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 (Ca+b), carotenoids (Car), anthocyanins (Anth), and Leaf Area Index (LAI)) along with SIF could provide moderate prediction accuracy for N (Ca+b, Car, Anth, SIF: R2=0.4 to 0.72) and P (Ca+b, LAI, SIF: R2=0.4 to 0.73). Furthermore, this work highlighted that SIF contributed to needle P and N assessment to different degrees.

Wheat (Triticum spp.) is widely grown and receives large amounts of nitrogen fertiliser. Grain protein content (GPC) is key to its nutritional and economic value and is influenced by genetic, management and environmental factors. Accurate pre-harvest estimation of GPC patterns could improve N efficiency and profit. Some plant traits with physiological links to GPC have been identified, but assessment of traits retrievable from satellite imagery could advance GPC estimation at commercial scales. By radiative transfer model inversion of Sentinel-2 imagery, we retrieved chlorophyll (Ca+b), leaf dry matter (Cm), leaf water content (Cw) and leaf area index (LAI) for > 6,000 hectares of commercial wheat across two years. Farmers supplied ~90,000 GPC data points collected during harvest. Using a gradient boosted machine, we predicted GPC (R2= 0.86, RMSE = 0.56) and found that Cwexplained most GPC variability under stress conditions but that contributions to GPC were distributed among plant traits in milder conditions.

Integrating far-red solar-induced chlorophyll fluorescence (SIF760) and leaf biochemical constituents (primarily leaf chlorophyll content (Ca+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 SIF760and Ca+btraits. 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, Ca+band 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 Ca+band SIF760alone. The best leaf N-estimation model built with FLs from the red and far-red regions (Ca+b, FL682.97 nm, FL757.002 nm) yielded an R2of 0.71, outperforming the standard approach used in previous works (Ca+b, SIF760) (R2= 0.56).

Traditional methods to identify biotic-induced plant stress are time-consuming and costly. Airborne hyperspectral and thermal imagery has shown promise in identifying disease symptoms caused by pathogens in several plant species. Specifically, to detect Xylella fastidiosa (Xf) and Verticillium dahliae (Vd), previous studies have aimed to detect symptomatic and asymptomatic trees with high accuracy. Nevertheless, these studies did not explore the progressive changes in plant traits with increasing disease severity levels. In this study, we investigate the dynamics of plant traits as a function of disease severity.Moreover, we focus on the plant traits derived from hyperspectral data that contribute the most to disease detection, assessing how their role is redistributed as a function of disease severity. Finally, we evaluate the contribution of the plant traits in asymptomatic trees undetectable by visual inspection, using as a reference qPCR analysis. The findings revealed that specific traits such as the NPQI and PRIn indices and SIF and Anth were the most crucial.

In this paper, we discuss the integration of systems such as multi-dimensional radiative transfer models (RTM) with deep learning (DL) algorithms to estimate plant biochemical, physiological, and morphological traits at canopy level using high-resolution hyperspectral imagery (361 bands in the 400-1000 nm spectral range). We applied the approaches to two case studies for dryland cropping in Australia (i.e., wheat and sorghum). Crop type averages for the early flight for leaf area index (LAI) varied between 2, for Canola, to as high as 4.3 for Lentils. Wheat and Barley had LAI of 4.1 and 3.8 (m2/m2), respectively. Chlorophyll a+b (Ca+b) averages for emerged crops were 18, 41, 44, 51 and 59 μg/cm2for Faba beans, Wheat, Canola, Barley and Oats, respectively. The pigment Anthocyanin varied from 4.9 to 15.9 μg/cm2for Lentils and Canola, respectively. Similar patterns were observed in the Carotenoid (Cx+c) levels (as high as 16.5 μg/cm2for Oats). For sorghum plots, the integrated DL approaches showed significant high correlation in predicting sorghum LAI (R2= 0.84, RMSE = 0.65 m2/m2) and Ca+b(R2= 0.94, RMSE = 4.94 µgcm-2). The maximum velocity carboxylation rates (Vcmax) varied between 45-75 µmol m-2s-1. For both studied periods, we yielded a R2> 0.78 and RMSE-2s-1, being the RMSE lower when using the modelled fluorescence emission for retrieving the Vcmax. In addition, we derived the solar induced fluorescence emission hyperspectral narrowband (5.8 nm) sensing and radiative transfer models (RTM).

Sub-nanometer hyperspectral imagers are increasingly being used to quantify solar-induced chlorophyll fluorescence (SIF) due to their ability to characterize narrow absorption features accurately. However, some limitations prevent their wide use in the operational context due to their high cost, weight, and complexity. On the other hand, more widely-used narrow-band hyperspectral imagers with 4–6 nm full width at half-maximum (FWHM) resolution could be a costeffective alternative for acquiring high-spatial-resolution hyperspectral imagery to derive SIF. Due to the large effects of the spectral resolution (SR) on the quantified fluorescence, the SIF levels derived from such airborne imagers with 4–6 nm FWHM are overestimated, requiring careful interpretation. In this study, we flew in tandem two airborne hyperspectral imagers with different spectral characteristics. These sensors' imagery was used to model the impact of SR on the SIF quantification using the Soil-Canopy Observation of Photosynthesis and Energy (SCOPE) model. A Support Vector Machine regression (SVR) model trained via SCOPE simulations was employed to quantify SIF at 1 nm SR from the original 5.8 nm FWHM resolution. The performance of the SIF quantification was evaluated theoretically with SCOPE and tested against airborne hyperspectral radiance and the derived SIF. Results showed that the estimated SIF at 1 nm SR agreed well with the reference SCOPE simulations (RMSE=0.097 mW/m2/nm/sr) and with airborne-quantified SIF (RMSE=0.094 mW/m2/nm/sr).

Several studies have demonstrated the influence of the spectral resolution (SR) on the retrieval of solar-induced chlorophyll fluorescence (SIF) from ground-based sensors with different spectral configurations. However, research studying the implications of the SR of airborne hyperspectral imagers on the retrieved SIF is lacking, and its interpretation is critical for precision agriculture, plant stress detection and phenotyping studies. This work investigates the effects of SR on SIF performance through the Fraunhofer Line Depth (FLD) principle at the O2-A absorption feature (760.4 nm) using two airborne hyperspectral imagers with different spectral characteristics. A sub-nanometer hyperspectral imager with 0.1-0.2 nm full-width at half-maximum (FWHM) resolution and a broader-band hyperspectral imager of 5.8 nm FWHM were flown in tandem. The campaigns were conducted over a winter wheat field with randomized experimental design, with plots receiving different nitrogen rates to ensure SIF variability. Results showed a bias on the SIF levels quantified by both airborne imagers$(\text{RMSE} =3.7$mW/m2/nm/sr), but a strong relationship between both sensors at the O2-A absorption feature ($\mathrm{R}^{2}=0.84,\ \mathrm{p} < 0.001$). Results confirm the utility of hyperspectral imagers ca. 5 nm FWHM resolution for stress detection and plant phenotyping where assessing the relative variability of SIF across experimental plots is sought.

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 (r2=0.87).