Next Article in Journal
Imaging of Subsurface Corrosion Using Gradient-Field Pulsed Eddy Current Probes with Uniform Field Excitation
Next Article in Special Issue
Progressive Line Processing of Kernel RX Anomaly Detection Algorithm for Hyperspectral Imagery
Previous Article in Journal
A Method to Reduce Non-Nominal Troposphere Error
Previous Article in Special Issue
Grading of Chinese Cantonese Sausage Using Hyperspectral Imaging Combined with Chemometric Methods
Article Menu
Issue 8 (August) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(8), 1746; https://doi.org/10.3390/s17081746

Assessment of Chlorophyll-a Algorithms Considering Different Trophic Statuses and Optimal Bands

1
Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan
2
Faculty of Engineering, Alexandria University, Lotfy El-Sied St. Off Gamal Abd El-Naser-Alexandria, Alexandria 11432, Egypt
3
Faculty of Urban Innovation, Yokohama National University, Tokiwadai 79-5, Hodogaya, Yokohama, Kanagawa 240-8501, Japan
4
Graduate School of Interdisciplinary Research, University of Yamanashi, 4-4-37 Takeda, Kofu, Yamanashi 400-8510, Japan
*
Author to whom correspondence should be addressed.
Received: 13 June 2017 / Revised: 17 July 2017 / Accepted: 26 July 2017 / Published: 31 July 2017
(This article belongs to the Special Issue Analysis of Multispectral and Hyperspectral Data)
View Full-Text   |   Download PDF [4975 KB, uploaded 31 July 2017]   |  

Abstract

Numerous algorithms have been proposed to retrieve chlorophyll-a concentrations in Case 2 waters; however, the retrieval accuracy is far from satisfactory. In this research, seven algorithms are assessed with different band combinations of multispectral and hyperspectral bands using linear (LN), quadratic polynomial (QP) and power (PW) regression approaches, resulting in altogether 43 algorithmic combinations. These algorithms are evaluated by using simulated and measured datasets to understand the strengths and limitations of these algorithms. Two simulated datasets comprising 500,000 reflectance spectra each, both based on wide ranges of inherent optical properties (IOPs), are generated for the calibration and validation stages. Results reveal that the regression approach (i.e., LN, QP, and PW) has more influence on the simulated dataset than on the measured one. The algorithms that incorporated linear regression provide the highest retrieval accuracy for the simulated dataset. Results from simulated datasets reveal that the 3-band (3b) algorithm that incorporate 665-nm and 680-nm bands and band tuning selection approach outperformed other algorithms with root mean square error (RMSE) of 15.87 mg·m−3, 16.25 mg·m−3, and 19.05 mg·m−3, respectively. The spatial distribution of the best performing algorithms, for various combinations of chlorophyll-a (Chla) and non-algal particles (NAP) concentrations, show that the 3b_tuning_QP and 3b_680_QP outperform other algorithms in terms of minimum RMSE frequency of 33.19% and 60.52%, respectively. However, the two algorithms failed to accurately retrieve Chla for many combinations of Chla and NAP, particularly for low Chla and NAP concentrations. In addition, the spatial distribution emphasizes that no single algorithm can provide outstanding accuracy for Chla retrieval and that multi-algorithms should be included to reduce the error. Comparing the results of the measured and simulated datasets reveal that the algorithms that incorporate the 665-nm band outperform other algorithms for measured dataset (RMSE = 36.84 mg·m−3), while algorithms that incorporate the band tuning approach provide the highest retrieval accuracy for the simulated dataset (RMSE = 25.05 mg·m−3). View Full-Text
Keywords: bio-optical model; Red-NIR algorithm; Case 2 waters; inland lakes; Tokyo Bay; water quality bio-optical model; Red-NIR algorithm; Case 2 waters; inland lakes; Tokyo Bay; water quality
Figures

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Supplementary material

SciFeed

Share & Cite This Article

MDPI and ACS Style

Salem, S.I.; Higa, H.; Kim, H.; Kobayashi, H.; Oki, K.; Oki, T. Assessment of Chlorophyll-a Algorithms Considering Different Trophic Statuses and Optimal Bands. Sensors 2017, 17, 1746.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top