Photon Recollision Probability Research Articles

Physically based modelling of spectral transmittance through forest canopies

Abstract Physically based models simulating the spectral transmittance of solar radiation through forest canopies are useful tools for examining the connections between the shortwave radiation environment and the productivity and biodiversity of the forest floor. We report a comprehensive evaluation of two approaches simulating forest canopy spectral transmittance. The approaches were (i) three‐dimensional radiative transfer modelling in canopies composed of individual trees filled with turbid media and (ii) photon recollision probability theory (p‐theory), and were implemented using DART‐FT and PARAS models, respectively. The simulations were evaluated against mean and standard deviation of canopy transmittance spectra measured under clear‐sky conditions in forest plots across central and Northern Europe. In general, both models agreed well with the in situ measurements. They performed equally in conifer forests, while PARAS had a slightly lower accuracy than DART‐FT in broadleaved forests. We conclude that both approaches produce realistic simulations of canopy spectral transmittance at the spatial scale tested in this study, and that p‐theory constitutes a computationally efficient and easy‐to‐parameterize alternative to three‐dimensional radiative transfer.

Estimation of canopy photon recollision probability from airborne laser scanning

The past two decades have witnessed the widespread application of the spectral invariant theory (p-theory) in modeling the shortwave radiation absorbed or scattered by vegetation. The basic principle of this theory is that the canopy reflectance is solely determined by the optical properties of leaves and the photon recollision probability p. As one of the spectral invariants, p serves as a critical bond between the spectral characteristics of the canopy at any wavelength and the reflectance, transmittance, or absorptance of the vegetation canopy, and is intimately associated with the solution of the radiative transfer equation. Currently, estimation of p-value is predominantly accomplished through canopy structural parameters, such as the spherically averaged silhouette to total area ratio (STAR¯) or Leaf Area Index (LAI). In this work, we provide an approach to estimate canopy photon recollision probability directly from Airborne Laser Scanning (ALS) data. We showed that the recollision probability can be interpreted as the interceptions of virtual rays emitted from sampling points within tree crowns described with turbid media, which avoided detailed reconstruction of leaf structures. The approach was evaluated with both virtual experiments as well as field measurements. The virtual experiments employed the Large-scalE remote Sensing data and image Simulation model (LESS) to simulate virtual ALS scanning data based on three RAdiation transfer Model Intercomparison (RAMI) actual canopies, with each divided into 25 segments, for which the p-values could be obtained through LESS. Our findings showed that p-values can be accurately estimated from ALS point clouds. Specifically, it exhibited great consistency with RMSE% less than 5% for broadleaved forest scenes, 25% for mixed forest scenes, and less than 10% for coniferous forest scenes in virtual experiments, respectively, and less than 25% compared to field measurements. This study displays the potential of ALS as a promising tool for forest structure characterization and short-wave radiation modeling in forest ecosystems.

Quantifying stand-level clumping of boreal, hemiboreal and temperate European forest stands using terrestrial laser scanning

Clumping is critical for quantifying the radiation regime of forest canopies, but challenging to measure. We developed a method to measure clumping index (CI) of forest stands using voxel-based estimates of leaf area density from terrestrial lidar data. Our method uses the principle of the silhouette to total area ratio (STAR), a commonly used shoot clumping correction approach. We adapted the concept to forest stands, and derived that STAR at canopy scale (STARf) is no longer simply a clumping index, but a summary variable for forest structure in general. CI can be calculated from STARf when leaf area index is known.We measured CI and STARf of 38 forest stands in Finland, Estonia, and Czechia to study the natural range of these variables, their relationships to other forest variables, and to Landsat 8 OLI surface reflectance. CI did not include clumping below voxel scale (20 cm), and ranged from 0.6 to 0.9, with the lowest values (i.e., the most clumped canopies) in conifer forests and temperate oak forests, and the highest CI values (i.e., the most random canopies) in boreal broadleaved forests. CI was closely correlated with surface reflectance in conifer forests, which may be explained by contradicting influence of clumping that decreases canopy reflectance, but increases visibility of the forest floor.From the viewpoint of forest reflectance modeling, STAR is a useful variable due to its close relationship with the photon recollision probability, i.e., the probability that a photon will interact with a canopy element after being scattered from another canopy element. The photon recollision probability is used to model the influence of forest structure on reflectance. Our method provides a physically-based means of measuring STARf, and thus the photon recollision probability, hence contributing to the development of new methods for interpreting forest canopy structure from optical remote sensing data.

Synergistic use of multi- and hyperspectral remote sensing data and airborne LiDAR to retrieve forest floor reflectance

Forest floor vegetation can account for a notable fraction of forest productivity and species diversity, and the composition of forest floor vegetation is an important indicator of site type. The signal from the forest floor influences the interpretation of optical remote sensing (RS) data. Retrieval of forest floor reflectance properties has commonly been investigated with multiangular RS data, which often have a coarse spatial resolution. We developed a method that utilizes a forest reflectance model based on photon recollision probability to retrieve forest floor reflectance from near-nadir data. The method was tested in boreal, hemiboreal, and temperate forests in Europe, with hemispherical photos and airborne LiDAR as alternative data sources to provide forest canopy structural information. These two data sources showed comparable performance, thus demonstrating the value of using airborne LiDAR as the structural reflectance model input to derive wall-to-wall maps of forest floor reflectance. We derived such maps from multispectral Sentinel-2 MSI and hyperspectral PRISMA satellite images for a boreal forest site. The validation against in situ measurements showed fairly good performance of the retrievals in sparse forests (that had effective plant area index less than 2). In dense forests, the retrievals were less accurate, due to the small contribution of forest floor to the RS signal. We also demonstrated the use of the method in monitoring the recovery of forest floor vegetation after a thinning disturbance. The reflectance model that we used is computationally efficient, making it well applicable also to data from new and forthcoming hyperspectral satellite missions.

First validation of Earth Reflector Type Index (p) parameter from DSCOVR EPIC VESDR data product using Terrestrial Ecosystem Research Network observing sites in Australia

The spectral invariants theory predicts that amount of radiation scattered by a canopy in a given wavelength is a simple function of the leaf albedo and a spectrally invariant parameter p. This study provides the first assessment of p parameter obtained from the Earth Reflector Type Index (ERTI) provided in Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) Vegetation Earth System Data Record (VESDR) product. Our results suggest that ERTI parameter can possibly deliver information about photon recollision probability, albeit with a risk of lower accuracy, even for conditions that differ from the original assumptions in the spectral invariants theory (non-reflecting soil, dense canopies). We verified the expected link between ERTI p parameter in the EPIC VESDR product, foliage clumping, and canopy cover with empirical data. Importantly, this was done with study sites in Australia that included types of forests that are still relatively unexplored with spectral invariants theory. Our findings contribute to understanding product uncertainty and spatial representativeness of the in situ measurements from the Terrestrial Ecosystem Research Network (TERN) SuperSites in Australia.

Airborne Measurements of Surface Albedo and Leaf Area Index of Snow‐Covered Boreal Forest

AbstractHelicopter based simultaneous measurements of broadband surface albedo and the effective leaf area index (LAIeff) were carried out in subarctic area of Finnish Lapland in spring 2008, 2009, and 2010 under varying illumination and snow cover conditions. Vertical profile measurements show that the found relationship between albedo and LAIeff seems to be rather independent of the flight altitude and therefore the footprint scale. Actually, flights above 500 m in altitude revealed low variations of the surface albedo approaching an aerial average at 1 km, meaning that a footprint of 20 km is representative of the landscape. The albedo of the area was beta distributed, and without LAIeff values below 0.25, the average albedo value of the area would decrease from 0.49 to 0.44 showing the albedo sensitivity to sparse vegetation. The results agreed with the photon recollision probability based model PARAS and the MODIS satellite albedo product MCD43A3. However, differences between satellite based and airborne albedo values were noticed, which could be explained by a difference in footprint size and/or the strong local heterogeneity as certain flights were operated on specific targets.

Assessment of a photon recollision probability based forest reflectance model in European boreal and temperate forests

We report a new version and an empirical evaluation of a forest reflectance model based on photon recollision probability (p). For the first time, a p-based approach to modeling forest reflectance was tested in a wide range of differently structured forests from different biomes. To parameterize the model, we measured forest canopy structure and spectral characteristics for 50 forest plots in four study sites spanning from boreal to temperate biomes in Europe (48°–62°N). We compared modeled forest reflectance spectra against airborne hyperspectral data at wavelengths of 450–2200 nm. Large overestimation occurred, especially in the near-infrared region, when the model was parameterized considering only leaves or needles as plant elements and assuming a Lambertian canopy. The model root mean square error (RMSE) was on average 80%, 80%, 54% for coniferous, broadleaved, and mixed forests, respectively. We suggest a new parameterization that takes into account the nadir to hemispherical reflectance ratio of the canopy and contribution of woody elements to the forest reflectance. We evaluated the new parameterization based on inversion of the model, which resulted in average RMSE of 20%, 15%, and 11% for coniferous, broadleaved, and mixed forests. The model requires only few structural parameters and the spectra of foliage, woody elements, and forest floor as input. It can be used in interpretation of multi- and hyperspectral remote sensing data, as well as in land surface and climate modeling. In general, our results also indicate that even though the foliage spectra are not dramatically different between coniferous and broadleaved forests, they can still explain a large part of reflectance differences between these forest types in the near-infrared, where sensitivity of the reflectance of dense forests to changes in the scattering properties of the foliage is high.

Effect of small-scale snow surface roughness on snow albedo and reflectance

Abstract. The primary goal of this paper is to present a model of snow surface albedo accounting for small-scale surface roughness effects. The model is based on photon recollision probability, and it can be combined with existing bulk volume albedo models, such as Two-streAm Radiative TransfEr in Snow (TARTES). The model is fed with in situ measurements of surface roughness from plate profile and laser scanner data, and it is evaluated by comparing the computed albedos with observations. It provides closer results to empirical values than volume-scattering-based albedo simulations alone. The impact of surface roughness on albedo increases with the progress of the melting season and is larger for larger solar zenith angles. In absolute terms, small-scale surface roughness can decrease the total albedo by up to about 0.1. As regards the bidirectional reflectance factor (BRF), it is found that surface roughness increases backward scattering especially for large solar zenith angle values.

The effect of snow at forest floor on boreal forest albedo diurnal and seasonal variation during the melting season

Seasonal snow cover affects markedly the albedo values of the boreal forest zone. It increases the forest albedo values and changes the diurnal variation of albedo compared to snow-free situations. Snow is also a high scatterer in visible and near-infrared wavelengths, and thus the probability of multiple scattering between forest floor and canopy is high. A photon recollision probability based model called PARAS, where multiple scattering components are included, was used to simulate albedo values of needleleaved evergreen forest with and without snow cover on the forest floor. We focused on two example dates representing typical premelt and melting season snow conditions in clear-sky cases, and multiple overcast days over melting season, and compared the diurnal and seasonal variations to estimate the effect of snow cover and multiple scattering. We found that changing the forest floor surface from snow-free to snow-covered changes the diurnal variation characteristics of the clear-sky albedo from U-shaped to W-shaped. In addition, ignoring the effect of the forest floor or multiple scattering leads to relative underestimation of forest albedo values and reflected solar energy, of up to 80% (up to 1.0 kWh/m2) and 30% (up to 0.25 kWh/m2), respectively. The underestimations depend on the forest floor cover type and the season.

Quantifying leaf optical properties with spectral invariants theory

Leaf optical spectra reflect the combination of leaf biochemical, morphological and physiological properties, and play an important role in many ecological and Earth system processes. Radiative transfer models are widely used to simulate leaf spectra by quantifying photon transfer processes of reflection, transmission and absorption within a plant leaf. Recent advances in spectral invariants theory offer a unique and efficient approach for modeling the canopy-scale radiative transfer processes, but remain underexplored for applications at the leaf scale. In this study, we developed a leaf-scale optical property model based on the spectrally invariant properties (leaf-SIP) of plant leaves. Similar to the canopy-scale model, the leaf-SIP model decouples leaf-scale radiative transfer process into two parts: wavelength-dependent contribution from leaf chemical components and wavelength-independent contribution from leaf structures, described by two spectrally invariant parameters (i.e., a photon recollision probability p and a scattering asymmetry parameter q). We implemented the leaf-SIP model by parameterizing p and q with a measurable leaf morphological trait, the leaf mass per area (LMA). We evaluated the performance of the leaf-SIP model with two in situ datasets (i.e., LOPEX and ANGERS) and the widely used PROSPECT leaf optical model. The results show that the leaf spectra simulated by the leaf-SIP model agreed well with in situ datasets and the simulations of the PROSPECT model, with a small root mean squared error (RMSE), bias, and high coefficients of determination (R2) of 0.026, 0.035, 0.95 and 0.037, 0.049, 0.91 for leaf reflectance and leaf transmittance, respectively. Our results also show that the leaf-SIP model can be used with measured leaf spectra to accurately estimate several key leaf functional traits, such as the leaf chlorophyll content, equivalent water thickness, and LMA. The leaf-SIP model provides an efficient and physical way of accurately simulating leaf spectra and retrieving key leaf functional traits from hyperspectral measurements.

Empirical validation of photon recollision probability in single crowns of tree seedlings

Physically-based methods in remote sensing provide benefits over statistical approaches in monitoring biophysical characteristics of vegetation. However, physically-based models still demand large computational resources and often require rather detailed informative priors on various aspects of vegetation and atmospheric status. Spectral invariants and photon recollision probability theories provide a solid theoretical framework for developing relatively simple models of forest canopy reflectance. Empirical validation of these theories is, however, scarce. Here we present results of a first empirical validation of a model based on photon recollision probability at the level of individual trees. Multiangular spectra of pine, spruce, and oak tree seedlings (height = 0.38–0.7 m) were measured using a goniometer, and tree hemispherical reflectance was derived from those measurements. We evaluated the agreement between modeled and measured tree reflectance. The model predicted the spectral signatures of the tree seedlings in the wavelength range between 400 and 2300 nm well, with wavelength-specific bias between −0.048 and 0.034 in reflectance units. In relative terms, the model errors were the smallest in the near-infrared (relative RMSE up to 4%, 7%, and 4% for pine, spruce, and oak seedlings, respectively) and the largest in the visible wavelength region (relative RMSE up to 34%, 20%, and 60%). The errors in the visible region could be partly attributed to wavelength-dependent directional scattering properties of the leaves. Including woody parts of tree seedlings in the model improved the results by reducing the relative RMSE by up to 10% depending on species and wavelength. Spectrally invariant model parameters, i.e. total and directional escape probabilities, depended on spherically averaged silhouette to total area ratio (STAR) of the tree seedlings. Overall, the modeled and measured tree reflectance mainly agreed within measurement uncertainties, but the results indicate that the assumption of isotropic scattering by the leaves can result in large errors in the visible wavelength region for some tree species. Our results help increasing the confidence when using photon recollision probability and spectral invariants -based models to interpret satellite images, but they also lead to an improved understanding of the assumptions and limitations of these theories.

Direct estimation of photon recollision probability using terrestrial laser scanning

Photon recollision probability p is a spectrally invariant structural parameter and a powerful tool to link canopy optical properties at any wavelengths to model reflectance, transmittance, or absorption of vegetation canopies. The concepts of the p-theory have been reported and examined at the shoot and canopy scales, but not yet for the crown level. Currently, the p-value is estimated indirectly, such as converted from the spherically averaged silhouette to total area ratio (STAR¯) or canopy transmittance measurements. In this work, we first validate the theoretical considerations of the p concept at the crown level (e.g., its relationship with STAR¯), and then provide the first method to directly estimate photon recollision probability using Terrestrial Laser Scanning (TLS) data. The proposed geometric method is data-driven and avoids explicit reconstructions of tree structures. The p-value estimated here is the average recollision probability over spatial locations. We showed that the average recollision probability can be interpreted as the local spherical openness on phytoelement (leaf or needle) surfaces, which enabled a simple visibility calculation by avoiding explicit ray tracing. The developed method was tested on synthetic crowns of needle-leaved tree species, for which the reference p-values were known. Results confirmed the validity of the p-STAR¯ relationship at the crown level, and showed that p-values can be accurately estimated from TLS point clouds with a relative root measure square error of less than 10%. This study displays the distinct advantage of TLS in delineating detailed tree crown structures and highlights its potential in studies of forest reflectance modeling.

An Interplay between Photons, Canopy Structure, and Recollision Probability: A Review of the Spectral Invariants Theory of 3D Canopy Radiative Transfer Processes

Earth observations collected by remote sensors provide unique information to our ever-growing knowledge of the terrestrial biosphere. Yet, retrieving information from remote sensing data requires sophisticated processing and demands a better understanding of the underlying physics. This paper reviews research efforts that lead to the developments of the stochastic radiative transfer equation (RTE) and the spectral invariants theory. The former simplifies the characteristics of canopy structures with a pair-correlation function so that the 3D information can be succinctly packed into a 1D equation. The latter indicates that the interactions between photons and canopy elements converge to certain invariant patterns quantifiable by a few wavelength independent parameters, which satisfy the law of energy conservation. By revealing the connections between plant structural characteristics and photon recollision probability, these developments significantly advance our understanding of the transportation of radiation within vegetation canopies. They enable a novel physically-based algorithm to simulate the “hot-spot” phenomenon of canopy bidirectional reflectance while conserving energy, a challenge known to the classic radiative transfer models. Therefore, these theoretical developments have a far-reaching influence in optical remote sensing of the biosphere.

Data synergy between leaf area index and clumping index Earth Observation products using photon recollision probability theory

Clumping index (CI) is a measure of foliage aggregation relative to a random distribution of leaves in space. The CI can help with estimating fractions of sunlit and shaded leaves for a given leaf area index (LAI) value. Both the CI and LAI can be obtained from global Earth Observation data from sensors such as the Moderate Resolution Imaging Spectrometer (MODIS). Here, the synergy between a MODIS-based CI and a MODIS LAI product is examined using the theory of spectral invariants, also referred to as photon recollision probability (‘p-theory’), along with raw LAI-2000/2200 Plant Canopy Analyzer data from 75 sites distributed across a range of plant functional types. The p-theory describes the probability (p-value) that a photon, having intercepted an element in the canopy, will recollide with another canopy element rather than escape the canopy. We show that empirically-based CI maps can be integrated with the MODIS LAI product. Our results indicate that it is feasible to derive approximate p-values for any location solely from Earth Observation data. This approximation is relevant for future applications of the photon recollision probability concept for global and local monitoring of vegetation using Earth Observation data.

Photon recollision probability in modelling the radiation regime of canopies — A review

Abstract Nearly two decades ago, the idea of the ‘spectral invariants theory’ was put forth as a new tool to model the shortwave radiation absorbed or scattered by vegetation. The theory states that the amount of radiation absorbed by a canopy should to a great accuracy depend only on the wavelength and a wavelength-independent parameter describing canopy structure. The revolutionary idea behind this theory was that it would be possible to approximate vegetation canopy absorptance, transmittance and reflectance based on only the optical properties of foliage elements and the spectrally invariant parameter(s). This paper explains how this so-called spectral invariant is related to photon recollision probability and to canopy structural variables. Other spectral invariants were later introduced to quantify the directionality of canopy scattering. Moreover, the paper reviews the advances in the theoretical development of the photon recollision probability (p) concept and demonstrates some of its applications in global and local monitoring of vegetation using remote sensing data.

The potential of Sentinel-2 data for estimating biophysical variables in a boreal forest: a simulation study

ABSTRACTIn this study we use ground reference data from 962 forest plots to demonstrate the potential of Sentinel-2 (S2) bands in estimating canopy biophysical properties in boreal forests in Finland. We simulated canopy bidirectional reflectance factors (BRFs) using the PARAS model, which applies photon recollision probability. Results showed that the highest correlation between simulated S2 BRFs and fraction of absorbed photosynthetically active radiation (fPAR) was for the band combination band 7/band 9 (wavelengths 773–793 nm and 935–955 nm, respectively) (the coefficient of determination (R2) was 0.93). For effective leaf area index (LAIe) the best band combination was band 8/band 4 (wavelengths 785–900 nm and 650–680 nm, respectively) (R2 = 0.93). Based on this study, the above-ground biomass (AGB) and S2 band combinations did not show strong relationships (R2 = 0.24). The new inverted red-edge chlorophyll index (IRECI) and Sentinel-2 red-edge position – index (S2REP) showed moderate relationships with fPAR (R2 = 0.61 and R2 = 0.45, respectively) and LAIe (R2 = 0.56 and R2 = 0.30, respectively). This study demonstrated the potential of the S2 data to estimate canopy biophysical properties.

Estimation of Spruce Needle-Leaf Chlorophyll Content Based on DART and PARAS Canopy Reflectance Models

Needle-leaf chlorophyll content (Cab) of a Norway spruce stand was estimated from CHRIS-PROBA images using the canopy reflectance simulated by the PROSPECT model coupled with two canopy reflectance models: 1) discrete anisotropic radiative transfer model (DART); and 2) PARAS. The DART model uses a detailed description of the forest scene, whereas PARAS is based on the photon recollision probability theory and uses a simplified forest structural description. Subsequently, statistically significant empirical functions between the optical indices ANCB <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">670-720</sub> and ANMB <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">670-720</sub> and the needle-leaf Cab content were established and then applied to CHRIS-PROBA data. The Cab estimating regressions using ANMB670_720 were more robust than using ANCB <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">670-720</sub> since the latter was more sensitive to LAI, especially in case of PARAS. Comparison between Cab estimates showed strong linear correlations between PARAS and DART retrievals, with a nearly perfect one-to-one fit when using ANMB <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">670-720</sub> (slope = 1.1, offset = 11 μg · cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> ). Further comparison with Cab estimated from an AISA Eagle image of the same stand showed better results for PARAS (RMSE = 2.7 μg · cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> for ANCB <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">670-720</sub> ; RMSE = 9.5 μg · cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> for ANMB670_720) than for DART (RMSE = 7.5 μg · cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> for ANCB <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">670-720</sub> ; RMSE = 23 μg · cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> for ANMB <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">670-720</sub> ). Although these results show the potential for simpler models like PARAS in estimating needle-leaf Cab from satellite imaging spectroscopy data, further analyses regarding parameterization of radiative transfer models are recommended.

Estimation of leaf area index and the fraction of absorbed photosynthetically active radiation in a boreal forest

The aim of this dissertation is to assess the accuracy of different ground reference methods used to validate satellite based leaf area index (LAI) and the fraction of absorbed photosynthetically active radiation (fPAR) products. LAI and fPAR are strongly linked, although they principally and practically measure different properties: LAI quantifies the areal interphase between soil and atmosphere, whereas fPAR quantifies the energy available for photosynthesis. Until now, the development of remote sensing based methods to estimate LAI and fPAR in a boreal forest has been hindered by the scarcity of ground data, which is required to validate and develop existing algorithms. The aim of the first part of this dissertation was to assess the impacts of different methodological approaches to estimate LAI in boreal forests, and to validate satellite based LAI products. Results showed that the accuracy of ground based LAI estimates is sensitive to both the retrieval methods and sampling scheme used to collect the optical LAI data. The satellite based measurements of LAI demonstrated a large temporal variability in LAI. The second part of the dissertation focused on measuring and modeling fPAR in a boreal forest. A new scheme for measuring and modeling ground reference fPAR based on photon recollision probability was presented in this dissertation. Ground reference fPAR is usually estimated only for the forest canopy layer. This study is among the first ones to validate the new global satellite based fPAR product called GEOV1 using data of both the forest canopy and understory layers from boreal forests. Results showed that satellite based fPAR products may correspond better with the total fPAR, instead of only the forest canopy fPAR as has often been presumed.

Scaling PRI Between Coniferous Canopy Structures

We measured simultaneously the spectral albedo of ten Scots pine shoots and the needles constituting the shoots. Next, we used the spectral information to calculate the photochemical reflectance index (PRI) which allows to retrieve photosynthetic productivity from imaging spectroscopy data. We showed that PRI can be scaled from needle to shoot level using a strictly positive scaling factor. The scaling factor is a function of photon recollision probability and the harmonic mean of needle spectral albedos at the wavelengths used in the index. The method is applicable to other normalized difference indices, as demonstrated here using normalized difference vegetation index (NDVI). At shoot level, both PRI and NDVI depend on the angle between view and illumination directions. This anisotropy, however, is not a direct function of scattering angle.

A note on upscaling coniferous needle spectra to shoot spectral albedo

Mutual shading of needles in coniferous shoots and small-scale variations in needle area density both within and between shoots violate conventional assumptions used in the definition of the elementary volume in radiative transfer models. In this paper, we test the hypothesis if it is possible to scale needle spectral albedo up to shoot spectral albedo using only one structural parameter: the spherically averaged shoot silhouette to total area ratio (STAR). To test the hypothesis, we measured both structural and spectral properties of ten Scots pine ( Pinus sylvestris) shoots and their needles. Our results indicate that it is possible to upscale from needle to shoot spectral albedo using STAR. The upscaling model performed best in the VIS and SWIR regions, and for shoots with high STAR values. As STAR is linearly related to photon recollision probability, it is also possible to apply the upscaling model as integral part of radiative transfer models.