• DocumentCode
    147685
  • Title

    A study of inversion modeling of water quality parameters in the dianchi lake using CCD1 data of HJ-1A satellite

  • Author

    Rong Yang ; Kun Yang ; Liang Hong ; Yan Yang

  • Author_Institution
    Sch. of tourism & Geogr. Sci., Yunnan Normal Univ., Kunming, China
  • fYear
    2014
  • fDate
    25-27 June 2014
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    Monitoring of water quality using remote sensing data can better reflect the distribution and changes of regional water quality in space and time, and is particularly suitable for rapid monitoring of a wide range of waters. In this paper, we research quantitative remote sensing methods for two water quality parameters of suspended sediment and chlorophyll a in Dianchi Lake by using CCD1 data of HJ-1A satellites. The experimental results show that: band2, band3 and band4 correlate bestwith suspended sediment concentrations, which is the best band combinations is (b2+b3)/(b2/b3) in building the inversion model of suspended sediment concentrations. For chlorophyll a, band3 and band4 have the best correlation, which is the best band combinations is (b4-b3)/(b4+b3) in building the inversion model of chlorophyll a concentrations. Finally, based on fitting trend analysis for selected band combinations, we built inversion model of water quality parameters about the Dianchi Lake.
  • Keywords
    lakes; remote sensing; water quality; CCD data; Dianchi lake; HJ-lA satellite; chlorophyll a; quantitative remote sensing methods; regional water quality; suspended sediment; suspended sediment concentrations; water quality monitoring; water quality parameters; Analytical models; Correlation; Lakes; Monitoring; Remote sensing; Satellites; Sediments; CCD1; Dianchi lake; chlorophyll a; suspended sediment;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Geoinformatics (GeoInformatics), 2014 22nd International Conference on
  • Conference_Location
    Kaohsiung
  • ISSN
    2161-024X
  • Type

    conf

  • DOI
    10.1109/GEOINFORMATICS.2014.6950798
  • Filename
    6950798