PCA versus ICA for the reduction of dimensions of the spectral signatures in the search of an index for the concentration of nitrogen in plant

Abstract

The vegetation spectral indices have been widely used as estimators of the nutritional status of the crops. This study has evaluated if it is possible to improve the effectiveness of these indices to estimate the nitrogen concentration using dimension reduction techniques to process the spectral signatures. It has also demanded that the model is valid in a wide range of growing conditions and phenological stages, thus increasing the predictive power guarantee and reducing the implementation effort. This work has been done using an agronomic trial with dual-purpose triticale (X Triticosecale Wittmack) whose design included plots with different planting densities, number of grazing and fertilizer doses. The spectral signatures of the leaves were recorded with the ASD-FieldSpec3 spectroradiometer and the nitrogen concentrations were determined by Kjeldahl method. The factors with effect on nitrogen concentration were identified by the analysis of variance and pairwise comparisons and, then, the mean spectral signature was calculated for each of the groups formed. The dimensional reduction was performed with both PCA and ICA. The analysis of the relationships between components and nitrogen concentration showed that only the components obtained with PCA generated a significant model (p = 0.00) with a R2 = 0.68. The best spectral vegetation index in this test, the reflectance in green, obtained a R2 = 0.31. Although further confirmation is needed, this study shows that the PCA may be a viable alternative to spectral vegetation indices.