| 光谱学与光谱分析 |
|
|
|
|
|
| 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, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, UK |
|
|
|
|
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.
|
|
Received: 2015-11-25
Accepted: 2016-03-27
|
|
|
|
Corresponding Authors:
ZHANG Shu-qing
E-mail: zhangshuqing@iga.ac.cn
|
|
[1] Xiao Z Q, Liang S L, Wang J D, et al. Remote Sensing of Environment, 2011, 115(1): 97.
[2] Jin H A, Wang J D, Xiao Z Q, et al. Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International. IEEE, 2010.
[3] LI Xi-jia, XIAO Zhi-qiang, WANG Jin-di, et al(李喜佳，肖志强，王锦地，等). Journal of Remote Sensing(遥感学报), 2013，18(1): 27.
[4] Savoy P, Mackay D S. Agricultural and Forest Meteorology, 2015, 200: 46.
[5] Dickinson R E, Tian Y H, Liu Q, et al. Journal of Geophysical Research: Atmospheres (1984—2012), 2008, 113(D16).
[6] Jiang B, Liang S L, Wang J D, et al. Remote Sensing of Environment, 2010, 114(7): 1432.
[7] Gao F, Morisette J T, Wolfe R E, et al. IEEE Geoscience and Remote Sensing Letters, 2008, 5(1): 60.
[8] Zhang G P. Neurocomputing, 2003, 50: 159.
[9] CUI Ri-xian, LIU Ya-dong, FU Jin-dong(崔日鲜, 刘亚东, 付金东). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2015，35(9): 46.
[10] ZHOU Chun-yan, HUA Deng-xin, LE Jing, et al(周春艳，华灯鑫，乐 静). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2015, 35(9): 53.
[11] Box G E, Jenkins G M, Reinsel G C. Time Series Analysis: Forecasting and Control. John Wiley & Sons, 2011.
[12] Cheng C, Cheng X S, Dai N, et al. Computers in Biology and Medicine, 2015, 66: 103.
[13] Liu Y K, Xie F, Xie C L, et al. Annals of Nuclear Energy, 2015, 85: 566.
[14] Fu L M. Neural Networks in Computer Intelligence. Tata: McGraw-Hill Education, 2003.
[15] Khashei M, Bijari M. Applied Soft Computing, 2011, 11(2): 2664.
[16] Faruk D . Engineering Applications of Artificial Intelligence, 2010, 23(4): 586. |
| [1] |
LI Hu1, ZHONG Yun1, 2, FENG Ya-ting1, LIN Zhen1, ZHU Shi-jiang1, 2*. Multi-Vegetation Index Soil Moisture Inversion Model Based on UAV
Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 207-214. |
| [2] |
ZHU Yan-ping1, CUI Chuan-jin1*, CHENG Peng-fei1, 2, PAN Jin-yan1, SU Hao1, 2, ZHANG Yi1. Measurement of Oil Pollutants by Three-Dimensional Fluorescence
Spectroscopy Combined With BP Neural Network and SWATLD[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2467-2475. |
| [3] |
LUO Dong-jie, WANG Meng, ZHANG Xiao-shuan, XIAO Xin-qing*. Vis/NIR Based Spectral Sensing for SSC of Table Grapes[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2146-2152. |
| [4] |
ZHOU Qi1, 2, WANG Jian-jun1, 2*, HUO Zhong-yang1, 2*, LIU Chang1, 2, WANG Wei-ling1, 2, DING Lin3. UAV Multi-Spectral Remote Sensing Estimation of Wheat Canopy SPAD Value in Different Growth Periods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1912-1920. |
| [5] |
ZHANG Fu1, 2, 3, CAO Wei-hua1, CUI Xia-hua1, WANG Xin-yue1, FU San-ling4*, ZHANG Ya-kun1. Non-Destructive Detection of Soluble Solids in Cherry Tomatoes by
Visible/Near Infrared Spectroscopy Based on SG-CARS-IBP[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 737-743. |
| [6] |
ZHU Chen-guang1, LIU Ya-jun2, LI Xin-xing1, 3, GONG Wei-wei4*, GUO Wei1. Detection Method of Freshness of Penaeus Vannamei Based on
Hyperspectral[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 107-110. |
| [7] |
LIU Meng-xuan1, 2, 3, 4, WU Qiong5, WANG Xu-quan1, 2, 4, CHEN Qi5, ZHANG Yong-gang1, 2, HUANG Song-lei1, 2*, FANG Jia-xiong1, 2*. Validity and Redundancy of Spectral Data in the Detection Algorithm of Sucrose-Doped Content in Tea[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3647-3652. |
| [8] |
LI Zi-zhao, BI Shou-dong, CUI Yu-huan, HAO Shuang*. Comparison of Forest Stock Volume Inversion Methods Coupled With Multiple Features——A Case Study of Forest in Yarlung Zangbo River Basin[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3263-3268. |
| [9] |
XU Liang-ji1, 2, MENG Xue-ying2, WEI Ren2, ZHANG Kun2. Experimental Research on Coal-Rock Identification Method Based on
Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(07): 2135-2142. |
| [10] |
YU Yue, YU Hai-ye, LI Xiao-kai, WANG Hong-jian, LIU Shuang, ZHANG Lei, SUI Yuan-yuan*. Hyperspectral Inversion Model for SPAD of Rice Leaves Based on Optimized Spectral Index[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1092-1097. |
| [11] |
PING Li1, ZHAO Rong1, YANG Bin1*, YANG Yang1, CHEN Xiao-long2, WANG Ying1. Inversion of Particle Size Distribution in Spectral Extinction Measurements Using PCA and BP Neural Network Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(11): 3639-. |
| [12] |
LIU Ting-yue1, DAI Jing-jing2*, TIAN Shu-fang1. A Neural Network Recognition Method for Garnets Subclass Based on Hyper Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(06): 1758-1763. |
| [13] |
SU Wei1,2, WU Jia-yu1,2, WANG Xin-sheng1,2, XIE Zi-xuan1,2, ZHANG Ying1,2, TAO Wan-cheng1,2, JIN Tian1,2. Retrieving Corn Canopy Leaf Area Index Based on Sentinel-2 Image and PROSAIL Model Parameter Calibration[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(06): 1891-1897. |
| [14] |
HE Shao-fang1, SHEN Lu-ming1, XIE Hong-xia2*. Hyperspectral Estimation Model of Soil Organic Matter Content Using Generative Adversarial Networks[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(06): 1905-1911. |
| [15] |
LIU You-fu, XIAO De-qin*, WANG Chun-tao. Fertilized Eggs’ Air-Cell Change Monitoring Algorithm Based on Thermal-Image[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(02): 572-578. |
|
|
|
|
