process utilizing available spectral models to achieve optimal Chl-a retrieval. The method begins with the generation of a set of feature candidates, followed by candidate selection and optimization. Each candidate links to a Chl-a estimation model, including two-band, three-band, and normalized different chlorophyll index models. Moreover, a set of selected candidates using available spectral bands implies an optimal composition of estimation models, which results in an optimal Chl-a estimation. Remote sensing images and in situ Chl-a measurements in Lake Kasumigaura, Japan, are analyzed quantitatively and qualitatively to evaluate the proposed method. Results indicate that the model outperforms related Chl-a estimation models. The root-mean-squared errors of the Chl-a concentration obtained by the resulting model (OptiM-3) improve from 11.95 mg · m 3 to 6.37 mg · m 3 , and the Pearson’s correlation coefficients between the predicted and in situ Chl- a improve from 0.56 to 0.89. View Full-Text
Keywords: water quality mapping; Chl-a estimation model; multispectral satellite images; chlorophyll-a; inland turbid water water quality mapping; Chl-a estimation model; multispectral satellite images; chlorophyll-a; inland turbid water
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MDPI and ACS Style

Van Nguyen, M.; Lin, C.-H.; Chu, H.-J.; Muhamad Jaelani, L.; Aldila Syariz, M. Spectral Feature Selection Optimization for Water Quality Estimation. Int. J. Environ. Res. Public Health 2020, 17, 272. https://doi.org/10.3390/ijerph17010272

AMA Style

Van Nguyen M, Lin C-H, Chu H-J, Muhamad Jaelani L, Aldila Syariz M. Spectral Feature Selection Optimization for Water Quality Estimation. International Journal of Environmental Research and Public Health. 2020; 17(1):272. https://doi.org/10.3390/ijerph17010272

Chicago/Turabian Style

Van Nguyen, Manh, Chao-Hung Lin, Hone-Jay Chu, Lalu Muhamad Jaelani, and Muhammad Aldila Syariz. 2020. "Spectral Feature Selection Optimization for Water Quality Estimation" International Journal of Environmental Research and Public Health 17, no. 1: 272. https://doi.org/10.3390/ijerph17010272

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