NEIGAE OpenIR  > 湿地与全球变化学科组
Modeling and Predicting of MODIS Leaf Area Index Time Series Based on a Hybrid. SARIMA and BP Neural Network Method
C. L. Jiang, S. Q. Zhang, C. Zhang, H. P. Li and X. H. Ding; Zhang SQ(张树清)
2017
Source PublicationSpectroscopy and Spectral Analysis
Volume37Issue:1Pages:189-193
AbstractThe 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 SARI MA (0.971) or BP neural network (0.942) SARIMA and the observation. Thus, it can be concluded that, the proposed SARI MA-BP neural network method can better adapt to the LAI time series, and it outperforms the SARIMA and BP neural network methods.
DOI10.3964/j.issn.1000-0593(2017)01-0189-05
Citation statistics
Document Type期刊论文
Identifierhttp://ir.iga.ac.cn/handle/131322/7599
Collection湿地与全球变化学科组
Corresponding AuthorZhang SQ(张树清)
Recommended Citation
GB/T 7714
C. L. Jiang, S. Q. Zhang, C. Zhang, H. P. Li and X. H. Ding,Zhang SQ. Modeling and Predicting of MODIS Leaf Area Index Time Series Based on a Hybrid. SARIMA and BP Neural Network Method[J]. Spectroscopy and Spectral Analysis,2017,37(1):189-193.
APA C. L. Jiang, S. Q. Zhang, C. Zhang, H. P. Li and X. H. Ding,&张树清.(2017).Modeling and Predicting of MODIS Leaf Area Index Time Series Based on a Hybrid. SARIMA and BP Neural Network Method.Spectroscopy and Spectral Analysis,37(1),189-193.
MLA C. L. Jiang, S. Q. Zhang, C. Zhang, H. P. Li and X. H. Ding,et al."Modeling and Predicting of MODIS Leaf Area Index Time Series Based on a Hybrid. SARIMA and BP Neural Network Method".Spectroscopy and Spectral Analysis 37.1(2017):189-193.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[C. L. Jiang, S. Q. Zhang, C. Zhang, H. P. Li and X. H. Ding]'s Articles
[张树清]'s Articles
Baidu academic
Similar articles in Baidu academic
[C. L. Jiang, S. Q. Zhang, C. Zhang, H. P. Li and X. H. Ding]'s Articles
[张树清]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[C. L. Jiang, S. Q. Zhang, C. Zhang, H. P. Li and X. H. Ding]'s Articles
[张树清]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.