Contribution of Solar-Induced-Fluorescence for Needle Nitrogen and Phosphorus Prediction with Airborne Hyperspectral Imagery

Hyperspectral remote sensing of advanced plant traits and physiological status are explored for leaf nitrogen (N) and phosphorus (P) monitoring in the context of sustainable forestry. Previous studies on agricultural species have demonstrated that plant biochemical and biophysical constituents derived via radiative transfer models (RTMs), and other parameters, such as solar-induced chlorophyll fluorescence (SIF), provided an improved prediction of leaf N compared to traditional methods based on chlorophyll indices. In this study, we assessed the transferability of such methods to assess needle N and P in coniferous canopies, where highly heterogeneous tree-crown structures dominate. Our study across three years showed that RTM-based functional traits (chlorophyll a+b (C
a+b
), carotenoids (C
ar
), anthocyanins (A
nth
), and Leaf Area Index (LAI)) along with SIF could provide moderate prediction accuracy for N (C
a+b
, C
ar
, A
nth
, SIF: R
²
=0.4 to 0.72) and P (C
a+b
, LAI, SIF: R
²
=0.4 to 0.73). Furthermore, this work highlighted that SIF contributed to needle P and N assessment to different degrees.