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

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.