Estimating nitrogen concentration in rape from hyperspectral data at canopy level using support vector machines

The estimation of nitrogen concentration from remotely sensed data has been the subject of some work. However, few studies have addressed the effective model for monitoring nitrogen status at canopy level using Support Vector Machines (SVM). The present study is focused on the assessment of an estimation model for nitrogen concentration of rape canopy with hyperspectral data. Two types of estimation model, the traditional statistical method based on stepwise linear regression (SLR) and the emerging computationally powerful techniques based on support vector machines were applied The Root Mean Square Error (RMSE) and T values were used to assess their predictability. The results show that a better agreement between the observed and the predicted nitrogen concentration were obtained by using the SVM model. Compared to the SLR model, the SVM model improved the results by lowering RMSE by 11.86–21.13 %, and by increasing T by 20.00–29.41 % for different spectral transformations. The study demonstrated the potential of SVM to estimate nitrogen concentration using canopy level hyperspectral data and it was concluded that SVM may provide a useful exploratory and predictive tool when applied to canopy-level hyperspectral reflectance data for monitoring nitrogen status of rape.