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随着遥感数据时空分辨率的提高,大范围实时监测总初级生产力GPP(Gross Primary Productivity)的变化成为可能。本研究收集了黑河流域阿柔冻融观测站的气象观测资料和MODIS数据,驱动VPM、TG、VI和EC-LUE4个模型估算了该站点的GPP,并应用涡动相关观测的GPP验证了模拟结果,并比较了这4个模型的模拟精度。结果表明:阿柔站2009年的涡动相关观测的GPP、NEE(Net Ecosystem Exchange)和ER(Ecosystem Respiration)分别为:804.2gC/m2/yr、129.6gC/m2/yr和673.6gC/m2/yr。该站点光合作用固定的碳有83.8%通过生态系统的呼吸作用释放到大气中。基于遥感的GPP模型能够很好地模拟高寒草甸的GPP,全年的判定系数在0.94以上,生长季的判定系数大于0.84。
With the improvement of spatio-temporal resolution of remote sensing data, it is possible to monitor the gross primary productivity (GPP) in a large area in real time. In this study, the meteorological data and MODIS data from the Adean Freeze-Thaw Observatory in the Heihe River Basin were collected. Four models of VPM, TG, VI and EC-LUE were driven to estimate the GPP of the site. The GPP of the eddy covariance was used to validate the simulation Results, and compared the simulation accuracy of these four models. The results show that the GPE, NEE (Net Ecosystem Exchange) and ER (Ecosystem Respiration) of A soft station in 2009 are: vortex-induced correlation of 804.2gC / m2 / yr, 129.6gC / m2 / yr and 673.6gC / m2 / yr. 83.8% of the photosynthesis-fixed carbon in this site is released into the atmosphere through the ecosystem’s respiration. GPP model based on remote sensing can well simulate the GPP of alpine meadow, the annual coefficient of determination is above 0.94, and the coefficient of determination of growing season is more than 0.84.