基于遥感和美国碳通量观测数据的GPP模型比较研究

基于遥感和碳通量观测数据,本文采用VPM、EC-LUE、TG、GR、VI和MOD17六个模型估算了五种主要植被类型站点尺度的总初级生产力(GPP)。利用线性相关和定量分析方法评价并比较了上述模型在不同时间尺度上(8天、生长季和年际)的GPP模拟精度。结果表明:1)EC-LUE和VPM模型总体估算精度最高(R20.78);2)森林生态系统中,GPP估算值和实测值在季节和年累积总量上相对误差较小,而在草地和农田系统中,相对误差较大;3)GR、VI和TG模型在森林生态系统GPP估算中模拟精度较高,因其在形式上相对简单,需要的参数和输入数据相对较少,因而适用于大尺度的森林生态系统GPP估算。

Comparison of Multiple GPP Models Using Remote Sensing and American Carbon Flux Data

Based on remote sensing and carbon flux measurements, GPP models including Vegetation Photosynthesis Model ( VPM) , Eddy Covariance Light Use Efficiency model ( EC-LUE) , Temperature and Greenness ( TG) model, Greenness and Radiation ( GR) model, Vegetation Index ( VI) model and MOD17 are used to estimate the gross primary productivity ( GPP) across five main vegeta-tion biomes. Methods of the linear correlation and quantitative analysis are used to evaluate and compare the simulation accuracy of the above six models at different time scales (eg, eight-day, growing season and annual). Our results show that: (1) Overall, EC-LUE and VPM algorithms generally have the highest accuracies (R2 >0. 78) in GPP estimation;(2) In forest ecosystems, the rela-tive error between sums of GPP values from models and field measurements is small for both growing seasons and years. However, for grassland and cropland ecosystems, the relative error is larger;(3) GR, VI and TG models show high accuracy in forest ecosystem GPP estimation, which indicates that they' re suitable for large-scale forest ecosystem GPP estimation due to relative simplicity in form, and required less parameters and input data.