• DocumentCode
    255140
  • Title

    The Correction ground spectral model for estimating above-ground net primary productivity at the peak of growing season on Meadow Steppe, Hulunbeier, Inner Mongolia, China

  • Author

    Zhaoyan Diao ; Shihai Lv ; Chaoyang Feng ; Derong Su ; Liang Sun ; Xue Tian

  • Author_Institution
    Key Lab. of Silviculture & Conservation of Minist. of Educ., Beijing Forestry Univ., Beijing, China
  • fYear
    2014
  • fDate
    11-14 Aug. 2014
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    On the surface of hyperspectral data of remote sensing to estimate grassland biomass, the soil water content, organic matter content important impact on the model application. An ASD Fieldspec 3 spectroradiometer was used for spectral measurements of the Hulunbeier Meadow Steppe, Inner Mongolia, China, in late July, 2013. Ground spectral models were built to estimate the Above-ground Net Primary Productivity (ANPP) at the peak of the growing season from the Correction Normalized Difference Vegetation Index (CNDVI) measured in the field. The SPSS software were used to assess relationships between ANPP and CNDVI. Based on the coefficient of determination (R2), quadratic model (R2=0.808) and exponential model (R2=0.717) were batter than others. Error analysis shows that the linear equation has the biggest standard error of the prediction (SE = 82.42g/m2), the logarithmic curve equation has the smallest SE (SE=18.59g/m2). After considering all factors, a quadratic equation between ANPP and CNDVI (ANPP=3738.048 NDVIMODIS2-259.1588NDVIMODIS+1309.847, R2=0.808, SE=66.25g/m2, MEC=0.820, P <; 0.001) was selected and used for the study area at the peak of the growing season.
  • Keywords
    geophysical techniques; remote sensing; vegetation; AD 2013 07; ASD Fieldspec 3 spectroradiometer; China; Hulunbeier Meadow Steppe; Inner Mongolia; SPSS software; above-ground net primary productivity; correction ground spectral model; correction normalized difference vegetation index; grassland biomass; growing season; hyperspectral data surface; organic matter content; quadratic model; remote sensing; soil water content; Biological system modeling; Biomass; Indexes; Mathematical model; Remote sensing; Soil; Vegetation mapping; Correction Normalized Difference Vegetation Index (CNDVI); Meadow Steppe; ground spectral models;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Agro-geoinformatics (Agro-geoinformatics 2014), Third International Conference on
  • Conference_Location
    Beijing
  • Type

    conf

  • DOI
    10.1109/Agro-Geoinformatics.2014.6910596
  • Filename
    6910596