Bidirectional Reflectance Factor Model Research Articles

In-Situ and Aircraft Reflectance Measurement Effectiveness for CAL/VAL Activities: A Study over Railroad Valley

This paper aims to assess the relationship between the surface reflectance derived from ground based and aircraft measurements. The parameters of the Rahman–Pinty–Verstraete (RPV) and Ross Thick-LiSparse (RTLS) kernel based bi-directional reflectance distribution functions (BRDF), have been derived using actual measurements of the hemispherical-directional reflectance factor (HDRF), collected during different campaigns over the Railroad Valley Playa. The effect of the atmosphere, including that of the diffuse radiation on bi-directional reflectance factor (BRF) parameter retrievals, assessed using 6S model simulations, was negligible for the low turbidity conditions of the site under investigation (τ550≤0.05). It was also shown that the effects of the diffuse radiation on RPV spectral parameters retrieval is linear for the isotropic parameter ρ0 and the scattering parameter Θ, and can be described with a second order polynomial for the k-Minnaert parameter. In order to overcome the lack of temporal collocations between aircraft and in-situ measurements, Monte Carlo 3-D radiative transfer simulations mimicking in-situ and remote sensing techniques were performed on a synthetic parametric meshed scene defined by merging Landsat and Multianglhe Imaging Spectroradiometer (MISR) remote sensing reflectance data. We simulated directional reflectance measurements made at different heights for PARABOLA and CAR, and analyzed them according to practices adopted for real measurements, consisting of the inversion of BRF functions and the calculation of the bi-hemispherical reflectance (BHR). The difference of retrievals against the known benchmarks of kernel parameters and BHR is presented. We associated an uncertainty of up to 2% with the retrieval of area averaged BHR, independently of flight altitudes and the BRF model used for the inversion. As expected, the local nature of PARABOLA data is revealed by the difference of the anisotropic kernel parameters with the corresponding parameters retrieved from aircraft loops. The uncertainty of the resultant BHR fell within ±3%.

Optical Properties of Reflected Light From Leaves: A Case Study From One Species

The reflection property of targets is the fundamental signal for applications of optical remote sensing of the earth’s surface. In this paper, we measured the photometric and polarimetric characteristics of 15 leaves with different properties from one plant species (i.e., Pachira aquatica) using a laboratory goniospectrometer system and used the bidirectional reflectance factor (BRF) and the bidirectional polarized reflectance factor (BPRF) to describe the reflection of these leaf samples. The results illustrated that the BRF can be replaced by the $I$ parameter reflectance factor ( $I$ pRF) when the extinction of the polarizer is considered. Subsequently, the BRF model was fit to the $I$ pRF measurements at selected wavelengths, and the inverted refractive index was used in a BPRF model, which has been proposed to simulate the polarization of surfaces. We found that the modeled photometric results of all the leaves matched well with our measurement results over all the measurement directions, while the modeled polarimetric results of the leaves gave a good agreement with the measurements at the forward scattering directions. Moreover, the degree of linear polarization (Dolp) of the leaf, which is derived from the ratio between BPRF and $I$ pRF, can also be effectively computed by the combination of BPRF and BRF models in the forward scattering directions. These findings suggested that more attention should be dedicated to the combination of BRF and BPRF of leaves in the future because we can completely describe the essential optical properties of the light reflected by leaves via the polarimetric measurement. This paper indicates that the polarimetric measurement is a beneficial method for optical remote sensing applications and helps us deepen the understanding of the optical properties of leaves.

Improved kernel-driven semi-empirical bidirectional reflectance factor models for characterizing the reflection of vegetation covers: Considering a specular kernel

The kernel-driven semi-empirical BRF (bidirectional reflectance factor) model has been one of the most effective methods for characterizing the distribution of reflection from vegetation covers and it has been used in applications at the regional and global scale. However, our measured BRFs of vegetation cover exhibit a strong forward scattering peak in visible wavelengths and cannot match the model results well. Subsequently, a specular kernel is proposed for inclusion in current BRF models. The advantage of BRF models with a specular kernel is that they can model the reflection peak in the forward scattering, which leads to a notably increased match between modeled BRFs and measured BRFs of vegetation covers at visible wavelengths, and it does not influence the hot spot results of vegetation covers in the backward scattering direction. Moreover, its disadvantage is that the improved BRF models become non-linear models and have two additional free parameters. This relatively simple improvement for BRF models presented here may provide a useful method for describing the reflection of vegetation covers dominated by specular reflection and for characterizing the reflection of vegetation covers with a backward scattering feature.

Bidirectional Polarized Reflectance Factors of Vegetation Covers: Influence on the BRF Models Results

In this paper, we performed multiangular measurements spanning a wide viewing range in a hemisphere space for three types of vegetation cover and analyzed the bidirectional reflectance factor (BRF) measurements based on basic physical reflectance mechanisms to ensure the accuracy of the data. The measurements and the results with the best fitted model parameters were evaluated to determine whether the BRF models produce vegetation cover reflectance factor values that are qualitatively the same as the measured values. These models effectively characterized the BRF of the vegetation cover at most of the selected wavelengths (565, 670, and 865 nm). However, for planophile vegetation cover with smooth leaves, the current BRF models did not produce accurate values in the selected visible wavelength range; the average relative difference was approximately 0.3 at 670 nm. Subsequently, we subtracted the specular reflectance factor (calculated using the bidirectional polarized reflectance factors) from the total BRF and compared these data with the modeled results. The difference between the measured and modeled BRFs was notably decreased when we separated the specular reflectance factor at 670 nm for the planophile vegetation cover with smooth leaves. Moreover, there was a different degree of improvement in the agreement between the measured and modeled results, which depended on the wavelength and the type of vegetation cover. These results indicated that the subtraction of the specular reflectance factor effectively improved the capability of the BRF models to calculate the diffuse portion of the BRF of the vegetation cover.

Measurement of forest BRF based on mini-tree models

Based on mini-tree models,the authors constructed forest stands with different distributions to measure bidirectional reflectance factor( BRF) by a mini goniometer. The results show that the observed BRF of simulated forest canopies is reasonable and inter-compared with a three-dimensional BRF model. The reflectance in different view directions indicates a typical "bowl"shape in the near-infrared band,with significant "hot spot"effects in solar principal plane. The study has confirmed that mini-tree models can be applied to the research on forest BRF. The comparisons between different spatial distributions and terrain conditions of tree models demonstrate that forest density and slope have certain effects on forest BRF. Therefore,their features and regularity can contribute to the inversion of the land surface parameters through modeling.

Analysis of broadband surface BRDFs derived from TOA SW CERES measurements for surfaces classified by the IGBP land cover

Most studies on the reflectance properties of the Earth's surface are addressed estimating the bidirectional reflectance distribution function (BRDF) of high spatial resolution and high spectral resolution satellite measurements. This article assesses the development of broadband (BB) BRDFs from radiances corresponding to large footprints classified according to the International Geosphere-Biosphere Programme (IGBP) land-cover classification. Top-of-atmosphere (TOA) shortwave (SW) CERES (Clouds and the Earth's Radiant Energy System) measurements are employed to invert the bidirectional reflectance factor (BRF) Rahman–Pinty–Verstraete (RPV) model for regions identified with the same IGBP type. The inversion of this non-linear parametric model is optimized to improve the computation efficiency and merged into a radiative transfer model to correct the surface radiances for the atmospheric effect. Analysis of the nature of the reflectance field simulated for several regions selected for every IGBP type determines whether the creation of general BRF models for surfaces defined by the same IGBP land cover is feasible. According to the results gathered in this study, the BB BRDFs for regions classified by the IGBP classification show values for the coefficient of variation (CV) between 3.5% and 44.1%. Consequently, the high differences achieved in the reflectance fields discourage the creation of BRDFs based on the IGBP land types.

AERONET-based surface reflectance validation network (ASRVN) data evaluation: Case study for railroad valley calibration site

The AERONET-based Surface Reflectance Validation Network (ASRVN) is an operational processing system developed for validation of satellite derived surface reflectance products at regional and global scales. The ASRVN receives 50 × 50 km 2 subsets of MODIS data centered at AERONET sites along with AERONET aerosol and water vapor data, and performs an atmospheric correction. The ASRVN produces surface bidirectional reflectance factor (BRF), albedo, parameters of the Ross-Thick Li-Sparse (RTLS) BRF model, as well as Hemispherical-Directional Reflectance Factor (HDRF), which is required for comparison with the ground-based measurements. This paper presents a comparison of ASRVN HDRF with the ground-based HDRF measurements collected during 2001–2008 over a bright calibration Railroad Valley, Nevada site as part of the MODIS land validation program. The ground measurements were conducted by the Remote Sensing Group (RSG) at the University of Arizona using an ASD spectrometer. The study reveals a good agreement between ASRVN and RSG HDRF for both MODIS Terra and Aqua with rmse ~ 0.01–0.025 in the 500 m MODIS land bands B1–B7. Obtained rmse is below uncertainties due to the spatial and seasonal variability of the bright calibration 1 km 2 area. While two MODIS instruments have a similar rmse in the visible bands, MODIS Aqua has a better agreement (lower rmse) with the ground data than MODIS Terra at wavelengths 0.87–2.1 μm. An independent overall good agreement of two MODIS instruments with the ground data indicates that the relative calibration of MODIS Terra and Aqua at medium-to-bright reflectance levels for the stated time period is significantly better than uncertainties of the ASRVN and ground data.

Analysis of snow bidirectional reflectance from ARCTAS Spring-2008 Campaign

Abstract. The spring 2008 Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) experiment was one of major intensive field campaigns of the International Polar Year aimed at detailed characterization of atmospheric physical and chemical processes in the Arctic region. A part of this campaign was a unique snow bidirectional reflectance experiment on the NASA P-3B aircraft conducted on 7 and 15 April by the Cloud Absorption Radiometer (CAR) jointly with airborne Ames Airborne Tracking Sunphotometer (AATS) and ground-based Aerosol Robotic Network (AERONET) sunphotometers. The CAR data were atmospherically corrected to derive snow bidirectional reflectance at high 1° angular resolution in view zenith and azimuthal angles along with surface albedo. The derived albedo was generally in good agreement with ground albedo measurements collected on 15 April. The CAR snow bidirectional reflectance factor (BRF) was used to study the accuracy of analytical Ross-Thick Li-Sparse (RTLS), Modified Rahman-Pinty-Verstraete (MRPV) and Asymptotic Analytical Radiative Transfer (AART) BRF models. Except for the glint region (azimuthal angles φ<40°), the best fit MRPV and RTLS models fit snow BRF to within ±0.05. The plane-parallel radiative transfer (PPRT) solution was also analyzed with the models of spheres, spheroids, randomly oriented fractal crystals, and with a synthetic phase function. The latter merged the model of spheroids for the forward scattering angles with the fractal model in the backscattering direction. The PPRT solution with synthetic phase function provided the best fit to measured BRF in the full range of angles. Regardless of the snow grain shape, the PPRT model significantly over-/underestimated snow BRF in the glint/backscattering regions, respectively, which agrees with other studies. To improve agreement with experiment, we introduced a model of macroscopic snow surface roughness by averaging the PPRT solution over the slope distribution function and by adding a simple model of shadows. With macroscopic roughness described by two parameters, the AART model achieved an accuracy of about ±0.05 with a possible bias of ±0.03 in the spectral range 0.4–2.2 μm. This high accuracy holds at view zenith angles below 55–60° covering the practically important range for remote sensing applications, and includes both glint and backscattering directions.

Local analysis of MISR surface BRF and albedo over GSFC and mongu AERONET sites

We have developed an atmospheric correction algorithm to retrieve the surface bidirectional reflectance factor (BRF) and albedo from Multiangle Imaging SpectroRadiometer (MISR) measurements for small areas around Aerosol Robotic Network (AERONET) sunphotometer sites, using AERONET aerosol and column water vapor information. Our goal is to develop an indirect validation method for MISR surface reflectance products over heterogeneous land. Our algorithm makes independent retrievals with both the Li Sparse-Ross Thick kernel BRF model and the modified Rahman-Pinty-Verstraete BRF model used in the Moderate Resolution Imaging Spectroradiometer and MISR land algorithms, respectively. In this study, we report the first results of processing MISR Collection 4 data for 2003-2004 for two sites, Mongu, Zambia, and Greenbelt, MD. We found that MISR generally provides accurate retrievals of BRF and albedo in both clear and hazy atmospheric conditions, correctly reproducing the parameter time series and spatial distribution. We found that the MISR BRF, on average, is less anisotropic in the visible bands. The difference is greatest in the blue band, but decreases with increasing wavelength such that it is negligible in the near-IR band. This discrepancy originates in part in the MISR aerosol retrieval algorithm over heterogeneous land, which tends to select an aerosol model that favors spectrally invariant shapes of surface BRF. The other part of the discrepancy comes from the surface hemispherical-directional reflectance factor retrieval algorithm where the iteration loop that removes the diffuse atmospheric transmittance is currently turned off. Our initial results suggest that the MISR surface albedo is on average lower than our retrievals by about 0.005 in the green and red bands. In the near-IR, it agreed with our retrievals with the modified Rahman-Pinty-Verstraete model for the Mongu site, but was systematically lower over the Greenbelt site by about 0.016. When significant aerosol absorption is present (Mongu), the albedo discrepancy is additionally biased by the difference between the MISR and AERONET retrievals of aerosol absorption

Parameterized code SHARM-3D for radiative transfer over inhomogeneous surfaces

The code SHARM-3D, developed for fast and accurate simulations of the monochromatic radiance at the top of the atmosphere over spatially variable surfaces with Lambertian or anisotropic reflectance, is described. The atmosphere is assumed to be laterally uniform across the image and to consist of two layers with aerosols contained in the bottom layer. The SHARM-3D code performs simultaneous calculations for all specified incidence-view geometries and multiple wavelengths in one run. The numerical efficiency of the current version of code is close to its potential limit and is achieved by means of two innovations. The first is the development of a comprehensive precomputed lookup table of the three-dimensional atmospheric optical transfer function for various atmospheric conditions. The second is the use of a linear kernel model of the land surface bidirectional reflectance factor (BRF) in our algorithm that has led to a fully parameterized solution in terms of the surface BRF parameters. The code is also able to model inland lakes and rivers. The water pixels are described with the Nakajima-Tanaka BRF model of wind-roughened water surface with a Lambertian offset, which is designed to model approximately the reflectance of suspended matter and of a shallow lake or river bottom.

Modelling and measuring the spectral bidirectional reflectance factor of snow‐covered sea ice: an intercomparison study

AbstractBroadband albedo is a very important geophysical parameter in the Earth surface–atmosphere interaction in either global climate change or hydrological cycle and snowmelt runoff studies. To derive the broadband albedo accurately from satellite optical sensor observation at limited bands and at a single observation angle, the bidirectional reflectance factor (BRF) has to be specified quantitatively. In the present albedo derivation algorithms from the satellite radiance data, the BRF is either modelled or observed. Questions may arise as to how well a BRF model can be in the broadband albedo derivation. To help answer such questions, we studied the performance of a snow‐surface BRF model for two specific cases under large solar zenith angles (65° and 85°). We measured snow‐surface spectral directional reflectance under clear skies. The snow physical properties, such as snow grain size and snow density, at the same sites were also measured. In situ snow physical data are used to simulate the snow‐surface BRF and hemispherical directional reflectance factor (HDRF) through a multilayered azimuth‐ and zenith‐dependent plane‐parallel radiative transfer model. The field measurements and BRF and HDRF simulations all reveal the forward‐scattering nature of snow surface under large solar incidence angles, but the BRF model results depict the strongest forward‐scattering patterns under such solar zenith angles. Because the HDRF is simulated through coupling of the surface BRF with radiative transfer in the atmosphere, the resulting HDRF patterns agree with the field measurements better than the simulated BRF does. The deviation of the simulated HDRF from field‐based clear‐sky directional reflectance (FCDR) is within ±10% for the central (viewing zenith angle <45° ) and lateral sides of the viewing hemisphere. This level of agreement between the simulated HDRF and FCDR also implies that the simulated BRF model can provide remote‐sensing estimates of spectral albedo with an uncertainty of ±10% for the same part of the viewing hemisphere. Further improvement in BRF model performance requires better handling of single scattering properties of snow grains, surface roughness, and atmospheric correction. Also, better procedures and techniques in field measurement are necessary for more accurate assessment of the performance of BRF models. Copyright © 2004 John Wiley & Sons, Ltd.

A new inversion scheme for the RPV model

Parametric bi-directional reflectance factor (BRF) models are often inverted against remote sensing data to describe the amplitude and shape of the anisotropy of geophysical media. For a given geophysical situation, this approach hinges on the availability of (1) a suitable inversion procedure delivering the most probable values of the coefficients entering these models, and (2) reliable and accurate observational data acquired under suitable geometries of illumination and observation. The Rahman, Pinty Verstraete (RPV) model one of the parametric BRF models, idealizes the BRF values through a simple non-linear equation requiring the values of three parameters to be estimated from the remote sensing data. One parameter describes the amplitude of the BRF and the other two jointly represent the angular shape of the anisotropy. The computational load and efficiency required to perform the inversion of the RPV model are critical for operational application. Most, if not all, of the suggested inversion procedures apply to linear versions of parametric models, including a linearized version of the RPV model. Although this approach enables fast inversion methods to be implemented, the linearization prevents the model from representing adequately the wide diversity of observed BRF fields. The present paper describes a computationally efficient inversion method that can be used with the original, i.e., non-linear, version of the RPV model. This method can be applied to retrieve the three model parameters of the original RPV model from an analysis of actual BRF data, while offering the opportunity of specifying the desired accuracy. The procedure delivers ranges of parameter values that depend on the user requirements and capitalizes on the performance of this non-linear model. Two applications are made with laboratory measurements and BRF data sets measured using the multi-angle imaging spectroradiometer (MISR) instrument of the Jet Propulsion Laboratory (JPL) on board the NASA EOS Terra platform.

衛星データを対象としたＢＲＦモデルを用いた草地植生の草丈算出に関する研究

It is important that understand BRF (Bi-directional Reflectance Factor) effect of ground reflectance for vegetation monitoring. BRF model proposed by Watanabe et al. (1996) has the characteristics of expression by ground based data and Sun-Target-Sensor geometry. Therefore, it may be able to estimate grass height by using multi-channel satellite sensor data, which has wide swath (ex. ADEOS OCTS (Ocean Color and Temperature Scanner), EOS-AM1 MODIS (Moderate Resolution Imaging Spectroradiometer) ) . The aim of this study is to develop the algorithm of estimation of grass height by using BRF model, which is expressed by polynomial equation. The results show that vegetation height estimated by BRF model has high accuracy, and also we confirmed that our proposed algorithm is useful for estimating vegetation height.

The impact of multi‐angular measurements on the accuracy of land‐surface Albedo retrieval: Peliminary results for the proposed ESA LSPIM mission

This paper documents, through a sensitivity study, the retrieval accuracy of the albedo and the bidirectional reflectance factor (BRF) field as a function of the number of observations and their angular distribution. A BRF data base is generated for different surface types, solar positions, and wavelengths. A realistic amount of random noise is added to approximate actual measurement conditions. A parametric BRF model is inverted against the noisy data strings of variable lengths and subsequently used in a forward mode to reconstruct the BRF field and estimate the albedo. The retrieval accuracy is estimated by comparing the original and the reconstructed fields and values. For a single observation, the retrieval accuracy based on the assumption of a Lambertian surface reflectance is compared to the one based on the assumption of a typical BRF field.

Inversion of a physically based bidirectional reflectance model of vegetation

The challenge in using remote sensing data for the characterization of vegetation is to exploit reflectance measurements to infer biophysical and structural properties of the medium under study. In the present paper, the resolution of this problem was achieved, in the case of an homogeneous canopy, through a physically based bidirectional reflectance factor model. In a first validation phase, some inversions were performed against actual data obtained during the FIFE'89 experiment, and the retrieved parameters were found to be in good agreement with the corresponding in situ measurements. A sensitivity study was conducted afterward with synthetic data sets in order to identify and address some of the difficulties encountered while inverting such models, with a view to improving the accuracy of the retrievals and evaluating the inversion costs. The tackled points concerned not only the influence of the initial estimate values or the repercussions of errors made on the estimation of one of the variables, but also the angular sampling and the effect of simplifying assumptions made at the modeling step.