Bidirectional Reflectance Distribution Function Research Articles

Improving atmospheric CO2 retrieval based on the collaborative use of Greenhouse gases Monitoring Instrument and Directional Polarimetric Camera sensors on Chinese hyperspectral satellite GF5-02

ABSTRACT The Greenhouse Gas Monitoring Instrument (GMI) onboard the Chinese hyperspectral satellite GF5–02 can provide abundant observations of global atmospheric CO2, which plays an important role in climate research. CO2 retrieval precision is the key to determining the application value of the GMI. To reduce the influence of atmospheric scattering on retrieval, we combined the Directional Polarimetric Camera (DPC) data on the same satellite to improve the anti-interference ability of GMI CO2 retrieval and ensure its retrieval precision. To realize the reliability and feasibility of the collaborative use of the GMI and DPC, this paper designs the pointing registration method of the GMI based on coastline observations, the spatial resolution matching method and the collaborative cloud screening method of the GMI and DPC observations. Combined with the DPC, which supplied the spectral data and aerosol product, the retrieval ability of the coupled bidirectional reflectance distribution function CO2 retrieval (CBCR) method developed for GMI CO2 retrieval was improved, with the retrieval efficiency of CO2 products increasing by 27%, and the CO2 retrieval precision increasing from 3.3 ppm to 2.7 ppm. Moreover, collaborative use not only guaranteed the GMI’s ability to detect global and area CO2 concentration distribution characteristics, such as significant concentration differences between the Northern and Southern Hemispheres in winter and high CO2 concentrations in urban agglomeration areas caused by human activities, but also extended the GMI’s potential for monitoring anomalous events, such as the Tonga volcanic eruption.

Radiometric Cross-Calibration of Wide-Field-of-View Cameras Based on Gaofen-1/6 Satellite Synergistic Observations Using Landsat-8 Operational Land Imager Images: A Solution for Off-Nadir Wide-Field-of-View Associated Problems

The Gaofen-1 satellite is equipped with four wide-field-of-view (WFV) instruments, enabling an impressive spatial resolution of 16 m and a combined swath exceeding 800 km. These WFV images have shown their valuable applications across diverse fields. However, achieving accurate radiometric calibration is an essential prerequisite for establishing reliable connections between satellite signals and biophysical, as well as biochemical, parameters. However, observations with large viewing angles (>20°) pose new challenges due to the bidirectional reflectance distribution function (BRDF) effect having a pronounced impact on the accuracy of cross-radiation calibrations, especially for the off-nadir WFV1 and WFV4 cameras. To overcome this challenge, a novel approach was introduced utilizing the combined observations from the Gaofen-1 and Gaofen-6 satellites, with Landsat-8 OLI serving as a reference sensor. The key advantage of this synergistic observation strategy is the ability to obtain a greater number of image pairs that closely resemble Landsat-8 OLI reference images in terms of geometry and observation dates. This increased availability of matching images ensures a more representative dataset of the observation geometry, enabling the derived calibration coefficients to be applicable across various sun–target–sensor geometries. Then, the geometry angles and bidirectional reflectance information were put into a Particle Swarm Optimization (PSO) algorithm incorporating radiative transfer modeling. This PSO-based approach formulates cross-calibration as an optimization problem, eliminating the reliance on complex BRDF models and satellite-based BRDF products that can be affected by cloud contamination. Extensive validation experiments involving satellite data and in situ measurements demonstrated an average uncertainty of less than eight percent for the proposed cross-radiation calibration scheme. Comparisons of top-of-atmosphere (TOA) results calibrated using our proposed scheme, the previous traditional radiative transfer modeling using MODIS BRDF products for BRDF correction (RTM-BRDF) method, and official coefficients reveal the superior accuracy of our method. The proposed scheme achieves a 36.99% decrease in root mean square error (RMSE) and a 38.13% increase in mean absolute error (MAE) compared to official coefficients. Moreover, it achieves comparable accuracy to the RTM-BRDF method while eliminating the need for MODIS BRDF products, with a decrease in RMSE exceeding 14% for the off-nadir WFV1 and WFV4 cameras. The results substantiate the efficacy of the proposed scheme in enhancing cross-calibration accuracy by improving image match-up selection, efficiently removing BRDF effects, and expanding applicability to diverse observation geometries.

High Accuracy Solar Diffuser BRDF Measurement for On-Board Calibration in the Solar Reflective Band

In the solar reflective band, an on-board calibration method based on a solar diffuser (SD) can realize full aperture, full field of view, and end-to-end absolute radiometric calibration of optical remote sensors. The SD’s bidirectional reflectance distribution function (BRDF) is a key parameter that affects the accuracy of the on-board calibration. High-accuracy measurement of the SD BRDF is required in the laboratory before launch. Due to the uncertainty of the goniometer system, polarization effects, and other factors, the measurement uncertainty of the SD BRDF at large incident angles is much higher than that at a 0° incident zenith angle and 45° reflection zenith angle. In this paper, an absolute BRDF measurement facility is reported. The goniometric system consists of a high-brightness integrating sphere as a radiation source, a six-axis robot arm, and a large rotation stage. The measurement wavelength range was from 350 nm to 2400 nm. An improved data processing method based on the reciprocity theorem was proposed to reduce the measurement uncertainty of the SD BRDF at large incident angles. At an incident zenith angle of 75°, the improved data processing method reduced the measurement uncertainty of the SD BRDF by 52% at 410 nm to 480 nm, by 70% at 480 nm to 1000 nm, and by 20% at other bands compared to the absolute measurement method. The influence of the radiation source, goniometer system, detection system, and other factors on the measurement uncertainty are analyzed in this paper. The results show that the measurement uncertainty (coverage factor k = 2) of the SD BRDF was better than 1.04% at 350 nm to 410 nm, 0.60% at 410 nm to 480 nm, 0.43% at 480 nm to 1000 nm, and 0.86% at 1000 nm to 2400 nm.

Simulating snow-covered forest bidirectional reflectance by extending hybrid geometric optical–radiative transfer model

Accurate simulations of the bidirectional reflectance distribution function (BRDF) of snow-covered forests are crucial for retrieving snow and canopy properties, which affects the analysis of net radiation, radiative forcing, and climate change. The anisotropic reflectance of snow in conifer forests is greatly influenced by the high reflectance of snow and the complex travel pathways of radiation in 3D canopy structures, which complicates BRDF simulations. Although bidirectional reflection models have been widely established for snow-free forests, few studies have attempted to simulate the BRDF in snow-covered forests. Therefore, we propose a new snow-covered forest bidirectional reflectance (SFBR) model by coupling geometric optical and radiative transfer models, which includes a snow anisotropic reflectance model (ART), needle leaf optical properties model (LIBERTY), canopy radiative transfer model (4SAIL), and geometric optical model (GOST2). The SFBR model mainly focuses on conifer forests with snow backgrounds; it can simulate anisotropic reflectance spectra from 400 nm to 2500 nm and is the only model to fully consider discontinuous canopy distribution, topographic effects, and snow anisotropy. The SFBR model was validated by simulating the reflectance over snow-covered forests, the results of which showed good consistency with simulations of 3D scene model and remote sensing observations. The proposed SFBR model has the potential to quantify radiation interactions between the snow background and forest overstory, evaluate the sensitivity of canopy reflectance to snow and forest properties, and retrieve snow-covered forest parameters from satellite-observed reflectance data.

Research on the Directional Characteristics of the Reflectance of Oil-Contaminated Sea Ice

Remote sensing has been widely used for oil spill monitoring in open waters. However, research on remote sensing monitoring of oil spills in ice-infested sea waters (IISWs) is still scarce. The spectral characteristics of oil-contaminated sea ice (OCSI) and clean sea ice (CSI) and their differences are an important basis for oil spill detection using visible/near-infrared (VNIR) remote sensing. Such features and differences can change with the observation geometry, affecting the identification accuracy. In this study, we carried out multi-angle reflection observation experiments of oil-contaminated sea ice (OCSI) and proposed a kernel-driven bidirectional reflectance distribution function (BRDF) model, Walthall–Ross thick-Litransit-Lisparse-r-RPV (WaRoLstRPV), which takes into account the strong forward-scattering characteristics of sea ice. We also analyzed the preferred observation geometry for oil spill monitoring in IISWs. In the validation using actual measured data, the proposed WaRoLstRPV performed well, with RMSEs of 0.0031 and 0.0026 for CSI and OCSI, respectively, outperforming the commonly used kernel-driven BRDF models, Ross thick-Li sparse (R-LiSpr), QU-Roujean (Qu-R), QU-Lisparse R-r-RPV (Qu-LiSpr-RrRPV), and Walthall (Wa). The observation geometry with a zenith angle around 50° and relative azimuth ranging from 250° to 290° is preferred for oil spill detection in IISWs.

Wave optics approach to solar cell BRDF modeling with experimental results.

Light curve analysis is often used to discern information about satellites in geosynchronous orbits. Solar panels, comprising a large part of the satellite's body, contribute significantly to these light curves. Historically, theoretical bidirectional reflectance distribution functions (BRDFs) have failed to capture key features in the scattered light from solar panels. In recently published work, a new solar cell BRDF was developed by combining specular microfacet and "two-slit" diffraction terms to capture specular and periodic/array scattering, respectively. This BRDF was experimentally motivated and predicted many features of the solar cell scattered irradiance. However, the experiments that informed the BRDF were limited to a single laser wavelength, single beam size, and single solar cell sample. In addition, the BRDF was not physics based and therefore, physical insight into what causes certain features in the scattered irradiance was not evident. In this work, we examine solar cell scattering from first principles and derive a simple physics-based expression for the scattered irradiance. We analyze this expression and physically link terms to important scattering features, e.g., out-of-plane phenomena. In addition, we compare our model with experimental data and find good agreement in the locations and behaviors of these features. Our new model, being more predictive by nature, will allow for greater flexibility and accuracy when modeling reflection from solar cells in both real-world and experimental situations.

Bidirectional reflectance distribution function measurements of the Winchcombe meteorite using the Visible Oxford Space Environment Goniometer

AbstractA laboratory study was performed using the Visible Oxford Space Environment Goniometer in which the broadband (350–1250 nm) bidirectional reflectance distribution function (BRDF) of the Winchcombe meteorite was measured, across a range of viewing angles—reflectance: 0°–70°, in steps of 5°; incidence: 15°, 30°, 45°, and 60°; and azimuthal: 0°, 90°, and 180°. The BRDF dataset was fitted using the Hapke BRDF model to (1) provide a method of comparison to other meteorites and asteroids, and (2) to produce Hapke parameter values that can be used to extrapolate the BRDF to all angles. The study deduced the following Hapke parameters for Winchcombe: w = 0.152 ± 0.030, b = 0.633 ± 0.064, and hS = 0.016 ± 0.008, demonstrating that it has a similar w value to Tagish Lake (0.157 ± 0.020) and a similar b value to Orgueil (0.671 ± 0.090). Importantly, the surface profile of the sample was characterized using an Alicona 3D® instrument, allowing two of the free parameters within the Hapke model φ and , which represent porosity and surface roughness, respectively, to be constrained as φ = 0.649 ± 0.023 and = 16.113° (at 500 μm size scale). This work serves as part of the characterization process for Winchcombe and provides a reference photometry dataset for current and future asteroid missions.

Satellite Optical Brightness

The apparent brightness of satellites is calculated as a function of satellite position as seen by a ground-based observer in darkness. Both direct illumination of the satellite by the Sun as well as indirect illumination due to reflection from the Earth are included. The reflecting properties of the satellite components and of the Earth must first be estimated (the Bidirectional Reflectance Distribution Function, or BRDF). The reflecting properties of the satellite components can be found directly using lab measurements or accurately inferred from multiple observations of a satellite at various solar angles. Integrating over all scattering surfaces leads to the angular pattern of flux from the satellite. Finally, the apparent brightness of the satellite as seen by an observer at a given location is calculated as a function of satellite position. We develop an improved model for reflection of light from Earth’s surface using aircraft data. We find that indirectly reflected light from Earth’s surface contributes significant increases in apparent satellite brightness. This effect is particularly strong during civil twilight. We validate our approach by comparing our calculations to multiple observations of selected Starlink satellites and show significant improvement on previous satellite brightness models. Similar methodology for predicting satellite brightness has already informed mitigation strategies for next-generation Starlink satellites. Measurements of satellite brightness over a variety of solar angles widens the effectiveness of our approach to virtually all satellites. We demonstrate that an empirical model in which reflecting functions of the chassis and the solar panels are fit to observed satellite data performs very well. This work finds application in satellite design and operations, and in planning observatory data acquisition and analysis.

Improvement of pBRDF Model for Target Surface Based on Diffraction and Transmission Effects

The polarised Bidirectional Reflectance Distribution Function (pBRDF) model relates the properties of target materials to the polarisation information of the incident and reflected light. The Priest–Germer (P-G) model was the first strictly pBRDF model to be officially released; however, some shortcomings remain. In this study, we first analyse the assumption framework of the P-G model, analyse the assumption framework to determine the imperfections in the framework, supplement the boundary conditions of the model for diffraction and transmission effects, and propose and construct a polarised pBTDF model based on the existing P-G model and parameter inversion; the output results of the model are compared with the experimental data through simulation. The results show that the intensity relative error and Degree of Linear Polarisation relative error of the target can be reduced by more than 40%, using the improved model, proving its accuracy and precision.

Tunnel lighting calculation model based on bidirectional reflectance distribution function: Considering the dynamic changes in light transmittance in road tunnels

The current tunnel lighting calculation method does not completely consider the dynamic change in light transmittance in a tunnel and reflection characteristics of the tunnel sidewall. Thus, the measured and calculated values of road surface illuminance present a considerable difference, which seriously affects the tunnel lighting design and quality evaluation. To overcome this limitation, in this study, in addition to theoretical analysis and measured experiments, the changing trend in light attenuation is analyzed and a light attenuation model is established. Next, a bidirectional reflectance distribution function that can accurately reflect the reflection characteristics of tunnel sidewall is combined with the light attenuation model and calculation steps based on optical theory to construct a tunnel lighting calculation model considering light transmittance (TLCM-LT). Finally, theoretical calculation and actual measurement verification are conducted via an experimental tunnel simulation, and the results obtained using TLCM-LT are compared with the values calculated based on the Commission Internationale de l'éclairage (CIE), Illuminating Engineering Society of North America (IESNA), and Industry Standards of the People’s Republic of China (JTG) specifications. The results show that the average relative error of the TLCM-LT in a smoke-free environment is 1.31 %. In the presence of smoke, the average relative error of the model fluctuates in the range of 2.19–6.8 % with the change in light transmittance in the tunnel, which is significantly lower than the errors obtained based on the CIE, IESNA, and JTG specifications. Compared with a cement sidewall, the average increase rates in road surface illuminance and illuminance uniformity of the highly diffuse reflective sidewall are 23.21 and 3.65 %, respectively.

A Modified BRDF Model Based on Cauchy-Lorentz Distribution Theory for Metal and Coating Materials

This paper presents a modified Bidirectional Reflectance Distribution Function (BRDF) model based on the Cauchy–Lorentz distribution that accurately characterizes the reflected energy distribution of typical materials, such as metals and coatings in hemispherical space. The proposed model overcomes the problem of large errors in classical models when detecting angles far away from the specular reflection angle by dividing the reflected light into specular reflection, directional diffuse reflection, and ideal diffuse reflection components. The newly added directional diffuse reflection component is represented by the Cauchy–Lorentz distribution, and its parameters are directly obtained from experimental measurement curves without distribution fitting. Surface morphology and model parameters are determined through measurements, and the comparison between simulation and actual measurement results shows that the modified BRDF model is in excellent agreement with the measured data. The proposed model not only achieves higher accuracy and universality, but it also represents a significant advancement in the field of BRDF modeling research. Its contributions have profound implications for advancing the state of the art in BRDF modeling, as well as having a broader impact on computer graphics and computer vision domains.

Dependence of the Bidirectional Reflectance Distribution Function Factor ƒ′ on the Particulate Backscattering Ratio in an Inland Lake

The bidirectional reflectance distribution function (BRDF) factor ƒ′ provides a bridge between the inherent and apparent optical properties (IOPs and AOPs) of inland waters. The previous BRDF studies focused on ocean waters, while few studies examine inland waters. It is meaningful to improve the theory of remote sensing of water surface and the accuracy of image derivation in inland waters. In this study, radiative transfer simulation was applied to calculate the ƒ′ values using appropriate IOPs based on in situ combined with realistic boundary conditions (N = 11,232). This study shows that ƒ′ factor varied over the range of 0.33–16.64 in Lake Nansihu, a finite depth water, higher than the range observed for the ocean (0.3–0.6). Our results demonstrate that the factor ƒ′ depends on not only solar zenith angle (θs) but also the average number of collisions (n−) and particulate backscattering ratio (b~bp). The ƒ′ factor shows a continuous geometric increase as the solar zenith angle increases at 400–650 nm but is relatively insensitive to solar angle in the 650–750 nm range in which ƒ′ increases as b~bp and n− decreases. To account for these findings, two empirical models for ƒ′ factor as a function of θs, n− and b~bp are proposed in various spectral wavelengths for Lake Nansihu waters. Our results are crucial for obtaining Hyperspectral normalized reflectance or normalized water-leaving radiance and improving the accuracy of satellite products.

Material characterisation for optical wireless communication combining measurement and Monte Carlo simulations

AbstractA method is proposed for optical characterisation of materials, which is a very important input for realistic channel simulation based on Monte‐Carlo Ray‐Tracing algorithms. This original approach consists first of all in carrying out some measurements of the optical power received after propagation in the environment containing the materials sought, using a simple and low‐cost experimental setup. In a second step, this approach is based on an optimization algorithm. It takes as input the optical power measurements made, associated with the parameters of the measurement environment, such as the positions or properties of the sensors. This algorithm searches for the parameters of the material reflection models, minimising the difference between the optical measurement and the simulation. Two cost functions are studied to perform this search and showed that the correlation measure is the more robust one. To avoid uncertainties in the real input data, this approach is discussed using only a virtual configuration with well‐controlled input data and thus a virtual measurement obtained by simulation. The results show that this method produces a correct estimate of the Bidirectional Reflectance Distribution Function (BRDF) albedos, provided that the chosen BRDF models correspond well to the reflection behaviour of the materials, and that the materials have a significant influence on the measured optical power.

Exploiting Local Shape and Material Similarity for Effective SV-BRDF Reconstruction from Sparse Multi-Light Image Collections

We present a practical solution to create a relightable model from small Multi-light Image Collections (MLICs) acquired using standard acquisition pipelines. The approach targets the difficult but very common situation in which the optical behavior of a flat, but visually and geometrically rich object, such as a painting or a bas relief, is measured using a fixed camera taking a limited number of images with a different local illumination. By exploiting information from neighboring pixels through a carefully-crafted weighting and regularization scheme, we are able to efficiently infer subtle and visually pleasing per-pixel analytical Bidirectional Reflectance Distribution Functions (BRDFs) representations from few per-pixel samples. The method has a low memory footprint and is easily parallelizable. We qualitatively and quantitatively evaluated it on both synthetic and real data in the scope of image-based relighting applications.

One-shot colored reflectance direction field imaging system for optical inspection.

Detecting microscale defects on the surface of an object is often difficult with conventional cameras. Microscale defects are known to greatly affect the bidirectional reflectance distribution function (BRDF) of light rays reflected from the surface. Therefore, an imaging system for capturing the reflectance direction field by color mapping using a multicolor filter placed in front of an imaging lens is proposed, which can have a simple structure. From the color variations of light rays passing through several different color regions of the multicolor filter, this imaging system can detect the extent of broadening of the BRDF. The effectiveness of the imaging system for optical inspection is experimentally validated by testing it on a plastic surface that has a shallow scratch with a depth of a few micrometers.

Investigating the optical translucency of Spectralon using BSSRDF measurements.

Translucent materials have the property of reflecting light beyond the illumination point due to subsurface light propagation in the material. These reflectance properties can be characterized using the bidirectional scattering-surface reflectance distribution function (BSSRDF), a radiometric quantity that is a function of spatial, angular, spectral, and polarization parameters. At very small scales, we have observed that Spectralon, a commercial material widely used as a diffuse reflectance calibration standard, can be regarded as translucent. This can generate measurement errors and limit Spectralon's reliability as a calibration artefact for instruments that measure optical quantities on very small surfaces. To characterize the translucent properties of Spectralon, we have measured its BSSRDF using an experimental setup based on a goniospectrophotometer with a spatial scanning system for detection. In the present study, we show that Spectralon cannot be considered an opaque material at small scales (below 1mm). For instrument measuring on small areas, Spectralon can be used for calibration only when the illumination area and the observation area differ by more than 1mm in radius.

Degree of polarization model based on a modified three-component pBRDF.

The polarized bidirectional reflectance distribution function (pBRDF) model not only can quantify the radiation intensity, but also can effectively describe the polarization characteristics of the scattered light on the target surface. In this paper, a modified three-component pBRDF model is proposed, which considers the reflection process to be composed of specular reflection, multiple reflection, and volume scattering. Key parameters such as the distribution of the microfacet, geometrical attenuation factor, multiple reflection, and volume scattering, are modified. The degree of polarization model is derived based on the new pBRDF, when the incident light is natural light. The degree of polarization of four coating fabric samples is measured by a multi-angle polarization instrument, and the undetermined coefficients in the model are inverted based on the experimental data. A comparison of the measured and modeled results at a wavelength of 720nm reveals that the model can accurately describe the spatial distribution of polarization characteristics of four samples and control the errors within 0.06, 0.1, 0.04, and 0.09, which provides a theoretical basis for polarization detection and polarization image simulation.

Development of the Ames Global Hyperspectral Synthetic Data Set: Surface Bidirectional Reflectance Distribution Function

AbstractThis study introduces the Ames Global Hyperspectral Synthetic Data set (AGHSD), in particular the surface bidirectional reflectance distribution function (BRDF) product, to support the NASA Surface Biology and Geology (SBG) mission development. The data set is generated based on the corresponding multispectral BRDF products from NASA's MODIS satellite sensor. Based on theories of radiative transfer in vegetation canopies, we derive a simple but robust relationship that indicates that the hyperspectral surface BRDF can be accurately approximated as a weighted sum of the soil surface reflectance, the leaf single albedo, and the canopy scattering coefficient, where the weights or coefficients are spectrally invariant and thus readily estimated from the multispectral MODIS products. We validate the algorithm with simulations by a Monte Carlo Ray Tracing model and find the results highly consistent with the theoretic derivation. Using reflectance spectra of soil and vegetation derived from existing spectral libraries, we apply the algorithm to generate the AGHSD BRDF product at 1 km and 8‐day resolutions for the year of 2019. The data set is biogeochemically and biogeophysically coherent and consistent, and serves the goal to support the SBG community in developing sciences and applications for the future global imaging spectroscopy mission.

Improving Crop Mapping by Using Bidirectional Reflectance Distribution Function (BRDF) Signatures with Google Earth Engine

Recent studies have demonstrated the potential of using bidirectional reflectance distribution function (BRDF) signatures captured by multi-angle observation data to enhance land cover classification and retrieve vegetation architectures. Considering the diversity of crop architectures, we proposed that crop mapping precision may be enhanced by using BRDF signatures. We compared the accuracy of four supervised machine learning classifiers provided by the Google Earth Engine (GEE), namely random forest (RF), classification and regression trees (CART), support vector machine (SVM), and Naïve Bayes (NB), using the moderate resolution imaging spectroradiometer (MODIS) nadir BRDF-adjusted reflectance data (MCD43A4 V6) and BRDF and albedo model parameter data (MCD43A1 V6) as input. Our results indicated that using BRDF signatures leads to a moderate improvement in classification results in most cases, compared to using reflectance data from a single nadir observation direction. Specifically, the overall validation accuracy increased by up to 4.9%, and the validation kappa coefficients increased by up to 0.092. Furthermore, the classifiers were ranked in order of accuracy, from highest to lowest: RF, CART, SVM, and NB. Our study contributes to the development of crop mapping and the application of multi-angle observation satellites.

Goniometer in the air: Enabling BRDF measurement of crop canopies using a cable-suspended plant phenotyping platform

The Bidirectional Reflectance Distribution Function (BRDF) quantifies the distribution of the spectral reflectance of a target surface at various viewing and illumination angles. In-field measurement of the BRDF of vegetation canopies improves the characterization of off-nadir measurements and informs radiative transfer models of canopy reflectance, where the Lambertian assumption does not hold. However, current field goniometers are unable to measure BRDF efficiently, especially for tall vegetation across the growing season because of the limitations of clearance, field accessibility, and flexibility of the sensor field of view. In this study, we explored the potential of using a large-scale cable-suspended field phenotyping system to quantify BRDF of the canopy reflectance at selected bands and Vegetation Indices (VIs) of maize and soybean canopies. The system performance benefited from the following conditions: no crop damage, full-season field accessibility of tall canopies, automatic measurement, and accurate positioning. Correlation analysis shows that a strong correlation exists among the reflectance, VIs, and the Green Pixel Fraction. The hemispheric distributions of the spectral reflectance at selected bands and VIs were quantified at multiple dates. Hot spots were observed in the backscatter direction at visible and near-infrared (NIR) bands at the largest sensor zenith angle around the Solar Principal Plane (SPP). In contrast, cold spots of the Normalized Difference Vegetation Index (NDVI) and its related VIs were observed in the backscatter direction around solar zenith angles. The row effect was found for the maize canopy at the NIR band and the Near-infrared reflectance of vegetation (NIRv). NDVI had the lowest anisotropy index values among investigated bands and VIs. This system could be further leveraged to generate rapid and detailed BRDF data at a high spatiotemporal resolution using multiple sensors.