中文摘要：

植被叶面积指数（LAI）时间序列的建模及预测是陆面过程模型和遥感数据同化方法的重要组成部分。MODIS数据产品MOD15A2是目前应用最为广泛的LAI数据源之一,然而MODIS LAI时间序列产品包含了一些低质量的数据,例如由于云层、气溶胶等的影响,该产品在时间和空间上缺乏连续性。MODIS LAI时间序列包含线性部分和外在干扰产生的非线性部分,单一的线性方法或非线性方法都不能对其精确建模和预测。首先利用Savitzky-Golay（SG）滤波和线性插值平滑受到干扰的LAI时间序列,然后采用季节自回归积分滑动平均（SARIMA）方法、BP神经网络方法及二者的组合方法（SARIMA-BP）对MODIS LAI时间序列进行建模及预测。在SARIMA-BP神经网络组合方法中,各自在线性与非线性建模的优势得以充分发挥,其中SARIMA方法用于建模及预测LAI时间序列中的线性部分,BP神经网络方法用于对非线性残差部分进行建模及预测。实验结果显示：SG滤波和线性插值后的LAI时间序列比原LAI时间序列更平滑;SARIMA-BP神经网络组合方法的决定系数为0.981,比SARIMA和BP神经网络的0.941和0.884更接近于1;SARIMA-BP神经网络组合方法的预测值同观测值之间的相关系数为0.991,高于SARIMA（0.971）和BP神经网络（0.942）的相关系数。由此得出结论：SARIMA-BP神经网络组合方法对MODIS LAI时间序列具有更好的适应性,其建模和预测准确性高于SARIMA方法或BP神经网络方法。

英文摘要：

The modeling and predicting of vegetation Leaf area index（LAI）is an important component of land surface model and assimilation of remote sensing data.The MODIS LAI product（i.e.MOD15A2）is one of the most widely used LAI data sources.However,the time series of MODIS LAI contains some data of low quality.For example,because of the influence of the cloud,aerosol,etc.,the MODIS LAI presents the characteristics of the discontinuous in time and space.In fact,the time series of MODIS LAI include both linear and nonlinear components,which cannot be accurately modeled and predicted by either linear method or nonlinear method alone.In this paper,the original LAI time series data were first smoothed with Savitzky-Golay（SG）filtration and linear interpolation;SARIMA,BP neural network and a hybrid method of SARIMA-BP neural network were then used for modeling and predicting MODIS LAI time series.The SARIMA-BP neural network combined both SARIMA and BP neural network,which could model the linear and the nonlinear component of MODIS LAI time series respectively.That is,the final result of SARIMA-BP neural network was the sum of results of the two methods.Experiments showed that the time series of MODIS LAI that were smoothed with the SG filtration and linear interpolation were more smooth than original time series,with a determination coefficient up to 0.981,closer to 1than that of SARIMA（0.941）and BP neural network（0.884）;the correlation coefficient between SARIMA-BP neural network and the observation is 0.991,higher than that of between SARIMA（0.971）or BP neural network（0.942）SARIMA and the observation.Thus,it can be concluded that,the proposed SARIMA-BP neural network method can better adapt to the LAI time series,and it outperforms the SARIMA and BP neural network methods.