首页> 外文期刊>Agriculture, Ecosystems & Environment: An International Journal for Scientific Research on the Relationship of Agriculture and Food Production to the Biosphere >Modeling gross primary productivity for winter wheat-maize double cropping system using MODIS time series and CO sub(2) eddy flux tower data
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Modeling gross primary productivity for winter wheat-maize double cropping system using MODIS time series and CO sub(2) eddy flux tower data

机译:利用MODIS时间序列和CO sub(2)涡流塔数据模拟冬小麦-玉米双作系统的总初级生产力

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Accurate and spatially explicit monitoring of gross primary productivity of agricultural ecosystems at a large scale is of great significance to assessment of crop conditions and agricultural production, and is necessary for understanding the carbon balance of the terrestrial biosphere. Identifying crop intensity (including multiple cropping and crop calendar) dynamics and assigning appropriate light use efficiency to C3 and C4 crops could substantially improve our ability to model and evaluate the seasonal dynamics of carbon flux in intensified agricultural ecosystems. In this paper, we have analyzed temporal dynamics of vegetation indices and phenological characteristics in the winter-wheat and maize double cropping system using multi-year satellite images from the moderate resolution imaging spectral radiometer (MODIS) and in situ observation of key crop phenological transition dates. The multiple cropping and crop calendar information were incorporated into simulations of the satellite-based vegetation photosynthesis model (VPM). Canopy-level maximum light use efficiency, a key parameter in the satellite-based VPM model, was estimated for both winter wheat (C3) and maize (C4) based on the observed CO sub(2) flux data from an eddy flux tower site in a winter wheat-maize double cropping agro-ecosystem in the Huang-Huai-Hai plain, China. The seasonal dynamics of GPP predicted by the VPM model agreed well with estimated GPP from eddy flux tower data. These results demonstrate the potential of the satellite-driven VPM model for scaling-up GPP estimation of intensified agricultural ecosystems, which is relevant to food production and security.
机译:大规模准确,空间明确地监测农业生态系统的总初级生产力,对评估作物状况和农业生产具有重要意义,对于理解陆地生物圈的碳平衡是必不可少的。确定作物强度(包括多种作物和作物日历)动态,并为C3和C4作物分配适当的光利用效率,可以大大提高我们建模和评估集约化农业生态系统中碳通量季节动态的能力。本文利用中分辨率成像光谱辐射计(MODIS)的多年卫星图像和关键作物物候过渡的原位观测分析了冬小麦和玉米双作系统中植被指数的时间动态和物候特征。日期。将多种作物和作物日历信息整合到基于卫星的植被光合作用模型(VPM)的模拟中。基于观测到的涡流塔站点的CO sub(2)流量数据,估算了冬小麦(C3)和玉米(C4)的冠层最大光利用效率(基于卫星的VPM模型中的关键参数)在中国黄淮海平原的冬小麦-玉米双作农业生态系统中。由VPM模型预测的GPP的季节动态与根据涡流塔数据估算的GPP吻合得很好。这些结果证明了卫星驱动的VPM模型在扩大集约化农业生态系统的GPP估算方面的潜力,这与粮食生产和安全有关。

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