Bidirectional Reflectance Factor Measurements Research Articles

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.

Estimation of leaf chlorophyll content with polarization measurements: Degree of linear polarization

Leaf chlorophyll content (LCC) is an important indicator of plant physiological status, photosynthetic capacity, plant stress and senescence. Optical reflection measurements offer a nondestructive method for estimating LCC and have been widely used in plant science and ecological applications. Light reflected from leaves surfaces can be described by photometric and polarimetric measurements. In this study, previously introduced spectral indices based on photometric measurements did not yield highly accurate estimates of LCC using multi-angular measurement in the forward scattering directions. Subsequently, an index derived from photometric measurements, the bidirectional reflectance factor (BRF), and some previously introduced spectral indices based on the degree of linear polarization (DOLP) were used to estimate LCC. We found that a newly proposed spectral index based on BRF and 1/DOLP allowed the convenient estimation of LCC with a high accuracy, explaining more than 90% of LCC variation. The method, which relates LCC to the BRF and 1/DOLP of leaves using multi-angular measurements, indicates that both photometric and polarimetric properties are effective for LCC estimation. This study opens the possibility for estimating LCC based on polarization reflected from leaves. One advantage of using polarization to remotely sense leaf biochemical parameter is that DOLP measurements do not need to be normalized by a reference panel as BRF measurements.

Observed Relationship Between BRF Spectral-Continuum Variance and Macroscopic Roughness of Clay Sediments

Spectral data offer a means of estimating the critical parameters of sediments, including sediment composition, moisture content, surface roughness, density, and grain-size distribution. Macroscopic surface roughness in particular has a substantial impact on the structure of the bidirectional reflectance factor (BRF) and the angular distribution of scattered light. In developing the models to invert the properties of the surface beyond just surface composition, roughness must also be accounted for in order to achieve reliable and repeatable results. This paper outlines laboratory studies in which the BRF and surface digital elevation measurements were performed on dry clay sediments. The results were used to explore the suitability of various roughness metrics to account for the radiometric effect of surface roughness. The metrics that are specifically addressed in this paper include random roughness and sill variance. Relative accuracy and tradeoffs between these metrics are described. We find that spectral variability, especially near spectral absorption features, correlates strongly with the quantified measures of surface roughness. We also find that spectral variability is sensitive to the sensor fore-optic size. The results suggest that roughness parameters might be directly determined from the spectrum itself. The relationship between spectral variability and macroscopic surface roughness was particularly strong in some broad spectral ranges of the visible, near infrared, and shortwave infrared, including the near-infrared region between 600 and 850 nm.

Assessment of unified models for estimating leaf chlorophyll content across directional-hemispherical reflectance and bidirectional reflectance spectra

As an important indicator of plant photosynthetic activity, leaf chlorophyll content (LCC) has often been estimated non-destructively in the past decades from reflectance spectra measured with various spectrometers and leaf-holding accessories. Most studies established LCC predictive models with either integrating sphere measured directional-hemispherical reflectance factor (DHRF) spectra or leaf clip measured bidirectional reflectance factor (BRF) spectra. Given the effect of specular reflection on leaf reflectance, it remains unclear how DHRF spectra differ from the corresponding BRF spectra and whether the derived predictive models could be transferred between these two types of spectral data. To fill the gap in spectral examination and model comparison, this study aimed to examine the effect of specular reflection on leaf reflectance properties, the commonly used spectral indices or indicators, and the derived regression models across a broad variety of leaf DHRF and BRF spectra. Specifically, we quantified the difference between leaf BRF and DHRF spectra measurements and evaluated the effect of specular reflection on the estimation of LCC with 20 spectral features in four categories (simple ratio, SR; modified SR, mSR; double difference index, DD and red edge position, REP). Seven measured datasets collected from a combination of species, growing conditions, sites, and years were used to evaluate the model difference and calibrate the unified models across DHRF and BRF spectra. The robustness of those predictive models was validated with independent measured and simulated datasets comprised of both BRF and DHRF spectra.Our results demonstrated that the BRF spectra exhibited systematically higher amplitude than DHRF spectra. The widely used vegetation indices (VIs) in the SR category exhibited the most noticeable sensitivity to specular reflection as a significant proportion in BRF spectra. The adverse effect of specular reflection can be alleviated by the other three categories to different extents. With further assessment of model comparisons, we determined four unified models from the pooled data with two DD indices and two REP metrics. Application of the unified models to the validation spectra yielded low RMSE values up to 4.5 μg/cm2 and would not result in a significant loss in accuracy as compared to the BRF-specific or DHRF-specific LCC predictive models derived from the four spectral features. The assessment of unified models with comprehensive measured and simulated data could help us better understand the mechanism underlying the specular reflection effect on the relationship of LCC with sensitive spectral features. It will also facilitate the direct estimation of LCC with the common types of leaf reflectance spectra, which is beneficial to rapid and non-destructive determination of LCC for the plant science and agronomy communities.

Effects of particle size on bidirectional reflectance factor measurements from particulate surfaces

The bidirectional reflectance factor (BRF) is commonly used to study the structure of a particulate surface based on photometric measurements. In this paper, we describe the bidirectional reflectance factor distribution of natural particulate surfaces with particles sizes varying from 0.15 mm to 0.9 mm. Two types of natural particulate surfaces (one with low reflectance and the other with moderate reflectance) were measured at visible and near-infrared wavelengths using the Northeast Normal University Laboratory Goniospectrometer System (NENULGS). Both the BRFs and anisotropic reflectance factors (ARFs) at selected wavelengths were compared with previously published results to verify the accuracy of our measurements, and we also quantitatively analyzed the effects of particle size on the BRF. It was found that the maximum reflectance difference, which was more distinct for the low-reflectance samples, between particulate surfaces with particle sizes of 0.15 mm and 0.9 mm occurred in the forward scattering direction for all samples, and the value of this maximum difference reached 59% for the low-reflectance samples. Then, we conducted a test of a photometric model to determine which parameters could be confidently linked to the surfaces' reflectance behavior. The inverted parameters were compared with the known physical parameters of our samples, such as the particle size. We found that the single-scattering albedo could be empirically used to determine the particle sizes of our samples when measurements of particulate surfaces with different particle sizes were performed under the same incidence conditions and with wide viewing angles. The potential applications of our results appear very promising for empirically resolving the spatial distribution of particle size within a given particulate sample as well as for deepening our understanding of the scattering properties of particulate media.

Soot on Snow experiment: bidirectional reflectance factor measurements of contaminated snow

Abstract. In order to quantify the effects of absorbing contaminants on snow, a series of spectral reflectance measurements were conducted. Chimney soot, volcanic sand, and glaciogenic silt were deposited on a natural snow surface in a controlled way as a part of the Soot on Snow (SoS) campaign. The bidirectional reflectance factors of these soiled surfaces and untouched snow were measured using the Finnish Geodetic Institute's Field Goniospectropolariradiometer, FIGIFIGO. A remarkable feature is the fact that the absorbing contaminants on snow enhanced the metamorphism of snow under strong sunlight in our experiments. Immediately after deposition, the contaminated snow surface appeared darker than the natural snow in all viewing directions, but the absorbing particles sank deep into the snow in minutes. The nadir measurement remained the darkest, but at larger zenith angles, the surface of the contaminated snow changed back to almost as white as clean snow. Thus, for a ground observer the darkening caused by impurities can be completely invisible, overestimating the albedo, but a nadir-observing satellite sees the darkest points, underestimating the albedo. Through a reciprocity argument, we predict that at noon, the albedo perturbation should be lower than in the morning or afternoon. When sunlight stimulates sinking more than melting, the albedo should be higher in the afternoon than in the morning, and vice versa when melting dominates. However, differences in the hydrophobic properties, porosity, clumping, or size of the impurities may cause different results than observed in these measurements.

Simulated impact of sensor field of view and distance on field measurements of bidirectional reflectance factors for row crops

It is well established that a natural surface exhibits anisotropic reflectance properties that depend on the characteristics of the surface. Spectral measurements of the bidirectional reflectance factor (BRF) at ground level provide us a method to capture the directional characteristics of the observed surface. Various spectro-radiometers with different field of views (FOVs) were used under different mounting conditions to measure crop reflectance. The impact and uncertainty of sensor FOV and distance from the target have rarely been considered. The issue can be compounded with the characteristic reflectance of heterogeneous row crops. Because of the difficulty of accurately obtaining field measurements of crop reflectance under natural environments, a method of computer simulation was proposed to study the impact of sensor FOV and distance on field measured BRFs. A Monte Carlo model was built to combine the photon spread method and the weight reduction concept to develop the weighted photon spread (WPS) model to simulate radiation transfer in architecturally realistic canopies. Comparisons of the Monte Carlo model with both field BRF measurements and the RAMI Online Model Checker (ROMC) showed good agreement. BRFs were then simulated for a range of sensor FOV and distance combinations and compared with the reference values (distance at infinity) for two typical row canopy scenes. Sensors with a finite FOV and distance from the target approximate the reflectance anisotropy and yield average values over FOV. Moreover, the perspective projection of the sensor causes a proportional distortion in the sensor FOV from the ideal directional observations. Though such factors inducing the measurement error exist, it was found that the BRF can be obtained with a tolerable bias on ground level with a proper combination of sensor FOV and distance, except for the hotspot direction and the directions around it. Recommendations for the choice of sensor FOV and distance are also made to reduce the bias from the real angular signatures in field BRF measurement for row crops.

ACT: A leaf BRDF model taking into account the azimuthal anisotropy of monocotyledonous leaf surface

Leaf reflectance of monocotyledons generally displays a strong azimuthal anisotropy due to the longitudinal orientation of the veins. The Cook and Torrance (CT) bidirectional reflectance distribution function model was adapted to account for this distinctive feature. The resulting ACT (Anisotropic Cook and Torrance) model is based on the decomposition of the roughness parameter into two perpendicular components. It is evaluated on sorghum (Sorghum halepense) and wheat (Triticum durum) leaf BRF (Bidirectional Reflectance Factor) measurements acquired using a conoscope system. Results show that the ACT model fits the measurements better than azimuthally isotropic surface models: the root mean square error computed over all the BRF measurements for both leaves decreases from ≈0.06 for the Lambertian model to ≈0.04 for the CT model and down to ≈0.03 for the ACT model. The adjusted value of the refraction index is plausible (n≈1.32) for both leaves while the retrieved roughness values perpendicular to the veins (sorghum=0.56; wheat=0.46) is about two times larger than that parallel to the veins (sorghum=0.27; wheat=0.18). Nonetheless, the observed residual discrepancies between the ACT model simulations and the measurements may be explained mainly by the Lambertian assumption of the volume scattering.

Land Surface Albedos Computed from BRF Measurements with a Study of Conversion Formulae

Land surface hemispherical albedos of several targets have been resolved using the bidirectional reflectance factor (BRF) library of the Finnish Geodetic Institute (FGI). The library contains BRF data measured by FGI during the years 2003–2009. Surface albedos are calculated using selected BRF datasets from the library. Polynomial interpolation and extrapolation have been used in computations. Several broadband conversion formulae generally used for satellite based surface albedo retrieval have been tested. The albedos were typically found to monotonically increase with increasing zenith angle of the Sun. The surface albedo variance was significant even within each target category / surface type. In general, the albedo estimates derived using diverse broadband conversion formulas and estimates obtained by direct integration of the measured spectra were in line.

Polarised bidirectional reflectance factor measurements from soil, stones, and snow

The bidirectional reflectance factor and degree of linear polarisation of selected snow, soil, and gravel types have been measured using Finnish geodetic institute field goniospectropolariphotometer (FIGIFIGO). It was observed that with all measured samples the degree of linear polarisation is weakest 0 – 30 ∘ backwards from nadir, turning 1–5% negative in the backward direction, and growing larger (5–50%) in the forward and Brewster directions. Polarisation was found to be inversely but non-linearly proportional to reflectance. In addition, a wavelength-dependent trend was found to exist in some data, but in general, the wavelength dependence was smooth. Dry old snow polarises clearly more than melting snow or new snow. White gravel polarises somewhat similarly to snow in visible region, and black gravel resembles snow in infrared. The authors conclude that polarisation observations may be useful in land surface remote sensing, but that only far-forward angles ( 60 – 70 ∘ ) contain a strong enough signal for easy interpretation. The polarisation spectrum brings little additional information to reflection spectrum, but in conditions where the reflectance spectrum cannot be accurately normalised or incident irradiation is not known, polarisation spectrum could still be usable. The directional signal is strong and may yield refractive index or microstructure, and must be understood in any application. Polarisation near nadir is low for all known samples, and can thus be safely ignored with most non-polarised imaging applications. However, for accurate atmospheric polarimetry, e.g. by polarisation and directionality of the earth's reflectances (POLDER) or aerosol polarimetry sensor (APS) sensors, it is recommended to take into account land surface polarisation.

Polarised bidirectional reflectance factor measurements from vegetated land surfaces

This study measured the spectral bidirectional reflectance factors of selected vegetated land surfaces in two linear polarisation directions. It was observed that the degree of linear polarisation is usually weakest near nadir, turning 1–5% negative in the backward direction, and growing larger in the forward and Brewster directions. Polarisation was found to be inversely but non-linearly proportional to reflectance. In addition, a wavelength-dependent trend was found to exist in some data, but in general, the wavelength dependence was smooth. In most of the practical applications, a few well-selected channels would give all the available information. The authors conclude that polarisation observations may be useful in land surface remote sensing, but that only far-forward angles ( 60 – 70 ∘ ) contain a strong enough signal for easy interpretation. Polarisation near nadir is low for all known samples, and can thus be safely ignored. Despite a strong theoretical interest, no clearly discriminating signal was found in the negative polarisation branch near backward direction, providing few opportunities for quantitative analysis. There must be a better understanding of the directional effects in all applications.

A Compact Laboratory Spectro-Goniometer (CLabSpeG) to Assess the BRDF of Materials. Presentation, Calibration and Implementation on Fagus sylvatica L. Leaves.

The design and calibration of a new hyperspectral Compact Laboratory Spectro-Goniometer (CLabSpeG) is presented. CLabSpeG effectively measures the bidirectionalreflectance Factor (BRF) of a sample, using a halogen light source and an AnalyticalSpectral Devices (ASD) spectroradiometer. The apparatus collects 4356 reflectance datareadings covering the spectrum from 350 nm to 2500 nm by independent positioning of thesensor, sample holder, and light source. It has an azimuth and zenith resolution of 30 and15 degrees, respectively. CLabSpeG is used to collect BRF data and extract BidirectionalReflectance Distribution Function (BRDF) data of non-isotropic vegetation elements suchas bark, soil, and leaves. Accurate calibration has ensured robust geometric accuracy of theapparatus, correction for the conicality of the light source, while sufficient radiometricstability and repeatability between measurements are obtained. The bidirectionalreflectance data collection is automated and remotely controlled and takes approximatelytwo and half hours for a BRF measurement cycle over a full hemisphere with 125 cmradius and 2.4 minutes for a single BRF acquisition. A specific protocol for vegetative leafcollection and measurement was established in order to investigate the possibility to extractBRDF values from Fagus sylvatica L. leaves under laboratory conditions. Drying leafeffects induce a reflectance change during the BRF measurements due to the laboratorySensors 2007, 7 1847 illumination source. Therefore, the full hemisphere could not be covered with one leaf. Instead 12 BRF measurements per leaf were acquired covering all azimuth positions for a single light source zenith position. Data are collected in radiance format and reflectance is calculated by dividing the leaf cycle measurement with a radiance cycle of a Spectralon reference panel, multiplied by a Spectralon reflectance correction factor and a factor to correct for the conical effect of the light source. BRF results of measured leaves are presented.

Using multi‐directional high‐resolution imagery from POLDER sensor to retrieve leaf area index

Multi‐directional satellite optical imagery collected at high spatial resolution potentially allows improving the accuracy of biophysical variable retrieval. The improvements result from the inclusion of the directional anisotropy of the target, which provides additional information related to vegetation structural properties. The research presented here analyses airborne imagery and ground reference data in order to quantify the accuracy of the retrieval methods for LAI (leaf area index). Both variables are estimated through processing of airborne POLDER (POLarization and Directionality of Earth Reflectances) sensor images from an agricultural area. In a first step, the BRDF (Bi‐directional Reflectance Distribution Function) of the surface is estimated using a simple parametric model, whose parameters where derived from fitting POLDER BRF (Bi‐directional Reflectance Factor) measurements. LAI estimation was performed using two different approaches, both based on an artificial neural network designed to invert a 1D soil‐vegetation radiative transfer model. The difference between the two methods is that one of them uses only the isotropic component of the BRDF parametric model and the other the full BRDF information, i.e. adding the anisotropic components. The algorithm using isotropic information shows a clear improvement when compared to semi‐empirical approaches. Root mean square error between estimated and ground measured LAI values is 0.87. However, the method using the full BRDF information yielded poorer estimates, pointing out the difficulty of fully exploiting the multi-directional information. The performance decrease is partially explained by the incoherence between real and modelled BRDF measurements.

A Spectralon BRF data base for MISR calibration applications

The Multi-angle Imaging SpectroRadiometer (MISR) is an Earth-observing sensor, which provides global retrievals of aerosols, clouds, and land surface parameters. Instrument specifications require an accurate absolute calibration, as well as accurate camera-to-camera, band-to-band and pixel-to-pixel relative response determinations. In order to achieve these requirements, MISR makes use of an on-board calibrator (OBC), as well as vicarious calibration (VC) experiments. The OBC makes use of two Spectralon diffuse panels in order to provide a flatfield illumination. Bi-monthly deployments of these panels into the camera fields-of-view will be made. At these times, solar-reflected radiance values are measured by photodiode detector standards. Accurate relative bidirectional reflectance factor (BRF) data are required to transfer these photodiode-measured radiances into camera-incident values. An independent calibration pathway, known as VC, will also be implemented in order to reduce systematic errors and to provide a crosscheck on the OBC findings. Here, the top-of-atmosphere radiances are computed from in situ observations of surface reflectance and atmospheric transmittances. Key to these measurements is knowledge of the hemispheric/directional reflectance factor (HDRF) of the field reflectance standards. This report describes the BRF measurements of the MISR flight panels, and provides an estimation as to the differences between HDRF and BRF for the MISR view angles. This BRF data base is available from the MISR web site, http://www-misr.jpl.nasa.gov.

Ground measurements of surface BRF and HDRF using PARABOLA III

The ground‐based Portable Apparatus for Rapid Acquisition of Bidirectional Observations of Land and Atmosphere (PARABOLA), version 3, provides multiangle measurements of sky and ground radiances on a spherical grid of 5° in the zenith‐to‐nadir and azimuthal planes in eight spectral channels. The hemispherical directional reflectance factor (HDRF) can be measured directly by comparing the radiance reflected by the surface in given direction to that reflected from a reference surface simultaneously observed by the PARABOLA 3. The surface bidirectional reflectance factor (BRF) cannot be measured directly, however, because of the presence of the sky diffuse illumination. The contribution of the diffuse sky radiance to the radiance reflected by the natural target surface is computed, and removed, using an iterative technique. Two approaches are employed: the first requires knowledge of the atmospheric optical depth, and the second requires the simultaneous measurements of the radiance reflected by a standard surface panel under the same atmospheric and illumination conditions. Ground measurements of the BRF and HDRF for dry lake surfaces were obtained from the PARABOLA 3 observations with better than ±10% accuracy. The results described in this work are used primarily for the vicarious calibration of the Multiangle Imaging Spectroradiometer (MISR) onboard the Earth Observing System (EOS) Terra platform and for validation of MISR BRF retrievals of selected Earth surface targets.

Modeling of the radiation regime and photosynthesis of a finite canopy using the DART model. Influence of canopy architecture assumptions and border effects

The scope of this work was to investigate the impact of the border effects and the 3-D architecture of a fallow field on: 1) its bidirectional reflectance factor (BRF); 2) its PAR (photosynthetically active radiation) regime; and, to a lesser extent, 3) on its carbon assimilation. For this purpose, laboratory BRF measurements were conducted on a sample of a fallow field. Moreover, we modified a 3-D radiative transfer model in order to simulate the visible and near infrared BRF of finite and heterogeneous media. Several scene representations were used (finite and infinite scenes with/without 1-D or 3-D distribution of leaf area index [LAI]). Results showed that border effects and LAI distribution strongly affect the BRF, with variations as large as 40% depending on the scene representations and on the spectral domain. PAR profiles and instantaneous canopy carbon assimilation of an infinite scene (natural conditions) were also studied with the 3-D model. The results stressed that, in the case of a fallow field, the use of a simple LAI profile provides enough information to accurately simulate the effects of the architecture on the PAR regime and the carbon assimilation of a fallow field.

Estimating land surface albedo in the HAPEX-Sahel southern super-site: inversion of two BRDF models against multiple angle ASAS images

The provision of spatially and temporally disaggregated values of albedo is critical to an improved understanding of energy interactions at the Earth surface. Remotely sensed estimates of land surface albedo can best be obtained by inverting models of the Bidirectional Reflectance Distribution Function (BRDF) against bidirectional reflectance factor measurements sampled at different sensor view angles and solar illumination angles. This paper describes the preliminary results obtained using such an approach over the HAPEX-Sahel (Hydrological and Atmospheric Pilot Experiment) southern super-site in Niger. Two BRDF models, one empirical (modified-Walthall) and one semi-empirical (a linear kernel-driven model, employing isotropic, geometric and volume scattering kernels), are inverted analytically against each pixel in a set of co-registered multispectral images acquired by NASA's Advanced Solid-state Array Spectroradiometer (ASAS). The paper describes the methods used to register these images to sub-pixel accuracy, to perform radiometric and first-order atmospheric correction of the data, and to invert the BRDF model against the pre-processed image data to yield spatially referenced estimates of the model parameters and angularly integrated terms related to albedo. Spatial patterns, closely related to variations in land cover type, are clearly evident in these data. It is shown that, in this instance, the simple empirical model provides a better fit to the measured data, particularly at red and near-infrared wavelengths. The poorer performance of the semi-empirical model at this particular study site is discussed in terms of the assumptions that the model makes about energy interaction with the land surface. The impacts of changes in the projected instantaneous field-of-view as a function of sensor view angle and of residual image-to-image mis-registration on the derived BRDF model parameters and estimated albedo values are also examined.