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Estimating Secchi depth by simulated HSI of HJ-1A from in situ hyperspectral data: A case study in Sishili Bay, China
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Accession number:       
20131016080649
        Title:        Estimating Secchi depth by simulated HSI of HJ-1A from in situ hyperspectral data: A case study in Sishili Bay, China
        Authors:         Yu, Dingfeng1, 2 ; Xing, Qianguo1 ; Shi, Ping1
        Author affiliation:        1 Key Laboratory of Coastal Zone Environmental Process, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai, China
                2 University of Chinese Academy of Sciences, Beijing, China
        Corresponding author:         Yu, D. (dfYu@yic.ac.cn)
        Source title:        Proceedings of International Conference on Computer Vision in Remote Sensing, CVRS 2012
        Abbreviated source title:        Proc. Int. Conf. Comput. Vis. Remote Sens., CVRS
        Monograph title:        Proceedings of International Conference on Computer Vision in Remote Sensing, CVRS 2012
        Issue date:        2012
        Publication year:        2012
        Pages:        291-295
        Article number:        6421277
        Language:        English
        ISBN-13:         9781467312738
        Document type:        Conference article (CA)
        Conference name:        2012 International Conference on Computer Vision in Remote Sensing, CVRS 2012
        Conference date:        December 16, 2012 - December 18, 2012
        Conference location:        Xiamen, China
        Conference code:         95796
        Sponsor:        Xiamen University; National University of Defense Technology
        Publisher:        IEEE Computer Society, 2001 L Street N.W., Suite 700, Washington, DC 20036-4928, United States
        Abstract:        We study the use of simulated Hyper-Spectral Imaging sensor (HSI) on Environmental Satellite 1A (HJ-1A) satellite remote sensing data for estimating Secchi depth of coastal waters. Field data such as Secchi depth of the Sishili Bay in Yantai's coastal waters were collected, meanwhile, hyperspectral remote sensing data were measured with Ocean Optics USB4000 spectrometer during two cruises carried out on 22nd and 23rd June 2009. The coastal water-leaving reflectance of HSI was simulated from in situ hyperspectral remote sensing spectrum with 0.19 nm spectral resolution. According to the spectral simulation, characteristics bands of HSI were identified for Secchi depth estimation model. On the basis of correlation analysis between Secchi depth and simulation spectra, a single band model for retrieving Secchi depth was established. Moreover, the relationship between Secchi depth and all the band combinations (band addition, band subtraction, and band ratio) were analyzed, and then a linear regression model of Secchi depth using simulated HSI was developed, which performed better than other models in Sishili Bay, with a mean relative error (RE) of 5.2% and relative mean square error (RMSE) of 0.28 m. The result indicates that the band ratio model of Rrs(508.42)/Rrs(513.56) of HSI could be used to estimate Secchi depth in coastal waters. © 2012 IEEE.
        Number of references:        18
        Main heading:         Computer simulation
        Controlled terms:         Estimation  -  Linear regression  -  Remote sensing  -  Spectroscopy
        Uncontrolled terms:         Coastal waters  -  HSI  -  HyperSpectral  -  Secchi depth  -  Sishili bays
        Classification code:         723.5 Computer Applications -  731.1 Control Systems -  801 Chemistry -  921 Mathematics -  922.2 Mathematical Statistics
        DOI:        10.1109/CVRS.2012.6421277
        Database:        Compendex
                Compilation and indexing terms, © 2013 Elsevier Inc.
2Â¥2013-04-01 10:29:14
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2Â¥: Originally posted by »ìãçǧÇï at 2013-04-01 10:29:14
Accession number:       
20131016080649
        Title:        Estimating Secchi depth by simulated HSI of HJ-1A from in situ hyperspectral data: A case study in Sishili Bay, China
        Authors:         Yu, Dingfeng1, 2 ; Xing ...

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