Volume 44 Issue 1
Feb.  2015
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Liang Dong, Yang Qinying, Huang Wenjiang, Peng Dailiang, Zhao Jinling, Huang Linsheng, Zhang Dongyan, Song Xiaoyu. Estimation of leaf area index based on wavelet transform and support vector machine regression in winter wheat[J]. Infrared and Laser Engineering, 2015, 44(1): 335-340.
Citation: Liang Dong, Yang Qinying, Huang Wenjiang, Peng Dailiang, Zhao Jinling, Huang Linsheng, Zhang Dongyan, Song Xiaoyu. Estimation of leaf area index based on wavelet transform and support vector machine regression in winter wheat[J]. Infrared and Laser Engineering, 2015, 44(1): 335-340.

Estimation of leaf area index based on wavelet transform and support vector machine regression in winter wheat

  • Received Date: 2014-05-18
  • Rev Recd Date: 2014-06-24
  • Publish Date: 2015-01-25
  • Leaf area index (LAI) is an important parameter of crop diagnosis and yield prediction. The LAI of winter wheat obtained from Beijing city had been estimated successfully by support vector machine regression (SVR) model built with LAI and wavelet coefficients of hyperspectral reflectance. The inversion results of this paper method and other five methods, such as selected vegetation indices and partial least-square (PLS) regression models, were analyzed. It was found that the sensitive bands to assess LAI were 680 nm, 739 nm, 802 nm, and 895 nm, and the corresponding wavelet decomposition scales were 8, 4, 9, and 8 determined by continuous wavelet transform(CWT), respectively. The decision coefficient (R2) of regression equation between LAI and wavelet coefficient was significantly higher than that of between LAI and canopy reflectance. The SVR model based on wavelet coefficients performed best with R2 of 0.86, and RMSE of 0.43, while the regression models based on two common spectral vegetation indices (NDVI and RVI) performed poor in estimating LAI of winter wheat's multiple birth period (R2 0.76, RMSE0.56). It can conclude that the pretreatment method of CWT is better effective for selecting sensitive spectral characteristics to LAI. Meanwhile, SVR is more suitable for developing model in LAI estimation than PLS regression. The combination of CWT and SVR is feasible to realize remote sensing inversion of LAI in the whole growth period of winter wheat.
  • [1] Vi a A, Gitelson A A, Nguy-Robertson A L, et al. Comparison of different vegetation indices for the remote assessment of green leaf area index of crops [J]. Remote Sensing of Environment, 2011, 115: 3468-3478.
    [2]
    [3] Knyazikhin Y, Martonchik J V, Myneni R B, et al. Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from MODIS and MISR data [J]. Journal of Geophysical Research, 1998, 103(24): 32257-32275.
    [4]
    [5]
    [6] Thenkabail P S, Smith R B, Pauw E D. Hyperspectral vegetation indices and their relationships with agricultural crop characteristics [J]. Remote Sensing of Environment, 2000, 71: 158-182.
    [7]
    [8] Blackburn G A, Ferwerda J G. Retrieval of chlorophyll concentration from leaf reflectance spectra using wavelet analysis [J]. Remote Sensing of Environment, 2008, 112: 1614-1632.
    [9] Liang Dong, Guan Qingsong, Huang Wenjiang, et al. Remote sensing inversion og leaf area index based on support vector machine regression in winter wheat [J]. Transactions of the Chinese Society of Agricultural Engineering, 2013, 29 (7): 117-123. (in Chinese) 梁栋, 管青松, 黄文江, 等. 基于支持向量机回归的冬小麦叶面积指数遥感反演[J]. 农业工程学报, 2013, 29(7): 117-123.
    [10]
    [11]
    [12] Cheng T, Rivard B, Snchez-Azofeifa G A, et al. Continuous wavelet analysis for the detection of green attack damage due to mountain pine beetle infestation [J]. Remote sensing of Environment, 2010, 114: 899-910.
    [13]
    [14] Bruce L M, Morgan C, Larsen S. Automated detection of subpixel hyperspectral targets with continuous and discrete wavelet transforms [J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39: 2217-2229.
    [15]
    [16] Vapnik V N. The Nature of Statistical Learning Theory[M]. New York: Springer-Verlag, 1995.
    [17] Li Ming, Yang Jie, Wang Hui, et al. Novel color image restoration method: sliding window based support vector regression algorithm [J]. Infrared and Laser Engineering, 2006, 35(1): 79-82. (in Chinese) 黎明, 杨杰, 王辉, 等. 一种彩色图像复原新方法: 基于滑动窗口的支持向量回归算法[J]. 红外与激光工程, 2006, 35(1): 79-82.
    [18]
    [19] Yao Yuan, Hu Gensheng, Liang Dong. Remote sensing image fuion based on wavelet support vector machine[J]. Computer Engineering, 2011, 37(3): 218-221. (in Chinese) 姚媛, 胡根生, 梁栋. 基于小波支持向量机的遥感影像融合[J]. 计算机工程, 2011, 37(3): 218-221.
    [20]
    [21] Chu Xiaoli, Yuan Hongfu, Luo Xianhui, et al. Developing near infrared spectroscopy calibration model of molar ratio between methanol and isobutylene by support vector regression [J]. Spectroscopy and Spectral Analysis, 2008, 28(6): 1227-1231. (in Chinese) 褚小立, 洪福, 骆献辉, 等. 支持向量回归建立测定醇烯比的近红外光谱校正模型[J]. 光谱学与光谱分析, 2008, 28(6): 1227-1231.
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Estimation of leaf area index based on wavelet transform and support vector machine regression in winter wheat

  • 1. Key Laboratory of Intelligent Computer & Signal Processing,Ministry of Education,Anhui University,Hefei 230039,China;
  • 2. School of Electronic and Information Engineering,Anhui University,Hefei 230039,China;
  • 3. Key Laboratory of Digital Earth Sciences,Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences,Beijing 100094,China;
  • 4. Beijing Agriculture Information Technology Research Center,Beijing 100097,China

Abstract: Leaf area index (LAI) is an important parameter of crop diagnosis and yield prediction. The LAI of winter wheat obtained from Beijing city had been estimated successfully by support vector machine regression (SVR) model built with LAI and wavelet coefficients of hyperspectral reflectance. The inversion results of this paper method and other five methods, such as selected vegetation indices and partial least-square (PLS) regression models, were analyzed. It was found that the sensitive bands to assess LAI were 680 nm, 739 nm, 802 nm, and 895 nm, and the corresponding wavelet decomposition scales were 8, 4, 9, and 8 determined by continuous wavelet transform(CWT), respectively. The decision coefficient (R2) of regression equation between LAI and wavelet coefficient was significantly higher than that of between LAI and canopy reflectance. The SVR model based on wavelet coefficients performed best with R2 of 0.86, and RMSE of 0.43, while the regression models based on two common spectral vegetation indices (NDVI and RVI) performed poor in estimating LAI of winter wheat's multiple birth period (R2 0.76, RMSE0.56). It can conclude that the pretreatment method of CWT is better effective for selecting sensitive spectral characteristics to LAI. Meanwhile, SVR is more suitable for developing model in LAI estimation than PLS regression. The combination of CWT and SVR is feasible to realize remote sensing inversion of LAI in the whole growth period of winter wheat.

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