Determination of rice paddy parameters in the global gross primary production capacity estimation algorithm using 6 years of JP-MSE flux observation data

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Abstract

Gross primary production (GPP) capacity is defined as GPP under low stress, and the algorithm for its estimation was developed by Thanyapraneedkul <i>et al</i>. (2012) using a light-response curve. The idea behind this algorithm is that the light response curve under low stress is related to chlorophyll content. The parameter is estimated from a vegetation index derived from satellite observations of the green chlorophyll index (<i>CI</i><SUB><i>green</i></SUB>) for seven vegetation types, including rice paddy. These previous studies included 1 year of data for the flux site and MODIS reflectance data. Recently, long-term data have become publicly available for flux data covering a period of 6 years, and MODIS reflectance data covering a period of more than 16 years.<BR> This study determined the parameters in the GPP capacity estimation algorithm for rice paddies using 6 years of Mase paddy flux site data and clear daytime reflectance data observed using MODIS. The fitted parameter-related initial slopes of the light-photosynthesis curves for each year were identical within the fitting error. Using the averaged parameter-related initial slope over 6 years, we were able to determine a linear relationship between <i>CI</i><SUB><i>green</i></SUB> and the maximum photosynthesis rate at 2000 PAR (μmol m<SUP>−2</SUP> s<SUP>−1</SUP>), the slope of which was slightly higher than has been reported previously. Using the parameters for the period 2001-2006, we investigated how GPP capacity varied for irrigated rice paddy. The ratio of the average GPP capacity to the GPP after transplanting until harvesting was 0.91 for the period 2001 to 2006. This result shows that GPP capacity provides a useful first approximation of GPP for irrigated rice paddies as a framework of the global GPP estimation algorithm.

Journal

  • Journal of Agricultural Meteorology

    Journal of Agricultural Meteorology 73(3), 119-132, 2017

    The Society of Agricultural Meteorology of Japan

Codes

  • NII Article ID (NAID)
    130005797418
  • NII NACSIS-CAT ID (NCID)
    AA12492451
  • Text Lang
    ENG
  • ISSN
    0021-8588
  • NDL Article ID
    028349168
  • NDL Call No.
    Z78-A873
  • Data Source
    NDL  J-STAGE 
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