Chang Xiao Qing, Xing Yan Qiu, Wang Xin Hui, You Hao Tian, Xu Ke
Center for Forest Operations and Environment, Northeast Forestry University, Harbin 150040, China.
College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, Guangxi, China.
Ying Yong Sheng Tai Xue Bao. 2019 May;30(5):1599-1607. doi: 10.13287/j.1001-9332.201905.037.
With the flux data of ChinaFLUX and the concurrent satellite remote sensing data in Changbai Mountain, we recombined parameters of four models, i.e., vegetation photosynthesis model (VPM), eddy covariance-light utility efficiency model (EC-LUE), terrestrial ecosystem model (TEM) and Carnegie-Ames-Stanford approach model (CASA) within 3PG model. The most suitable parameters of 3PG model were determined by comparing the root mean square error, coefficient of determination and average error between measured and observed flux values. To improve its accuracy in estimating gross primary productivity (GPP) of broadleaved Korean pine forest in Changbai Mountain, the fitness of the optimal model was validated using the observed flux data. The results showed that when temperature, enhanced vegetation index, and surface water index were used to characterize the temperature limiting factor, photosynthetic active radiation absorption ratio and water limiting factor in the original model to estimate GPP of broadleaved Korean pine forest, the simulation results were the best. The precision of the optimized model (R=0.948, RMSE=0.035 mol·m·month) was better than that of the original model (R=0.854, RMSE=0.177 mol·m·month), which could effectively improve the phenomenon of obvious overestimation of the original model in the growing season. Results from the parameter sensitivity analysis showed that the uncertainty of GPP estimation was dominated by temperature, followed by enhanced vegetation index, photosynthetically active radiation and land surface water index, as well as their interactions.
利用中国生态系统研究网络（ChinaFLUX）的通量数据以及长白山同步卫星遥感数据，我们在3PG模型框架内重新组合了植被光合作用模型（VPM）、涡度相关-光能利用效率模型（EC-LUE）、陆地生态系统模型（TEM）和卡内基-艾姆斯-斯坦福方法模型（CASA）这四个模型的参数。通过比较实测通量值与观测通量值之间的均方根误差、决定系数和平均误差，确定了3PG模型的最优参数。为提高其对长白山阔叶红松林总初级生产力（GPP）的估算精度，利用观测通量数据对最优模型的适用性进行了验证。结果表明，当利用温度、增强植被指数和地表水指数来表征原模型中温度限制因子、光合有效辐射吸收比例和水分限制因子以估算阔叶红松林GPP时，模拟效果最佳。优化后模型的精度（R=0.948，RMSE=0.035 mol·m⁻²·月⁻¹）优于原模型（R=0.854，RMSE=0.177 mol·m⁻²·月⁻¹），能有效改善原模型在生长季明显高估的现象。参数敏感性分析结果表明，GPP估算的不确定性主要由温度主导，其次是增强植被指数、光合有效辐射和地表水分指数及其相互作用。
