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).

Among all essential nutrients, nitrogen (N) is required by plants in large quantities throughout the entire developmental process. This is due to its importance for plant growth and development and as a primary source of energy for photosynthesis. Previous research has demonstrated that solar-induced chlorophyll fluorescence (SIF) coupled with chlorophyll a+b content (Cab) improved the estimation of leaf N, outperforming standard vegetation indices. The present study investigates the contribution of leaf Cx, a measure of the de-epoxidation state of the xanthophyll cycle, for explaining leaf N variability, concluding that it ranks third after Caband SIF consistently over two growing seasons. Among the rest of the biochemical constituents estimated by model inversion, Cxcontributed more than anthocyanins (Anth), the total carotenoid content (Ccar), and crown-level structural traits.

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).

Traditional methods to estimate leaf nitrogen (N) from satellite imagery rely on structural and chlorophyll$a+b\,(\mathrm{C}_{\text{ab}})$vegetation indices. Recent progress with airborne hyperspectral imagery identified Cab and SIF as critical indicators for evaluating leaf N variability, yielding superior performance than standard vegetation indices. In tree orchards, accurate physiological assessments require high-spatial-resolution hyperspectral imagery to minimize canopy architecture and soil background effects. Understanding the potential of coarse-spatial-resolution spaceborne hyperspectral imagery for leaf N estimation is critical. In this study, DESIS hyperspectral imagery collected on board the International Space Station was used to assess the quantification of leaf N, evaluating the relative contributions of physiological plant traits and SIF. High-resolution airborne hyperspectral imagery and ground N data were used for validation. Results show that Cab and SIF were the most critical parameters explaining leaf N both from DESIS and from airborne hyperspectral imagery, yielding strong correlations against ground truth N data ($r^{2}=0.90, p < 0.0001$) and with airborne-predicted$\mathrm{N}\,(r^{2}=0.75, p < 0.0001)$.

Adjusting nitrogen (N) fertilization and accounting for the legacy of past N fertilizer application (i.e., residual N) based on remote sensing estimation of crop nutritional status may increase resource efficiency and promote sustainable management of cropping systems. Our main goal was to evaluate the potential of hyperspectral airborne imagers and ground-level sensors for identifying N fertilizer rates and the residual N effect from the previous crop fertilization in a maize/wheat rotation. A two-season field trial that provided various combinations of N rates and residual N response was established in central Spain. Ground-level sensors and aerial hyperspectral images were used to calculate vegetation indices (VIs). In addition, the solar-induced chlorophyll fluorescence (SIF760) was estimated by the Fraunhofer line-depth method using high-resolution hyperspectral imagery, and together with biophysical modeling, biochemical and biophysical constituents at canopy scales were retrieved. N uptake, N output, grain N concentration, and proximal sensors discriminated between different N fertilizer rates and identified the residual effect when it was relevant. Structural, photosynthetic pigments and short-wave infrared region (SWIR)-based VIs, together with SIF760and the chlorophyll$a + b$($C_{{\mathrm {ab}}}$), biomass, and the leaf area index (LAI), performed similarly on N rate detection. However, the residual effect of nitrification inhibitors was only detected by the structural (NDVI and OSAVI), chlorophyll (CCCI and NDRE), blue/green, NIR-SWIR ($\text{N}_{850,1510}$) indices, SIF760,$C_{{\mathrm {ab}}}$, biomass, and the LAI. This study confirmed the ability of remote sensing to identify N rates at early growth stages and highlighted its potential to detect residual N in crop rotation.

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.

This work focuses on understanding the effects caused by the within-tree structural heterogeneity on the Crop Water Stress Index (CWSI). We present an assessment of the CWSI variability and its relationship with stomatal conductance (Gs) using different automatic object-based tree-crown detection algorithms based on temperature quartile thresholds. The study was carried out in an almond orchard cultivated under three irrigated regimes. High-resolution (25 cm) thermal imagery was acquired by an aircraft on summer 2015. The tree crowns were segmented into 4 classes using the 25th, 50th and 75th percentiles via automatic object-based methods. Results showed that CWSI was linearly and inversely correlated with Gs in all thermal classes. However, the relationship with Gs was heavily affected by the crown segmentation levels applied, and improved remarkably when CWSI values where those corresponding to the coldest and purest vegetation pixels (r2=0.78 from pure vegetation pixels vs. r2=0.52 when warmer pixels were used).

This study present in situ measurements and model simulations aimed to understand the ability of sun-induced fluorescence (SIF) and other physiological and structural hyperspectral indices as an early indicator of forest decline. Experiments were conducted over an oak forest (Quercus ilex) affected by water stress and Phytophthora infection in the southwest of Spain. The robustness of the SIF quantification through the Fraunhofer Line Depth (FLD) principle with three spectral bands F (FLD3) was assessed using high-resolution hyperspectral imagery. FluorFLIGHT, a modified version of the 3-D radiative transfer model FLIGHT was developed to enable the simulation of canopy radiance and reflectance including fluorescence effects accounting for forest structure. Fluorescence retrievals performed better than structural and physiological indices. Albeit other pigment-related vegetation indices such as CRI550515 and RNIRCRI700 were also strongly related to physiological variables. The 3D modelling approach significantly improved the relationship between Fs and SIF and enabled the quantification of SIF as a function of fractional cover, leaf area index and chlorophyll content, yielding significant relationships between Fs ground-data measurements and fluorescence quantum yield estimated with FluorFLIGHT. The methodology also demonstrated its capabilities for mapping SIF at the crown level to detect early damage assessment in oaks undergoing forest decline.

This paper attempts to provide an understanding of the potential application of Sentinel-2 imagery for the monitoring and detection of disease symptoms caused by Xylella fastidiosa (Xf) in olive trees. A time series data of 188 Sentinel-2a images collected over the last two years was used to analyse the temporal trends in areas with Xf infected olive trees in Puglia, Southern Italy. The robustness of different physiological and structural hyperspectral indices was evaluated as an early indicator of Xf symptoms. Three validation sites for Sentinel-2a products were hence established over olive orchards in the Xf-infected zone in two different years (2016 and 2017) and overflown with a hyperspectral sensor to acquire high spatial resolution images (50 cm). Disease incidence and severity levels were recorded for more than 3300 olive trees in 18 orchards. Results demonstrate the capability of temporal Sentinel-2a was able to detect and discriminate between high and medium Xf incidence, reaching the maximum differences during the summer season. Among all the vegetation indices evaluated from Sentinel-2 imagery, OSAVI showed superior performance for detecting Xfincidence trends and OSA VI1510for detecting changes in Xf severity levels.

This letter outlines a method for quantifying the fraction of intercepted photosynthetically active radiation (fIPAR) from high spatial resolution airborne images acquired from an unmanned aerial vehicle. Airborne campaigns provided imagery of peach and citrus orchards using a six-band multispectral camera with 15-cm resolution. At the time of the airborne flights, field measurements of fIPAR taken with a ceptometer and structural data were obtained to characterize the study sites. Measuring fIPAR can be time consuming because of the need to sample for spatial and temporal variability. In this context, remote sensing techniques are useful as they make it possible to assess large areas. There is a lack of studies exploring the use of remote sensing techniques to estimate fIPAR in structurally complex crops. In this letter, the use of high spatial resolution imagery allowed us to classify each study plot into three pure components: vegetation, shaded soil, and sunlit soil. The radiation intercepted by a canopy is determined by the architecture and optical properties of the canopy. Consequently, the fractions of each component and their pure reflectance were used to estimate fIPAR in each study area with rmse = 0.06 for orange orchards and peach orchards.

Recent studies have demonstrated that the R570/R515 index is highly sensitive to carotenoid (Cx + c) content in conifer forest canopies and is scarcely influenced by structural effects. However, validated methods for the prediction of leaf carotenoid content relationships in forest canopies are still needed to date. This paper focuses on the simultaneous retrieval of chlorophyll (Cα + b) and (Cx + c) pigments, which are critical bioindicators of plant physiological status. Radiative transfer theory and modeling assumptions were applied at both laboratory and field scales to develop methods for their concurrent estimation using high-resolution hyperspectral imagery. The proposed methodology was validated based on the biochemical pigment quantification. Canopy modeling methods based on infinite reflectance formulations and the discrete anisotropic radiative transfer (DART) model were evaluated in relation to the PROSPECT-5 leaf model for the scaling-up procedure. Simpler modeling methods yielded comparable results to more complex 3-D approximations due to the high spatial resolution images acquired, which enabled targeting pure crowns and reducing the effects of canopy architecture. The scaling-up methods based on the PROSPECT-5+DART model yielded a root-mean-square error (RMSE) and a relative RMSE of 1.48 μg/cm2(17.45%) and 5.03 μg/cm2(13.25%) for Cx + c and Cα + b, respectively, while the simpler approach based on the PROSPECT-5+Hapke infinite reflectance model yielded 1.37 μg/cm2(17.46%) and 4.71 μg/cm2(14.07%) for Cx + c and Cα+b, respectively. These predictive algorithms proved to be useful to estimate Cα + b and Cx + c from high-resolution hyperspectral imagery, providing a methodology for the monitoring of these photosynthetic pigments in conifer forest canopies.

Increasing attention is being given to chlorophyll fluorescence (F) for global monitoring of vegetation due to its relationship with physiology. New progress has been made in the methodological and technical aspects of signal retrieval with the recently published low-resolution global maps of fluorescence. Nevertheless, little progress has been made in the interpretation of the F signal when quantified in large pixels, an important issue due to the effects of structure, percentage cover, shadows, and background. High-resolution (40 cm) airborne hyperspectral imagery is used in this letter to assess the retrieval of fluorescence by the Fraunhofer line depth method from pure tree crowns and aggregated pixels. Due to canopy heterogeneity, the F signal extracted from aggregated pixels is highly degraded. A poor relationship is obtained between fluorescence extracted from pure tree crowns (Fcrown) and that quantified from pixels aggregating pure tree crowns, shadows, and background (Faggregated) (R2= 0.25; p2= 0.69 (p2= 0.38 (p2= 0.72, p <; 0.01).

Two critical limitations for using current satellite sensors in real-time crop management are the lack of imagery with optimum spatial and spectral resolutions and an unfavorable revisit time for most crop stress-detection applications. Alternatives based on manned airborne platforms are lacking due to their high operational costs. A fundamental requirement for providingusefulremote sensing products in agriculture is the capacity to combine high spatial resolution and quick turnaround times. Remote sensing sensors placed on unmanned aerial vehicles (UAVs) could fill this gap, providing low-cost approaches to meet the critical requirements of spatial, spectral, and temporal resolutions. This paper demonstrates the ability to generate quantitative remote sensing products by means of a helicopter-based UAV equipped with inexpensive thermal and narrowband multispectral imaging sensors. During summer of 2007, the platform was flown over agricultural fields, obtaining thermal imagery in the 7.5-13-mum region (40-cm resolution) and narrowband multispectral imagery in the 400-800-nm spectral region (20-cm resolution). Surface reflectance and temperature imagery were obtained, after atmospheric corrections with MODTRAN. Biophysical parameters were estimated using vegetation indices, namely, normalized difference vegetation index, transformed chlorophyll absorption in reflectance index/optimized soil-adjusted vegetation index, and photochemical reflectance index (PRI), coupled with SAILH and FLIGHT models. As a result, the image products of leaf area index, chlorophyll content (Cab), and water stress detection from PRI index and canopy temperature were produced and successfully validated. This paper demonstrates that results obtained with a low-cost UAV system for agricultural applications yielded comparable estimations, if not better, than those obtained by traditional manned airborne sensors.

Efficient water management in agriculture requires accurate estimation of the evapotranspiration (ET) of crops. This work presents the progress made on assessing the water status and ET estimation on discontinuous crop canopies where soil and shadow components affect the remote sensing imagery used. Two orchards used to conduct regulated deficit irrigation (RDI) experiments were monitored between 2004 and 2006 and at three different levels: i) near-field point thermal sensors monitoring single crowns continuously; ii) airborne level using high-resolution thermal and multispectral imagery collected at different times of day; and iii) satellite level using TERRA-ASTER and Quickbird for estimating surface temperature and multispectral imagery, respectively.

The spatial organization of above-ground plant material plays an important role in controlling not only plant functional activities like photosynthesis and evapotranspiration, but also the photo-vegetation interactions. To improve our understanding of such interactions, the acquisition of highly detailed information about the 3D architecture of individual plants and communities of plants is required. Recently, Light detection and ranging (LiDAR) sensors, both at the ground and the airborne-level, have emerged as useful tools for mapping 3D plant structure. One such ground-based instrument is the Intelligent Laser Ranging and Imaging System (ILRIS 3D), which was developed at Optech Incorporated. This laser scanner, generates a 3D digital reconstruction of any scene, by actively emitting laser pulses and recording the time elapsed for the return of a pulse, thereby measuring the distance of any given object. It is the objective of this research to utilize the ILRIS 3D to measure structural, and biophysical information of individual trees for use as direct inputs into complex radiative transfer models. The key parameters under investigation are crown dimensions (i.e. shape, area, and volume), crown-level gap fraction (GF) and crown- level leaf area index (LAI). The ILRIS 3D was used to acquire 3D point clouds of an artificial 6' Ficus tree, in a controlled laboratory environment. Measured XYZ point cloud data was segmented to retrieve laser pulse return density profiles, which subsequently were used to estimate gap fraction and LAI . Gap fraction estimates were cross-validated with traditional methods of histogram thresholding of digital photographs (r2= 0.96). Crown LAI estimates were compared with the actual values (r2= 0.95, RMSE = 0.45). The next challenge was to implement the developed algorithms to real crowns, namely olive (Olea europaea L.) orchards in southern Spain. Individual tree-level ILRIS 3D data was collected from 24 structurally diverse crowns. Crown dimensional profiles were extracted for ILRIS data that was collected from a horizontal view (i.e ground-based) and a nadir view (i.e from platform 12 meters above ground). Preliminary retrievals from the olive orchards dataset is described here, while current ongoing field measurements are being conducted to validate the findings. Successful demonstration of extracting crown-level structural parameters like gap fraction and LAI from ground-based LiDAR will be important new information that can be used for detailed radiative transfer modeling in olive orchards and likely lead to more robust inversion algorithms.

This work provides a description of the research conducted to assess if ASTER satellite data enable the detection of the water status in orchard tree crops. Summer and winter TERRA-ASTER images were acquired over a study area of Seville in southern Spain over a 6-year period. 1076 olive orchards were monitored in this area, obtaining field location, area, tree density, and irrigation status information. Surface temperature images were obtained using the TES method and 0.5 m resolution panchromatic ortho-rectified imagery collected over the entire area to obtain vegetation cover. A comparison study of the temperature difference between orchards under different irrigation schemes is presented. Results in summer ASTER images showed differences between irrigated and rainfed orchard fields of 2 K, decreasing the differences to 0.5 K in ASTER winter images due to the lack of water stress condition. This methodology could be useful to detect the crop irrigation stratus operationally from ASTER satellite thermal imagery, with potential implications for evapotranspiration studies in non- homogeneous canopies.

It has been demonstrated that the spectrum of fluorescence emission is dependent on leaf temperature, thus there is a need for thermal information in order to interpret fluorescence signals. Temperature is also related to transpiration and stomata closure, which affects CO2 uptake and fluorescence. Therefore temperature measurements help to confirm the trends observed in fluorescence measurements. While fluorescence is immediately and uniquely related to photosynthesis, temperature provides additional information about plant status and instantaneous energy/water fluxes between plants and the atmosphere. The objective of this paper is to demonstrate the role of surface temperature in the context of FLuorescence EXplorer (FLEX) mission. To this end a database of land surface emissivity and temperature obtained from thermal radiometric measurements carried out in the framework of the Sen2FLEX (SENtinel-2 and FLuorescence Experiment) campaign has been used. These data were acquired in the agricultural site of Barrax (Spain) in June and July of 2005 simultaneously with airborne imagery acquired with Airborne Hyperspectral Scanner (AHS) and Compact Airborne Spectrographic Imager (CASI) sensors and data from the airborne fluorescence measuring instrument (AIRFLEX). As a result of these studies we have identified the optimal band configuration for the FLEX mission, that allows the estimation of land surface temperature with an accuracy lower than 1.5 K. To this end single-channel, split-window, and Temperature Emissivity Separation algorithms have been compared using a database of simulated brightness temperatures.

The combined PROSPECT leaf optical properties model and SAIL canopy bidirectional reflectance model, i.e. PROSAIL, has been used for about fifteen years to increase our understanding of plant canopy spectral and bidirectional reflectance in the solar domain and to develop new methods of vegetation biophysical properties retrieval. It links the spectral variation of canopy reflectance with its directional variation. This link is the key to simultaneously estimate biophysical/structural canopy variables for applications in agriculture, plant physiology, and forestry at different scales. PROSPECT and SAIL are still evolving: they have undergone recent improvements both at the leaf and the plant levels and became one of the most popular radiative transfer tools in these domains due to their ease of use, their robustness, and because they have been validated by many lab/field/space experiments over the years. This paper is intended to review this subject, which has been extensively researched in optical remote sensing.

Life on Earth depends on photosynthesis. Photosynthetic systems evolved early in Earth history and have been stable for 2.5 billion years, providingprimafacieevidence for these significance of evolutionary functions. Pigments perform multiple plant functions from increasing the range of energy captured for photosynthesis to a range of protective functions. Given the importance of pigments to leaf functioning, greater effort is needed to determine whether individual pigments can be identified and quantified by high fidelity spectroscopy. New methods to identify overlapping pigment absorptions would provide a major advance for understanding plant functions, quantifying net carbon exchange, and identifying plant stresses.

A high spectral resolution spectrometer with 0.065-nm full-width half-maximum was used for collecting spectral measurements in an orchard field under three water stress treatments. The study was part of the FluorMOD project funded by the European Space Agency to develop a leaf-canopy reflectance model to simulate the effects of fluorescence. Water deficit protocols generated a gradient in solar-induced chlorophyll fluorescence emission and tree physiological measures. Diurnal steady-state chlorophyll fluorescence was measured from leaves in the field between June and November 2004 using the PAM-2100 fluorometer to study the effects of water stress on chlorophyll fluorescence. Spectral measurements of downwelling irradiance and upwelling crown radiance were conducted with the narrow-band spectrometer, enabling the canopy reflectance to be obtained at subnanometer spectral resolution and permitting the evaluation of the fluorescence in-filling effects on reflectance in trees under water stress conditions. Diurnal and seasonal measurements showed consistently lower steady-state fluorescence (Ft) and quantum yield /spl Delta/F/Fm' in water-stressed trees, yielding mean values of Ft=0.38 (well-irrigated) and Ft=0.21 (water-stressed trees). The agreement between Ft and water potential showed that steady-state fluorescence could be used to detect differences in water stress levels, with determination coefficients ranging between r/sup 2/=0.48 and r/sup 2/=0.81 for individual dates. Analysis in the 680-770-nm range showed that the chlorophyll fluorescence in-filling in the O/sub 2/-A band at 760 nm is sensitive to diurnal variations of fluorescence and water stress, yielding r/sup 2/=0.76 (well-watered treatment), r/sup 2/=0.89 (intermediate stress treatment), and r/sup 2/=0.7 (extreme stress treatment), demonstrating the close relationships between Ft and in-filling at the crown level.

Fire danger models identify fuel moisture content (FMC) of live vegetation as a critical variable, since it affects fire ignition and propagation. FMC can be calculated by dividing equivalent water thickness (EWT) by dry matter content (DM). The "leaf optical properties spectra" (PROSPECT) model was inverted to estimate EWT and DM separately using the Leaf Optical Properties Experiment (LOPEX) database, based on 490 measurements of leaf optical and biochemical properties. DM estimations were poor when leaf samples were fresh (r/sup 2/ = 0.38). Results of a sensitivity analysis conducted on the spectral response of samples demonstrated that water absorption masks the effects of DM on the spectral response. This causes a poor estimation of FMC (r/sup 2/ = 0.33), even though EWT estimation was good (r/sup 2/ = 0.94). However, DM of dry samples was accurately estimated (r/sup 2/ = 0.84), since no water was present. FMC estimation in fresh leaf material improved considerably (r/sup 2/ = 0.89) by accounting for DM in fresh samples (r/sup 2/ = 0.71), when a constant species-dependent DM value is used. A similar approach was taken on a canopy level by linking the PROSPECT leaf model with the Lillesaeter infinitive reflectance canopy model using data from laboratory measurements under controlled conditions. As expected, results indicate grater difficulty to estimate DM and FMC on a canopy level, although similar trends were observed. DM estimation improved from r/sup 2/ = 0.12 to r/sup 2/ = 0.39 when considering measurements of dry samples, which when used for FMC estimation, correlations increase from r/sup 2/ = 0.62 to r/sup 2/ = 0.81. Therefore, DM can be accurately estimated only when plant material is dry, and it is a necessary measurement in order to estimate FMC accurately. DM remains stable over annual time periods although lower values are expected during the drought season. However, time variations in DM are smaller than DM variations among species. Because there is also a decrease in water content with low EWT values during phenologically dormant periods, multitemporal data could be used to estimate FMC. Further research must be applied on real canopies to confirm these results.

Typical environment plant stress factors are excess of light, deficiencies of water and nutrients, temperature extremes, diseases, pests and pollutants. An early indicator for vegetation status and vitality by means of remote sensing would therefore serve a range of applications such as renewable resource management and precision farming. Vegetation fluorescence is a direct indicator for plant physiology, and could therefore be used as an early indicator for vegetation health status and vitality. Vegetation chlorophyll fluorescence is a function of photochemical processes and efficiency, which are directly linked to primary productivity and CO/sub 2/ flux from the atmosphere, and could therefore also provide a means to assess the terrestrial carbon cycle. A study was launched in October 2002 by the European Space Agency to advance the underlying science of a possible future vegetation fluorescence space mission by addressing the need for an integrated canopy fluorescence model. The objective of this study is to review and advance existing fluorescence models at the leaf level and to integrate these into canopy models in order to simulate the combined spectral reflected radiance and passive fluorescence emission signals. This model is to be validated with new and existing field campaign measurements. This paper reports on the status of this project, the input radiometric and photosynthetic variables have been selected to define the vegetation fluorescence signal consisting of far-red and red-chlorophyll fluorescence as spectral emission features, normalized to the canopy illumination levels, when linked to the leaf-level fluorescence reflectance-transmittance model defined in this study. Measurement protocols to validate fluorescence-leaf models will be defined.

This investigation quantitatively links physiologically based estimators of forest stand condition, such as chlorophyll concentration, to hyperspectral observations of Jack Pine (Pinus Banksiana), a dominant Boreal Forest species. Between June and September of 2001, four Intensive Field Campaigns (IFC) of data collection were conducted over the forested areas near Sudbury, Ontario, Canada. Using the CASI sensor, data were collected, in the visible and near infrared domain, over eight selected Jack Pine sites. Supplementing the airborne campaigns was simultaneous on-site collection of foliage samples for laboratory spectral and chemical measurements. The study first linked needle-level reflectance and pigment content through the inversions of leaf level radiative transfer models such as PROSPECT. Next, the red-edge index (R750/710), was scaled up to the canopy level through the use of canopy models and infinite reflectance calculations, which simulate the canopy as an optically thick vegetation medium. However, for the relatively open and clumped jack pine stands such a simple approach requires careful validation due to the confounding effects of the open canopy structure. Accordingly, the analysis has focused on high spatial resolution CASI imagery (1 meter) for which tree crowns, shadows, and open (sun-lit) understory can be identified visually and approaches can be examined for validity and effects. Effectively eliminating these confounding variables will permit the generation of predictive needle pigment content maps for forest condition assessment.

2003 - International Geoscience and Remote Sensing Symposium, IGARSS'03 pp. 1676-1678 vol. 3, Toulouse (France), 21-25/7/2004.

This work provides a description of the investigations conducted to assess the detection of chlorophyll fluorescence from hyperspectral CASI data. The viability of retrieval of solar-induced fluorescence through airborne imaging spectrometer measurements of radiance of targets under natural illumination is studied. A method based on in-filling of fluorescence signals in atmospheric oxygen absorption lines is applied to study sites of corn crop grown under different stress conditions due to variation in nitrogen treatment. Results of the relationships found between measurements of laser-induced fluorescence and chlorophyll concentration at the ground level with the in-filling of the 762 nm oxygen band and optical indices calculated from CASI imagery R/sub 685//R/sub 655/, derivative D/sub 730//D/sub 706/, and the double-peak derivative f/spl bsol/reflectance index Dpi (D/sub 688//spl middot/D/sub 710/ )/ D/sub 697//sup 2/ are presented.

This study used support vector machines to classify multiangle POLDER data. Boreal wetland ecosystems cover an estimated 90 x 10/sup 6/ ha, about 36% of global wetlands, and are a major source of trace gases emissions to the atmosphere. Four to 20 percent of the global emission of methane to the atmosphere comes from wetlands north of 40/spl deg/N latitude. Large uncertainties in emissions exist because of large spatial and temporal variation in the production and consumption of methane. Accurate knowledge of the areal extent of open water and inundated vegetation is critical to estimating magnitudes of trace gas emissions. Improvements in land cover mapping have been sought using physical-modeling approaches, neural networks, and active-microwave, examples that demonstrate the difficulties of separating open water, inundated vegetation and dry upland vegetation. Here we examine the feasibility of using a support vector machine to classify POLDER data representing open water, inundated vegetation and dry upland vegetation.

Radiative transfer theory and modeling assumptions were applied at laboratory and field scales in order to study the link between leaf reflectance and transmittance and canopy hyper-spectral data for chlorophyll content estimation. This study was focused on 12 sites of Acer saccharum M. (sugar maple) in the Algoma Region, Canada, where field measurements, laboratory-simulation experiments, and hyper-spectral compact airborne spectrographic imager (CASI) imagery of 72 channels in the visible and near-infrared region and up to 1-m spatial resolution data were acquired in the 1997, 1998, and 1999 campaigns. A different set of 14 sites of the same species were used in 2000 for validation of methodologies. Infinite reflectance and canopy reflectance models were used to link leaf to canopy levels through radiative transfer simulation. The closed and dense (LAI>4) forest canopies of Acer saccharum M. used for this study, and the high spatial resolution reflectance data targeting crowns, allowed the use of optically thick simulation formulae and turbid-medium SAILH and MCRM canopy reflectance models for chlorophyll content estimation by scaling-up and by numerical model inversion approaches through coupling to the PROSPECT leaf radiative transfer model. Study of the merit function in the numerical inversion showed that red edge optical indices used in the minimizing function such as R/sub 750//R/sub 710/ perform better than when all single spectral reflectance channels from hyper-spectral airborne CASI data are used, and in addition, the effect of shadows and LAI variation are minimized.