| 光谱学与光谱分析 |
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| Modeling and Predicting of MODIS Leaf Area Index Time Series Based on a Hybrid SARIMA and BP Neural Network Method |
| JIANG Chun-lei1，2, ZHANG Shu-qing1*, ZHANG Ce3, LI Hua-peng1, DING Xiao-hui1，2 |
| 1. Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China2. University of Chinese Academy of Sciences, Beijing 100049, China3. Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, UK |
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Abstract 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 1 than 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.
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Received: 2015-11-25
Accepted: 2016-03-27
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Corresponding Authors:
ZHANG Shu-qing
E-mail: zhangshuqing@iga.ac.cn
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