Literature DB >> 23262320

Remote chlorophyll-a estimates for inland waters based on a cluster-based classification.

Kun Shi1, Yunmei Li, Lin Li, Heng Lu, Kaishan Song, Zhonghua Liu, Yifan Xu, Zuchuan Li.   

Abstract

Accurate estimates of chlorophyll-a concentration (Chl-a) from remotely sensed data for inland waters are challenging due to their optical complexity. In this study, a framework of Chl-a estimation is established for optically complex inland waters based on combination of water optical classification and two semi-empirical algorithms. Three spectrally distinct water types (Type I to Type III) are first identified using a clustering method performed on remote sensing reflectance (R(rs)) from datasets containing 231 samples from Lake Taihu, Lake Chaohu, Lake Dianchi, and Three Gorges Reservoir. The classification criteria for each optical water type are subsequently defined for MERIS images based on the spectral characteristics of the three water types. The criteria cluster every R(rs) spectrum into one of the three water types by comparing the values from band 7 (central band: 665 nm), band 8 (central band: 681.25 nm), and band 9 (central band: 708.75 nm) of MERIS images. Based on the water classification, the type-specific three-band algorithms (TBA) and type-specific advanced three-band algorithm (ATBA) are developed for each water type using the same datasets. By pre-classifying, errors are decreased for the two algorithms, with the mean absolute percent error (MAPE) of TBA decreasing from 36.5% to 23% for the calibration datasets, and from 40% to 28% for ATBA. The accuracy of the two algorithms for validation data indicates that optical classification eliminates the need to adjust the optimal locations of the three bands or to re-parameterize to estimate Chl-a for other waters. The classification criteria and the type-specific ATBA are additionally validated by two MERIS images. The framework of first classifying optical water types based on reflectance characteristics and subsequently developing type-specific algorithms for different water types is a valid scheme for reducing errors in Chl-a estimation for optically complex inland waters.
Copyright © 2012 Elsevier B.V. All rights reserved.

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Year:  2012        PMID: 23262320     DOI: 10.1016/j.scitotenv.2012.11.058

Source DB:  PubMed          Journal:  Sci Total Environ        ISSN: 0048-9697            Impact factor:   7.963


  1 in total

1.  Remote estimation of cyanobacterial blooms using the risky grade index (RGI) and coverage area index (CAI): a case study in the Three Gorges Reservoir, China.

Authors:  Botian Zhou; Mingsheng Shang; Guoyin Wang; Li Feng; Kun Shan; Xiangnan Liu; Ling Wu; Xuerui Zhang
Journal:  Environ Sci Pollut Res Int       Date:  2017-06-28       Impact factor: 4.223

  1 in total

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