Evaluating the Relative Contribution of Photosystems I and II for Leaf Nitrogen Estimation Using Fractional Depth of Fraunhofer Lines and SIF Derived From Sub-Nanometer Airborne Hyperspectral Imagery

Integrating far-red solar-induced chlorophyll fluorescence (SIF
760
) and leaf biochemical constituents (primarily leaf chlorophyll content (C
a+b
)) has recently been demonstrated to improve the estimation of leaf nitrogen (N) concentration from airborne and spaceborne hyperspectral imagery in homogenous and heterogeneous crop canopies. The advent of sub-nanometer resolution imagers capable of detecting narrow solar Fraunhofer lines (FLs) has enabled a novel opportunity to investigate the prospect of leaf N estimation using individual FLs in addition to SIF
760
and C
a+b
traits. This study seeks to determine whether incorporating distinct FL depth derived from sub-nanometer airborne hyperspectral imagery could improve leaf N estimates. A sub-nanometer hyperspectral imager with ≤0.2 nm full-width at half-maximum (FWHM) resolution was flown in tandem with a narrow-band hyperspectral imager with 5.8 nm FWHM over a winter wheat field. Plots were fertilized with variable concentrations of nitrogen to enable nutrient variability. Regression models utilizing Gaussian process regression (GPR) were built with different permutations of SIF, C
a+b
and depths of individual FLs for estimating leaf N concentration. Laboratory-determined leaf N estimates were obtained by destructive sampling. Results show that GPR models incorporating the depth of distinct Fraunhofer lines as predictor variables performed better than the benchmark model constructed using C
a+b
and SIF
760
alone. The best leaf N-estimation model built with FLs from the red and far-red regions (C
a+b
, FL
682.97 nm
, FL
757.002 nm
) yielded an R
²
of 0.71, outperforming the standard approach used in previous works (C
a+b
, SIF
760
) (R
²
= 0.56).