Assessing the Contribution of Airborne-Retrieved Chlorophyll Fluorescence for Nitrogen Assessment in Almond Orchards

Abstract

Standard remote sensing methods for nitrogen (N) assessment in precision agriculture rely on empirical relationships built with chlorophyll a+b (Cab) sensitive vegetation indices. Nevertheless, methods of N estimation based on the Cab vs. N relationships are strongly affected by the saturation of these indices at high N levels, and by canopy structure, shadows and soil background variability. These effects are even more pronounced in heterogeneous orchards where the tree crown structural variability is a major factor that limits the transferability of the algorithms within- and across-tree crop species. Solar-induced fluorescence (SIF) has been proposed in precision agriculture as a plant functional trait related to N due to its link with photosynthesis. However, retrieving SIF from orchards is challenging due to the mixture of sunlit and shaded crown components. The present study explored the retrieval of airborne SIF in almond orchards from hyperspectral imagery, assessing its contribution to the estimation of N. Results show that the assessment of N improved when SIF was coupled to the model estimated Cab (e.g., Cab+SIF; r2=0.95) as compared with using Cab alone (r 2 =0.87).