Li Xue Jian, Mao Fang Jie, Du Hua Qiang, Zhou Guo Mo, Xu Xiao Jun, Li Ping Heng, Liu Yu Li, Cui Lu
Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang, Lin'an 311300, Zhejiang, China.
School of Environmental and Resources Science, Zhejiang A&F University, Lin'an 311300, Zhejiang, China.
Ying Yong Sheng Tai Xue Bao. 2016 Dec;27(12):3797-3806. doi: 10.13287/j.1001-9332.201612.005.
LAI is one of the most important observation data in the research of carbon cycle of forest ecosystem, and it is also an important parameter to drive process-based ecosystem model. The Moso bamboo forest (MBF) and Lei bamboo forest (LBF) were selected as the study targets. Firstly, the MODIS LAI time series data during 2014-2015 was assimilated with Dual Ensemble Kalman Filter method. Secondly, the high quality assimilated MBF LAI and LBF LAI were used as input dataset to drive BEPS model for simulating the gross primary productivity (GPP), net ecosystem exchange (NEE) and total ecosystem respiration (TER) of the two types of bamboo forest ecosystem, respectively. The modeled carbon fluxes were evaluated by the observed carbon fluxes data, and the effects of different quality LAI inputs on carbon cycle simulation were also studied. The LAI assimilated using Dual Ensemble Kalman Filter of MBF and LBF were significantly correlated with the observed LAI, with high R of 0.81 and 0.91 respectively, and lower RMSE and absolute bias, which represented the great improvement of the accuracy of MODIS LAI products. With the driving of assimilated LAI, the modeled GPP, NEE, and TER were also highly correlated with the flux observation data, with the R of 0.66, 0.47, and 0.64 for MBF, respectively, and 0.66, 0.45, and 0.73 for LBF, respectively. The accuracy of carbon fluxes modeled with assimilated LAI was higher than that acquired by the locally adjusted cubic-spline capping method, in which, the accuracy of mo-deled NEE for MBF and LBF increased by 11.2% and 11.8% at the most degrees, respectively.
叶面积指数（LAI）是森林生态系统碳循环研究中最重要的观测数据之一，也是驱动基于过程的生态系统模型的重要参数。选取毛竹林（MBF）和雷竹林（LBF）作为研究对象。首先，采用双集合卡尔曼滤波方法同化2014 - 2015年期间的MODIS叶面积指数时间序列数据。其次，将高质量同化后的毛竹林叶面积指数和雷竹林叶面积指数作为输入数据集，分别驱动BEPS模型来模拟这两种竹林生态系统的总初级生产力（GPP）、净生态系统交换量（NEE）和总生态系统呼吸量（TER）。通过观测到的碳通量数据对模拟的碳通量进行评估，并研究不同质量叶面积指数输入对碳循环模拟的影响。利用双集合卡尔曼滤波同化的毛竹林和雷竹林叶面积指数与观测到的叶面积指数显著相关，R值分别高达0.81和0.91，且均方根误差（RMSE）和绝对偏差较低，这表明MODIS叶面积指数产品的精度有了很大提高。在同化叶面积指数的驱动下，模拟的总初级生产力、净生态系统交换量和总生态系统呼吸量也与通量观测数据高度相关，毛竹林的R值分别为0.66、0.47和0.64，雷竹林的R值分别为0.66、0.45和0.73。用同化叶面积指数模拟的碳通量精度高于局部调整三次样条封顶法获得的精度，其中，毛竹林和雷竹林模拟净生态系统交换量的精度在大多数程度上分别提高了11.2%和11.8%。
