Nitrogen retrieval in grapevine (Vitis vinifera L.) canopy by hyperspectral imaging

Various nitrogen (N) prediction approaches were tested using aerial hyperspectral imagery and ground truth data (leaf tissue analysis) collected from a table grape vineyard in Shafter, California, at various phenological stages, namely pre-bloom, bloom, fruit set, and veraison. The best results were achieved by chemometrics, machine learning, and physically based modeling with the coefficient of determination (R2) values ranging between 0.68 and 0.69. A significant finding was the high correlation between the VIS-NIR spectrum and canopy N at bloom, a pattern not replicated in other phenological stages. This suggests that measurement timing may be critical for remote sensing of N, possibly due, in part, to known interactive effects of leaf age on the relationship between Chl and N. The results offer insights into using aerial spectral imagery and Radiative Transfer Modeling (RTM) for more accurate N prediction in grapevines, also suggesting that using the full VIS-NIR spectrum can potentially improve N prediction accuracy by incorporating both chlorophyll influence and canopy structure effects, thereby surpassing the traditional reliance solely on the chlorophyll-nitrogen correlation. Our study highlights the need for a better understanding of the factors that affect the efficacy of remote sensing.