Canopy Bidirectional Reflectance Factors Research Articles

Improving the estimation of canopy structure using spectral invariants: Theoretical basis and validation

Directional area scattering factor (DASF) is a critical canopy structural parameter for vegetation monitoring as it provides an efficient tool for detection of vegetation functional traits and their dynamics. Current standard approach to estimate DASF from canopy bidirectional reflectance factor (BRF) is based on the assumption that in the weakly absorbing 710 to 790 nm spectral interval, leaf scattering does not change much with the concentration of dry matter and thus its variation can be neglected. This results in biased estimates of DASF and consequently leads to uncertainty in DASF-related applications. This study proposes a new approach to account for variations in concentrations of this biochemical constituent, which additionally uses the canopy BRF at 2260 nm. Analysis of the proposed approach based on radiative transfer models suggests significant decrease in relative root mean square error (rRMSE) by nearly a half for one- and three dimensional dense vegetated scenes, and the improvement is insensitive to the reflectivity of background. Robust refinement is also achieved in a larger actual forest scene. When the new approach is adopted to indoor multi-angular hyperspectral measurements on individual tree crowns, the relative error has also reduced, with more improvement seen in needle-leaf than in broadleaf species. Thus, the proposed DASF estimation approach outperforms the current one and can be used more reliably in DASF-related applications, such as retrieval of vegetation biochemical contents, grasping periodic changes of equatorial forests, and other ecological and environmental vegetation monitoring tasks based on spectral invariants theory.

Remote estimation of leaf nitrogen concentration in winter oilseed rape across growth stages and seasons by correcting for the canopy structural effect

Estimating crop leaf nitrogen concentration (LNC, %) with the canopy bidirectional reflectance factor (BRF) is an effective method for detecting the nitrogen (N) deficiency in crops. It is challenging to remotely estimate LNC across growth stages and seasons with a general empirical model, since the complex change in canopy structure under N deficiencies and across growth stages affects the accuracy of the estimations. The canopy scattering coefficient (CSC), the ratio of the BRF to the directional area scattering factor (DASF), has been suggested to reduce the canopy structural effect on BRF. However, the DASF can only be calculated for closed canopies and is not applicable to the early growth stages of crops when the fields are sparsely vegetated. This study proposed a new method for decoupling the canopy structural effect and canopy BRF using the near-infrared reflectance of vegetation (NIRV). NIRV is driven by the change in canopy structure while mitigates the soil contribution. The method was tested through six field experiments on ten farmers' fields of winter oilseed rape (Brassica napus L.) using both in-situ hyperspectral data and unmanned aerial vehicle (UAV) multispectral images. The results demonstrated that NIRV was closely related to the leaf area index (LAI) (R2 = 0.79) across growth stages and seasons. The CSC was derived with NIRV based on the linear relationship between NIRV and DASF for the closed canopies. The LNC predicted by the NIRV-derived CSC (R2 = 0.69, RMSE = 0.51 for in-situ hyperspectral data and R2 = 0.65, RMSE = 0.49 for UAV multispectral images) was more accurate than the results derived from the BRF (R2 = 0.55, RMSE = 0.62 for in-situ hyperspectral data and R2 = 0.59, RMSE = 0.60 for UAV multispectral images) with the independent dataset, suggesting that correcting for the canopy structural effect with NIRV provided a new alternative for suppressing the impact of canopy structure on the canopy BRF. As NIRV is easily calculated with diverse remote sensing data sources, this study proved the potential of applying NIRV to improving the accuracy and transferability of the LNC prediction model at different scales.

Effects of Mixture Mode on the Canopy Bidirectional Reflectance of Coniferous–Broadleaved Mixed Plantations

One of the main initiatives for China to achieve the goal of being carbon neutral before 2060 is transforming monocultures into mixed plantations in subtropical China, because mixed forests possess a higher quality than monocultures in various ways. Very high spatial resolution (VHR) satellite imagery is very promising to precisely monitor the transformation process under the premise of clarifying the canopy reflectance anisotropy of mixed plantations. However, it is almost impossible to understand the canopy reflectance anisotropy of mixed plantations with real satellite data due to the extreme lack of multiangular VHR satellite images. In this study, the effects of the mixture mode on the canopy bidirectional reflectance factor (BRF) were comprehensively analyzed with simulated VHR images. The three-dimensional (3D) Discrete Anisotropic Radiative Transfer model (DART) was used to construct a pure coniferous scene, a pure broadleaved scene, and 27 coniferous–broadleaved mixed plantation scenes containing 3 mixture patterns (i.e., mixed by single trees, mixed by stripes, and mixed by patches) and 9 mixing proportions (i.e., from 10% to 90% with the interval of 10%), and to simulate red (R) and near-infrared (NIR) VHR images for these 3D scenes at both the solar principal plane (SPP) and perpendicular plane (PP) under different solar-viewing geometries. Negative correlations were generally found between the canopy BRF and the ratio of conifers in a mixed stand. The anisotropy of conifer dominated plantations is more prominent than broadleaf dominated plantations, especially for the single tree mixture. Although the level of anisotropy is much lower for PP than SPP, it should not be ignored, especially for the R band. Observations under large viewing zenith angles at PP are more preferred to study the effect of mixing proportions, followed by forward observations at SPP. The R band image has higher potential to distinguish mixture patterns for broadleaf-dominated situations, while the NIR band image has a higher potential for conifer-dominated situations. Furthermore, the canopy BRF generally increases with the solar zenith angle, and one meter can be considered as the optimal spatial resolution for the optical monitoring of the mixture mode. The findings of the current study add some valuable theoretical knowledge for the accurate monitoring of coniferous–broadleaved mixed plantations with VHR imagery.

Application of a Hypergeometric Model in Simulating Canopy Gap Fraction and BRF for Forest Plantations on Sloping Terrains

The influence of tree distribution and slope on canopy gap fraction (GF) and bidirectional reflectance factor (BRF) is shown here to be non-negligible. Trees are often assumed to be randomly distributed in natural forests due to random distribution of natural resources, but this assumption is not valid for forest plantations. A geometric optical model for forest plantations (GOFP) is a geometric optical model for forest plantations on horizontal surfaces based on the theory of exclusion distance among crowns. Sloping terrains change the exclusion distance among crowns, and inevitably affect the canopy GF and BRF. In this article, GOFP with a hypergeometric model (distances among trees are considered) on horizontal surfaces is modified as GOFP-T to simulate BRF for forest plantations on sloping terrains under two scenarios: (horizontal distances among crowns remain unchanged with slope) and (sloping distances among crowns remain unchanged with slope). Two three-dimensional (3-D) radiative transfer models (DART and LESS) and field measurements are used to evaluate and validate GOFP-T simulations. The results show that 1) the canopy GF, four component area ratios, and canopy BRF simulated by GOFP-T show high consistency with results from the two 3-D model: root-mean-square errors in GF, sunlit foliage, and sunlit ground are less than 0.02, 0.06, and 0.03, respectively; 2) forest coverage and canopy reflectance in GOFP-T are compared well with point cloud results from an airboard LiDAR system and Landsat 8 OLI surface reflectance products, respectively, indicating that GOFP-T has ability in simulating canopy reflectance for forest plantations on sloping terrains. GOFP-T with the hypergeometric model in this article is the first simple model to simulate canopy GF and BRF for forest plantations on sloping terrains.

Characterizing reflectance anisotropy of background soil in open-canopy plantations using UAV-based multiangular images

The canopy bidirectional reflectance distribution function (BRDF) plays a pivotal role in estimating the biophysical parameters of plants, whereas soil background anisotropy creates challenges for their retrieval. Soil optical properties affect canopy anisotropic characteristics, especially in open-canopy areas. However, the remote sensing of background anisotropy is challenging due to the difficulties of information extraction in complex forest ecosystems and varying illumination conditions. This study develops an efficient photogrammetric technique to extract the background soil bidirectional reflectance factor (BRF) from unmanned aerial vehicle (UAV)-based multiangular images and to verify the need for accurate soil anisotropy information in canopy radiative transfer modeling. Soil BRF profiles were measured over three open-canopy sample plots from multiangular remotely sensed multispectral images collected with a hexacopter. As validation, reference soil BRF profiles were synchronously acquired by a ground-based multiangular imaging system.A high level of consistency between the ground- and UAV-measured soil BRF was observed with an RMSE of less than 0.012. Uncertainty analysis of the measured soil BRF showed that multiple scattering between sunlit soil in large sunflecks and foliage elements contributed less than 5%. Both results demonstrated that soil anisotropy can be accurately extracted from UAV multiangular measurements. To explicitly demonstrate that the use of soil anisotropy can reduce uncertainties in canopy radiative transfer simulations, we simulated the canopy BRF with Lambertian soil and with anisotropic soil using a three-dimensional (3D) radiative transfer model under different soil moisture content (SMC) levels, canopy cover (CC) levels and solar zenith angles (SZAs) with simulated realistic forest scenes. We found that less CC, lower SZAs and less SMC lead to a more significant influence of soil anisotropy on canopy reflectance; e.g., the reflectance bias reaches up to 0.3 in the hotspot direction. This illustrates that neglecting soil anisotropy can cause considerable errors in the modeling of the canopy BRF of open forests (i.e., CC levels of less than 0.5). The proposed technique facilitates the characterization of anisotropic forest background soil, which is important for advancing canopy radiative transfer modeling and validation and for the retrieval of vegetation parameters.

Improving Kernel-Driven BRDF Model for Capturing Vegetation Canopy Reflectance With Large Leaf Inclinations

Semiempirical, kernel-driven linear bidirectional reflectance distribution function (BRDF) models are widely used to characterize vegetation reflectance anisotropy and provide land surface bidirectional reflectance factor (BRF) products at the regional and global scales. However, these models usually imply an assumption of spherical leaf inclination. The effects of such an ideal assumption on simulating surface BRF remain inadequately quantified. In this article, we first evaluated the effects of leaf inclination on the most commonly used kernel-driven RossThick-LiSparse-Reciprocal (RTLSR) model by using the reflectance benchmark simulated by the mature PROSAIL (PROSAIL+SAIL) radiative transfer model. Subsequently, we improved the RTLSR model into a four-parameter version (RTLSRV4p) with a new volumetric scattering kernel derived from the assumption of vertical leaf inclination. Finally, the proposed RTLSRV4p model was validated by PROSAIL canopy BRF simulations, in situ canopy BRF measurements, and wide-angle infrared dual-mode line/area Aarray scanner (WIDAS) airborne observations. Validation results demonstrate that RTLSRV4p improves vegetation reflectance characterization for large leaf inclinations compared to the original RTLSR model, especially for the near-infrared (NIR) spectral domain. When validated against the simulated canopy BRFs, the mean root-mean-square error (RMSE), mean absolute percentage error (MAPE), bias, and coefficient of determination ( R 2) were improved from 0.0810, 31.63%, 0.0651, 0.6578 to 0.0453, 13.38%, 0.0326, 0.8734. Using the in situ BRF measurement, the fitted RMSE, MAPE, bias, and R 2 were improved from 0.0917, 14.31%, 0.0728, and 0.5776 to 0.0226, 3.35%, 0.0166, and 0.9744. These validation metrics were improved from 0.0423, 10.85%, 0.0347, and 0.6598 to 0.0258, 5.85%, 0.0181, and 0.8732 when compared against the WIDAS observations. The RTLSRV4p model also shows good performance in calculating the reflectance of vegetation. These extensive validations suggest that RTLSRV4p is promising for capturing vegetation reflectance under large leaf inclinations.

Influence of Leaf Specular Reflection on Canopy Radiative Regime Using an Improved Version of the Stochastic Radiative Transfer Model

Interpreting remotely-sensed data requires realistic, but simple, models of radiative transfer that occurs within a vegetation canopy. In this paper, an improved version of the stochastic radiative transfer model (SRTM) is proposed by assuming that all photons that have not been specularly reflected enter the leaf interior. The contribution of leaf specular reflection is considered by modifying leaf scattering phase function using Fresnel reflectance. The canopy bidirectional reflectance factor (BRF) estimated from this model is evaluated through comparisons with field-measured maize BRF. The result shows that accounting for leaf specular reflection can provide better performance than that when leaf specular reflection is neglected over a wide range of view zenith angles. The improved version of the SRTM is further adopted to investigate the influence of leaf specular reflection on the canopy radiative regime, with emphases on vertical profiles of mean radiation flux density, canopy absorptance, BRF, and normalized difference vegetation index (NDVI). It is demonstrated that accounting for leaf specular reflection can increase leaf albedo, which consequently increases canopy mean upward/downward mean radiation flux density and canopy nadir BRF and decreases canopy absorptance and canopy nadir NDVI when leaf angles are spherically distributed. The influence is greater for downward/upward radiation flux densities and canopy nadir BRF than that for canopy absorptance and NDVI. The results provide knowledge of leaf specular reflection and canopy radiative regime, and are helpful for forward reflectance simulations and backward inversions. Moreover, polarization measurements are suggested for studies of leaf specular reflection, as leaf specular reflection is closely related to the canopy polarization.

Contribution of leaf specular reflection to canopy reflectance under black soil case using stochastic radiative transfer model

Numerous canopy radiative transfer models have been proposed based on the assumption of “ideal bi-Lambertian leaves” with the aim of simplifying the interactions between photons and vegetation canopies. This assumption may cause discrepancy between the simulated and measured canopy bidirectional reflectance factor (BRF). Few studies have been devoted to evaluate the impacts of such assumption on simulation of canopy BRF at a high-to-medium spatial resolution (∼30 m). This paper focuses on quantifying the contribution of leaf specular reflection on the estimation of canopy BRF under a black soil case using one of the most efficient radiative transfer models, the stochastic radiative transfer model. Analyses of field and satellite data collected over the boreal Hyytiälä forest in Finland show that leaf specular reflection may lead to errors of up to 33.1% at 550 nm and 32.8% at 650 nm in terms of relative root mean square error. The results suggest that, in order to minimize these errors, leaf specular reflection should be accounted for in modeling BRF.

A study on the drivers of canopy reflectance variability in a boreal forest

ABSTRACTThe degree of which the observable canopy bidirectional reflectance factors (BRF) express plant trait variation at leaf and canopy scales is the fundamental physical basis underlying the use of optical remote sensing data for discriminating tree species and estimating forest biophysical variables. In this study, we quantified the relative contribution of variations in leaf optical properties (LOP), canopy structural properties, and understory reflectance, to canopy BRF variability in a boreal forest, at the spatial and spectral resolutions of Sentinel-2 (S2) Multi-Spectral Instrument. Our approach was based on physically-based forest reflectance model and global sensitivity analysis (SA) parameterized entirely with field measurements. Results showed LOP had dominant contribution to canopy BRF in shortwave infrared (SWIR) in multispecies forest areas, while canopy gap fraction in sensor’s view direction (i.e. nadir) was consistently found as the main driver of canopy BRF in red. This implies the satellite-measured BRF in red is the most robust predictor of effective canopy cover (ECC), while BRF in SWIR are optimal for tree species classification based on interspecific differences in mean leaf traits.

Influences of Leaf-Specular Reflection on Canopy BRF Characteristics: A Case Study of Real Maize Canopies With a 3-D Scene BRDF Model

The diffuse and specular components of leaf reflection are both important to determine the leaf optical properties as well as to describe the leaf bidirectional reflectance distribution function (BRDF). However, the specular component is usually ignored in practice in numerous canopy reflectance models that describe the interaction between solar light and vegetated scene components. To evaluate the impact of leaf-specular reflection on canopy bidirectional reflectance factor (BRF) characteristics, we introduce a leaf BRDF model into the radiosity-graphics combined model (RGM; a 3-D scene model) to calculate canopy BRFs with nondiffuse leaves. The modified RGM is validated by comparing simulated BRFs against in situ measured BRFs over real maize canopies. The results show that ignorance of leaf-specular reflection can result in up to 50% of relative error in the blue band (435.8 nm). A series of maize canopies with different leaf angle distributions (LADs) is reconstructed to investigate the effect of five major biophysical/geometrical parameters such as leaf area index, LAD, leaf surface property, view direction, and solar zenith angle on leaf-specular reflection contributions to the canopy BRF. It is demonstrated that increasing the incident solar zenith angle and decreasing the mean leaf angle impact the angular distribution of the canopy BRF more significantly than other factors. The cumulative hemispherical relative and absolute errors of canopy BRF caused by the leaf-specular reflection are often too large to be ignored, even for canopies with rough surface leaves. Moreover, the relative error of BRF in visible waveband shows that, in general, leaf-specular reflection has a large impact than that in near-infrared waveband. However, such impact can be sufficiently accounted for by even just consideration of the first-order leaf-specular reflection in canopy reflectance calculation, leading to a substantial improvement in simulation accuracy for most vegetation canopies.

Canopy foliar nitrogen retrieved from airborne hyperspectral imagery by correcting for canopy structure effects

A statistical relationship between canopy mass-based foliar nitrogen concentration (%N) and canopy bidirectional reflectance factor (BRF) has been repeatedly demonstrated. However, the interaction between leaf properties and canopy structure confounds the estimation of foliar nitrogen. The canopy scattering coefficient (the ratio of BRF and the directional area scattering factor, DASF) has recently been suggested for estimating %N as it suppresses the canopy structural effects on BRF. However, estimation of %N using the scattering coefficient has not yet been investigated for longer spectral wavelengths (>855nm). We retrieved the canopy scattering coefficient for wavelengths between 400 and 2500nm from airborne hyperspectral imagery, and then applied a continuous wavelet analysis (CWA) to the scattering coefficient in order to estimate %N. Predictions of %N were also made using partial least squares regression (PLSR). We found that %N can be accurately retrieved using CWA (R2=0.65, RMSE=0.33) when four wavelet features are combined, with CWA yielding a more accurate estimation than PLSR (R2=0.47, RMSE=0.41). We also found that the wavelet features most sensitive to %N variation in the visible region relate to chlorophyll absorption, while wavelet features in the shortwave infrared regions relate to protein and dry matter absorption. Our results confirm that %N can be retrieved using the scattering coefficient after correcting for canopy structural effect. With the aid of high-fidelity airborne or upcoming space-borne hyperspectral imagery, large-scale foliar nitrogen maps can be generated to improve the modeling of ecosystem processes as well as ecosystem-climate feedbacks.

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.

The effect of clumping on canopy scattering and its directional properties: a model simulation using spectral invariants

The spectral invariants theory predicts that the bidirectional reflectance factor (BRF) of a vegetation canopy can be expressed in terms of the canopy interceptance (i0), the recollision probability (p), and the directional escape probability (ρ). These spectral invariant parameters together form a novel canopy structural parameter – the directional area scattering factor (DASF). The DASF can be retrieved from remotely sensed hyperspectral imagery and has been found to be useful, e.g. for the separation of tree species. The spectral invariants theory, however, does not provide an interpretation of which specific canopy structural properties are captured by the DASF. In this study, we examined the possible link between the DASF and the canopy clumping index (β). A simple model was designed to simulate the effect of β on canopy first order scattering, which was assumed to govern the directional behaviour of the DASF. The model is based on a modified spectral invariants approach, where the assumption of constant p is relaxed so that the first order recollision probability (p1) and single scattering are calculated separately, and canopy BRF is expressed as the sum of the first and multiple order components. Simulations were performed on model canopies, where radiation penetration is described using a traditional statistical approach but allowing non-random foliage distributions caused by clumping. The results indicated a strong dependency between the modelled DASF and the canopy clumping index.

Hyperspectral remote sensing of foliar nitrogen content

A strong positive correlation between vegetation canopy bidirectional reflectance factor (BRF) in the near infrared (NIR) spectral region and foliar mass-based nitrogen concentration (%N) has been reported in some temperate and boreal forests. This relationship, if true, would indicate an additional role for nitrogen in the climate system via its influence on surface albedo and may offer a simple approach for monitoring foliar nitrogen using satellite data. We report, however, that the previously reported correlation is an artifact--it is a consequence of variations in canopy structure, rather than of %N. The data underlying this relationship were collected at sites with varying proportions of foliar nitrogen-poor needleleaf and nitrogen-rich broadleaf species, whose canopy structure differs considerably. When the BRF data are corrected for canopy-structure effects, the residual reflectance variations are negatively related to %N at all wavelengths in the interval 423-855 nm. This suggests that the observed positive correlation between BRF and %N conveys no information about %N. We find that to infer leaf biochemical constituents, e.g., N content, from remotely sensed data, BRF spectra in the interval 710-790 nm provide critical information for correction of structural influences. Our analysis also suggests that surface characteristics of leaves impact remote sensing of its internal constituents. This further decreases the ability to remotely sense canopy foliar nitrogen. Finally, the analysis presented here is generic to the problem of remote sensing of leaf-tissue constituents and is therefore not a specific critique of articles espousing remote sensing of foliar %N.

Influence of woody elements of a Norway spruce canopy on nadir reflectance simulated by the DART model at very high spatial resolution

A detailed sensitivity analysis investigating the effect of woody elements introduced into the Discrete Anisotropic Radiative Transfer (DART) model on the nadir bidirectional reflectance factor (BRF) for a simulated Norway spruce canopy was performed at a very high spatial resolution (modelling resolution 0.2 m, output pixel size 0.4 m). We used such a high resolution to be able to parameterize DART in an appropriate way and subsequently to gain detailed understanding of the influence of woody elements contributing to the radiative transfer within heterogeneous canopies. Three scenarios were studied by modelling the Norway spruce canopy as being composed of i) leaves, ii) leaves, trunks and first order branches, and finally iii) leaves, trunks, first order branches and small woody twigs simulated using mixed cells (i.e. cells approximated as composition of leaves and/or twigs turbid medium, and large woody constituents). The simulation of each scenario was performed for 10 different canopy closures (CC = 50–95%, in steps of 5%), 25 leaf area index (LAI = 3.0–15.0 m 2 m − 2 , in steps of 0.5 m 2 m − 2 ), and in four spectral bands (centred at 559, 671, 727, and 783 nm, with a FWHM of 10 nm). The influence of woody elements was evaluated separately for both, sunlit and shaded parts of the simulated forest canopy, respectively. The DART results were verified by quantifying the simulated nadir BRF of each scenario with measured Airborne Imaging Spectroradiometer (AISA) Eagle data (pixel size of 0.4 m). These imaging spectrometer data were acquired over the same Norway spruce stand that was used to parameterise the DART model. The Norway spruce canopy modelled using the DART model consisted of foliage as well as foliage including robust woody constituents (i.e. trunks and branches). All results showed similar nadir BRF for the simulated wavelengths. The incorporation of small woody parts in DART caused the canopy reflectance to decrease about 4% in the near-infrared (NIR), 2% in the red edge (RE) and less than 1% in the green band. The canopy BRF of the red band increased by about 2%. Subsequently, the sensitivity on accounting for woody elements for two spectral vegetation indices, the normalized difference vegetation index (NDVI) and the angular vegetation index (AVI), was evaluated. Finally, we conclude on the importance of including woody elements in radiative transfer based approaches and discuss the applicability of the vegetation indices as well as the physically based inversion approaches to retrieve the forest canopy LAI at very high spatial resolution.

Differences in plant canopy bi-directional reflectance factors among rice varieties

We statistically discuss the possible ways to classify rice varieties using canopy bi-directional reflectance factor (BRF) data. Fourteen varieties of rice (Oryza sativa L.) were grown in an experimental paddy field where environmental conditions such as soil, nutrients, water supply, and local climate were homogeneous. Spectral reflectance of each of the rice varieties was measured at nadir and at off-nadir angles of 45°, 30°, 15°, −15°, −30°, and −45° on both the principal and perpendicular planes at intervals of 1 nm from 400 to 850 nm. The reflectances in green (550–560 nm), red (675–685 nm), and near infrared (745–749 nm) bands at every measuring angle were computed for each rice variety. As a result of unpaired Student t-tests, the number of pairs of rice varieties that can be statistically distinguished using BRF data was larger than the number that can be distinguished using just the spectral reflectance data at the nadir angle. The difference in BRF among rice varieties was statistically significant.

Modeling BRF and Radiation Regime of Boreal and Tropical Forests: I. BRF

Monitoring of forest evolution and functioning with remote sensing depends on canopy BRF (bidirectional reflectance factor) sensitivity to biophysical parameters and to canopy PAR (photosynthetically active radiation) regime. Here, we study the canopy BRF of a tropical (Sumatra) and three boreal (Canada) forest sites, with the DART (discrete anisotropic radiative transfer) model. The behavior of PAR regime of these forests is analyzed in a companion article. We assessed the BRF sensitivity to some major experimental parameters (scale of analysis, viewing and illumination directions, sky radiation) and compared it with BRF sensitivity to commonly studied biophysical quantities: Leaf area index (LAI) and leaf optical properties. Simulations showed that BRF directional anisotropy is very large for all forests. For example, maximum relative reflectance difference with view zenith angle less than 25° is around 0.5 in the visible, 0.4 in the short wave infrared, and 0.25 in the near-infrared for tropical forest. We showed that this BRF variability associated with experimental conditions can hamper the remote detection of forest LAI and tree cover change such as deforestation of tropical forest. DART BRFs of the boreal sites were favorably compared with ground (PARABOLA) and airborne (POLDER) measured BRFs. This work stressed 1) the potential of the DART model, 2) the importance of accurate field data for validation approaches, and 3) the very strong influence of canopy architecture on forest BRF; for example, depending on forest sites, a LAI increase may imply that nadir near-infrared reflectance increases or decreases.

Relations between directional spectral vegetation indices and leaf area and absorbed radiation in Alfalfa

Sensors on satellite platforms with extreme view angles have been increasingly used to analyze regional and global vegetation cover and productivity because of frequent observations. This study, using experimental and theoretical methods, analyzed variations in vegetation indices with sun-view geometry as a means of understanding the sensitivity of relations beween vegetation indices and the biophysical properties, the leaf area index (LAI), and the instantaneous fraction of absorbed photosynthetically active radiation (fAPAR). Canopy bidirectional reflectance factors (BRFs) of an alfalfa crop were measured and simulated at a variety of solar and view zenith angles. Also, fAPAR, LAI, and leaf optical properties were measured. Measured and simulated canopy reflectances agreed generally within 1% (absolute). Normalized difference and simple ratio vegetation indices (NDVI and SRVI, respectively), derived from BRFs, varied with view and solar zenith angles. The minimum for near-infrared (NIR) BRFs and relatively high red BRFs generally occurred near nadir, resulting in some of the lowest vegetation index values. Highest VI values were generally obtained at forward view angles. Variation of NDVI with sun-view-geometry was greatest at LA's <2, whereas the range in SRVI was greatest for LAIs>2. Measured reflectances indicate that relations between NDVI and LAI and between SRVI and fAPAR were curvilinear across all solar and view zenith angle combinations in the solar principal plane, whereas relations between SRVI and LAI and between NDVI and fAPAR varied from linear to curvilinear. Analyses revealed that vegetation indices at large view zenith angles were poorly correlated with fAPAR, whereas those at small zenith angles were strongly correlated. In general, vegetation indices were more sensitive to fAPAR than to LAI, which is attributed to the fact that fAPAR is a radiation quantity, whereas LAI is nonlinearly related to radiation. Regression of fAPAR with VI values derived from combinations of red and NIR BRFs from similar and nonsimilar directions indicates that the highest correlation is in near-nadir and backscatter directions. However, further investigation into variations of relations between remotely sensed observations and canopy attributes and into the usefulness of off-nadir in extracting information is recommended.

A simple anisotropic reflectance model for homogeneous multilayer canopies

This article deals with the representation of the canopy bidirectional reflectance factor (BRF) with a model that is simple, for inversion purposes, and computationally cheap. The objective was to derive the analytical expressions of radiative transfer in covers that may comprise strongly anisotropic vegetation layers and/or bottom interface. This is carried out through the introduction of an additional upward directional flux that originates from the anisotropic components of the cover, in a widely used and reliable three-flux model, that is, the SAIL (scattering by arbitrarily inclined leaves) model. It results in a multi-layered four-flux model with a fast computer code. Parameters (i.e., r sm and r dm ) that characterize the anisotropic behavior of canopy layers are computed with the same input parameters as the SAIL model. Moreover, at least one additional parameter is used for characterizing the anisotropic behavior of the bottom interface, that is, soil surface. Depending on the LAI (leaf angle index) and LAD (leaf angle distribution) of the upper layer and on the degree of anisotropy of the underlying layers, differences between BRFs that are derived from the four-flux model and from the SAIL model can be large. This stresses the necessity of the introduction of an upward directional flux in presence of covers with strongly anisotropic characteristics.

Panicle contribution to bidirectional reflectance factors of a wheat canopy

Abstract Bidirectional reflectance factors (BRFs) of crop stands are strongly influenced by canopy architecture. In wheat, as well as in many other crops, canopy architecture changes dramatically with the phenological development of the plant community. A ground-based experiment was performed to examine the effect of panicles of winter wheat (Triticum aestivum L.) at the flowering stage on canopy BRFs. Reflectance factors were measured in the field with a portable radiometer in the red (0-63-0-69 μm) and near-infrared (0-76-0-90 μm) wavelength intervals. Observations were made at three viewing angles and 14 solar zenith angles during two consecutive days on a control target and on a target where panicles had been removed. Panicles did not contribute significantly to the red nor to the near-infrared (NIR) reflectance factors computed from nadir observations. Off-nadir NIR reflectance was also not altered by the presence of panicles, but was moderately sensitive to illumination angle. Off-nadir red reflecta...