日本リモートセンシング学会誌
Online ISSN : 1883-1184
Print ISSN : 0289-7911
論文
Determination of Parameters for Shrubs in the Global GrossPrimary Production Capacity Estimation Algorithm
Yukiko MINESHITAKanako MURAMATUNoriko SOYAMAJuthasinee THANYAPRANEEDKULMotomasa DAIGO
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2016 年 36 巻 3 号 p. 236-246

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Gross primary production (GPP) capacity is defined as GPP under low stress, and the algorithm for its estimation was developed by Thanyapraneedkul et al. (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 for five vegetation types: grass, needleleaf deciduous trees, needleleaf evergreen trees, broadleaf deciduous trees, and cropland (paddy fields). Global GPP capacity estimations require modifications to include additional vegetation types, such as closed and open shrubs, which account for approximately 13 % of global land cover.

In this study, the open and closed shrub parameters in the GPP capacity estimation algorithm were determined using AmeriFlux data and satellite data. The optimal parameter for maximum photosynthesis estimation at 2000 PAR (μmol m-2 s-1) of open shrubs was similar to that of grass, but for closed shrubs it differed. We concluded that grass and open shrubs could be combined into a single group, and plant functional types in this study and the prior study (Thanyapraneedkul, et al., 2012) could be divided into two categories: grass and woody plants. From this, estimation formula’s parameters were determined for the two categories. Additionally, seasonal changes in GPP capacity were investigated, based on AmeriFlux data and the MODIS GPP product (MOD17A2). GPP capacity and AmeriFlux GPP observations were nearly identical if the vegetation did not experience high stress levels. Also, our results indicated that GPP capacity reflected drought conditions.

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© 2016 The Remote Sensing Society of Japan
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