Spectral Feature Selection Optimization for Water Quality Estimation

Manh Van Nguyen, Chao-Hung Lin, Hone-Jay Chu, Lalu Muhamad Jaelani and Muhammad Aldila Syariz
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Manh Van Nguyen: Department of Geomatics, National Cheng Kung University, Tainan City 701, Taiwan
Chao-Hung Lin: Department of Geomatics, National Cheng Kung University, Tainan City 701, Taiwan
Hone-Jay Chu: Department of Geomatics, National Cheng Kung University, Tainan City 701, Taiwan
Lalu Muhamad Jaelani: Department of Geomatics Engineering, Institut Teknologi Sepuluh Nopember, Jawa Timur 60111, Indonesia
Muhammad Aldila Syariz: Department of Geomatics, National Cheng Kung University, Tainan City 701, Taiwan

IJERPH, 2019, vol. 17, issue 1, 1-14

Abstract: 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.

Keywords: water quality mapping; Chl-a estimation model; multispectral satellite images; chlorophyll-a; inland turbid water (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
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