遥感GPP模型在高寒草甸的应用比较

摘要

Development of remote sensing makes it possible to estimate Gross Primary Productivity (GPP) regionally. In recent years, a lot of researches on GPP estimation based on remote sensing data were conducted. In this study, meteorological and moderate-resolution imaging spectroradiometer (MODIS) data at A'rou (AR) freeze/thaw observation station, which is located in upper stream of Heine River Basin, was collected to drive four remote-sensing based GPP models: Vegetation Photosynthesis Model (VPM), Temperature and Greenness model (TG), Vegetation Index model (VI) and Eddy Covariance-Light Use Efficiency model (EC-LUE). GPP observed by Eddy Covariance (EC) was used to validate the results from the four models. It is indicated that GPP, NEE and ER at AR station were 804.2 gC/m2/yr, 129.6 gC/m2/yr and 673.6 gC/m2/yr, respectively in 2009, indicating that 83.8% of carbon fixed by photosynthesis was released to atmosphere by ecosystem respiration. All the four models can predict GPP of alpine meadow very well. Determination coefficient between observed GPP and predicted GPP was larger than 0.94 in 2009, and was larger than 0.84 during growing season.%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-LUE 4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:804.2 gC/m2/yr、129.6 gC/m2/yr和673.6 gC/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.