Evaluation of directional normalization methods for Landsat TM/ETM+ over primary Amazonian lowland forests

With the rapid development of hyperspectral applications using unmanned aerial vehicles (UAVs), the traditional assumption that ground objects exhibit Lambertian reflectance is no longer sufficient to meet the high-precision requirements for quantitative inversion and airborne hyperspectral data applications. Therefore, it is necessary to establish a hyperspectral bidirectional reflectance distribution function (BRDF) model suitable for the area of imaging. However, obtaining multi-angle information from UAV push-broom hyperspectral data is difficult. Achieving uniform push-broom imaging and flexibly acquiring multi-angle data is challenging due to spatial distortions, particularly under heightened roll or pitch angles, and the need for multiple flights; this extends acquisition time and exacerbates uneven illumination, introducing errors in BRDF model construction. To address these issues, we propose leveraging the advantages of multi-spectral cameras, such as their compact size, lightweight design, and high signal-to-noise ratio (SNR) to reconstruct hyperspectral multi-angle data. This approach enhances spectral resolution and the number of bands while mitigating spatial distortions and effectively captures the multi-angle characteristics of ground objects. In this study, we collected UAV hyperspectral multi-angle data, corresponding illumination information, and atmospheric parameter data, which can solve the problem of existing BRDF modeling not considering outdoor ambient illumination changes, as this limits modeling accuracy. Based on this dataset, we propose an improved Walthall model, considering illumination variation. Then, the radiance consistency of BRDF multi-angle data is effectively optimized, the error caused by illumination variation in BRDF modeling is reduced, and the accuracy of BRDF modeling is improved. In addition, we adopted Transformer for spectral reconstruction, increased the number of bands on the basis of spectral dimension enhancement, and conducted BRDF modeling based on the spectral reconstruction results. For the multi-level Transformer spectral dimension enhancement algorithm, we added spectral response loss constraints to improve BRDF accuracy. In order to evaluate BRDF modeling and quantitative application potential from the reconstruction results, we conducted comparison and ablation experiments. Finally, we solved the problem of difficulty in obtaining multi-angle information due to the limitation of hyperspectral imaging equipment, and we provide a new solution for obtaining multi-angle features of objects with higher spectral resolution using low-cost imaging equipment.

Land surface albedo retrieval via kernel-based BRDF modeling: I. Statistical inversion method and model comparison

A statistical inversion method is first presented in support to the application of kernel-based BRDF (bi-directional reflectance distribution function) models for the calculation of the surface albedo. We present an operational procedure for the inversion of a kernel-driven BRDF model and further albedo retrieval to be applicable to the SEVIRI/MSG reflectance measurements. The processing steps applied to space-borne POLDER sensor data were as follows: (1) quality control, (2) accumulation of a priori information on model coefficients of directional hemispherical reflectance, (3) implementation of the BRDF model inversion methods based on the biased estimation instead of usual non-biased least solution, which has too big a variance in this case. The data control procedure consists both in filtering inputs of reflectance data and output of model coefficients based on analysis criteria determined by Fisher statistics. A multi-criteria procedure follows considering in particular the shape of the reflectance angular signature: (1) T-statistics, (2) the bowl shape index, (3) the dome shape index, (4) the white sky albedo (bi-hemispherical reflectance), (5) the black sky albedo variance (directional hemispherical reflectance). The procedure is applied to POLDER data corresponding to the 16 classes of IGBP land cover classification. The statistical results include mean values and covariance matrix for the spectral BRDF model coefficients.

A new bidirectional reflectance distribution function (BRDF) model of earth objects is established for the calibration of remote sensing image by the national metrology equipment. This model colligates the solar radiance, the atmosphere status, the object type, and the space camera parameter, etc. The output data of this model is the enter radiance data for the space camera. The remote sensing image can be appeared more “true” through this calibration. A kind of ground glass for architecture is measured and the correspond remote sensing image is simulated. After calibrated, the chromatism of this image is improved by 2 and the luminance contrast of that is improved by 3. OCIS codes: 120.0120, 280.0280, 290.0290. doi: 10.3788/COL201210.S11201. The aero camera receives different radiance and emit different wavelength light at the same time for the generation of the remote sensing image. Bidirectional reflectance distribution function (BRDF) combine these incident light, reflect light, and scatter light in one variable and it is the best variable to describe the space radiance performance of objects. The complex BRDF model for remote sensing image is based on the BRDF fundament equation, and colligates the solar radiance, the atmosphere status, the object type, and the space camera parameter, etc. In this model, the solar radiance and it’s change through atmosphere are considered in the incident variable of BRDF, and the proximity effect of atmosphere and the parameters of aero camera are considered for the reflect variable of BRDF, for example, the BRDF performance of aerosol [1] . Then the BRDF value of some typical objects are measured and calibrated. The absolute BRDF value are achieved and simulated using the complex BRDF model. Thus the remote sensing image which is calibrated is achieved. The whole procedure is shown in Fig.1. The basement equation of the complex BRDF model is established by the definition of BRDF as shown in Eq.(1) which defined as the ration of the reflect radiance and the incident irradiance on the appearance of the objects [2] . In Eq.(1), �i represents the incident zenith angle, 'i represents the incident azimuth angle, �r represents the incident zenith angle, 'r represents the incident azimuth angle, � the represents wavelength, Lr(�i,'i,�r,'r,�) represents the reflect radiance at specified angles and wavelength, Ei(�i,'i,�) represents the incident irradiance at specified angles and wavelength. Equation (1) is true in the controllable incident solid angle d i, which is shown in Fig.2. Under this condition, Lr(�i,'i,�r,'r,�) can be simplified to Lr(�r,'r,�). fr(�i,'i,�r,'r,�) = dLr(�i,'i,�r,'r,�) dEi(�i,'i,�) = Lr(�r,'r,�) Ei(�i,'i,�) .

Land surface albedo may be derived from the satellite data through the estimation of a bidirectional reflectance distribution function (BRDF) model and angular integration. However many BRDF models do not consider explicitly the topography. In rugged terrain, the topography influences the observed surface reflectance, and varies the distribution of downward irradiance, so that BRDF and angular integration will change from flat area to rugged area. This paper details an approach to take into account the topography influences on the surface reflectance, and outlines an algorithm considering the topographic factor. This algorithm makes use of a Digital Elevation Model (DEM) to correct the incidence angle and the reflection angle in a BRDF model, and performs the angular integration based on the different distribution of downward irradiance between a flat area and a rugged area. This approach was applied to HJ-1A/B data and the results were compared with the ones obtained with the algorithm designed for NASA's MODIS data.

Surface Albedos and Angle-Corrected NDVI from AVHRR Observations of South America

When a satellite sensor with a large field of view and wide swath is calibrated, it is not easy to obtain the image when the calibration site is located precisely at the nadir position. If the location of a calibration site is at an off-nadir position in the image, calibration errors will be caused by the inconsistent observation angle between the sensor view and the ground measurement view. The bidirectional reflectance distribution function (BRDF) model plays an important role in solving this problem. In this study, a BRDF measurement system based on an unmanned aerial vehicle (UAV) is developed. This system has the capability of measuring angular data with observation azimuth angle ranging from 0 deg to 360 deg with an angle interval of 30 deg, and observation zenith angle ranging from 0 deg to 50 deg with an angle interval of 10 deg. The directional data of the Dunhuang calibration site were measured using the UAV BRDF measuring system at different solar zenith and azimuth angles, and the spatiotemporal distribution characteristic of forward- and backward-scattering of Dunhuang calibration site was analyzed. A Ross–Li BRDF model, built using measurement data, is used to calculate the directional surface reflectance under any observation geometry of solar and satellite. These calculations are applied to correct the calibration data of the CBERS-04 WFI sensor. Results show that the BRDF model significantly improves the calibration accuracy, especially in the case of large observation angles.

Row sowing is a basic crop sowing method in China, and thus an accurate Bidirectional Reflectance Distribution Function (BRDF) model of row crops is the foundation for describing the canopy bidirectional reflectance characteristics and estimating crop ecological parameters. Because of the macroscopically geometric difference, the row crop is usually regarded as a transition between continuous and discrete vegetation in previous studies. Were row treated as the unit for calculating the four components in the Geometric Optical model (GO model), the formula would be too complex and difficult to retrieve. This study focuses on the microscopic structure of row crops. Regarding the row crop as a result of leaves clumped at canopy scale, we apply clumping index to link continuous vegetation and row crops. Meanwhile, the formula of clumping index is deduced theoretically. Then taking leaf as the basic unit, we calculate the four components of the GO model and develop a BRDF model for continuous vegetation, which is gradually extended to the unified BRDF model for row crops. It is of great importance to introduce clumping index into BRDF model. In order to evaluate the performance of the unified BRDF model, the canopy BRDF data collected in field experiment, “Watershed Allied Telemetry Experiment Research (WATER)”, from May 30th to July 1st, 2008 are used as the validation dataset for the simulated values. The results show that the unified model proposed in this paper is able to accurately describe the non-isotropic characteristics of canopy reflectance for row crops. In addition, the model is simple and easy to retrieve. In general, there is no irreconcilable conflict between continuous and discrete vegetation, so understanding their common and individual characteristics is advantageous for simulating canopy BRDF. It is proven that the four components of the GO model is the basic motivational factor for bidirectional reflectance of all vegetation types.

Adaptive regularized filtering for BRDF model inversion and land surface albedo retrieval based on spectrum cutoff technique

In this study we show that multiangle remote sensing is useful for increasing the accuracy of vegetation community type mapping in desert regions. Using images from the National Aeronautics and Space Administration (NASA) Multiangle Imaging Spectroradiometer (MISR), we compared roles played by Bidirectional Reflectance Distribution Function (BRDF) model parameters with those played by topographic parameters in improving vegetation community type classifications for the Jornada Experimental Range and the Sevilleta National Wildlife Refuge in New Mexico, USA. The BRDF models used were the Rahman–Pinty–Verstraete (RPV) model and the RossThin‐LiSparseReciprocal (RTnLS) model. MISR nadir multispectral reflectance was considered as baseline because nadir observation is the most basic remote sensing observation. The BRDF model parameters and the topographic parameters were considered as additional data. The BRDF model parameters were obtained by inversion of the RPV model and the RTnLS model against the MISR multiangle reflectance data. The results of 32 classification experiments show that the BRDF model parameters are useful for vegetation mapping; they can be used to raise classification accuracies by providing information that is not available in the spectral‐nadir domain, or from ancillary topographic parameters. This study suggests that the Moderate Resolution Imaging Spectroradiometer (MODIS) and MISR BRDF model parameter data products have great potential to be used as additional information for vegetation mapping.

Previous work has successfully demonstrated the application of a priori knowledge to bidirectional reflectance distribution function (BRDF) model inversion. The a priori knowledge used in the tests is the general knowledge which can be applied to all land cover types. One of the advantages of general knowledge is that it can be applied independently and doesn't need more information. However, the inversion accuracy may be low, compared to results using correct specific knowledge. This paper mainly focuses on the accumulation of a priori knowledge for the linear BRDF model in both general and specific realms. The authors first provide some specific knowledge based on ground and spaceborne BRDF measurements for BRDF model parameters.

Accurately evaluating the quality of bidirectional reflectance distribution function (BRDF) models is essential for photo-realistic rendering. Traditional BRDF-space metrics often employ numerical error measures that fail to capture perceptual differences evident in rendered images. In this paper, we introduce the first perceptually informed neural quality metric for BRDF evaluation that operates directly in BRDF space, eliminating the need for rendering during quality assessment. Our metric is implemented as a compact multi-layer perceptron (MLP), trained on a dataset of measured BRDFs supplemented with synthetically generated data and labelled using a perceptually validated image-space metric. The network takes as input paired samples of reference and approximated BRDFs and predicts their perceptual quality in terms of just-objectionable-difference (JOD) scores. We show that our neural metric achieves significantly higher correlation with human judgments than existing BRDF-space metrics. While its performance as a loss function for BRDF fitting remains limited, the proposed metric offers a perceptually grounded alternative for evaluating BRDF models.

Spectral albedo may be derived from atmospherically corrected, cloud-cleared multiangular reflectance observations through the inversion of a bidirectional reflectance distribution function (BRDF) model and angular integration. This paper outlines an algorithm suitable for this task that makes use of kernel-based BRDF models. Intrinsic land surface albedos are derived, which may be used to derive actual albedo by taking into account the prevailing distribution of diffuse skylight. Spectral-to-broadband conversion is achieved using band-dependent weighting factors. The validation of a suitable BRDF model, the semiempirical Ross-Li (reciprocal RossThick-LiSparse) model and its performance under conditions of sparse angular sampling and noisy reflectances are discussed, showing that the retrievals obtained are generally reliable. The solar-zenith angle dependence of albedo may be parameterized by a simple polynomial that makes it unnecessary for the user to be familiar with the underlying BRDF model. The algorithm given is that used for the production of a BRDF/albedo standard data product from NASA's EOS-MODIS sensor, for which an at-launch status is provided. Finally, the algorithm is demonstrated on combined AVHRR and GOES observations acquired over New England, from which solar zenith angle-dependent albedo maps with a nominal spatial resolution of 1 km are derived in the visible band. The algorithm presented may be employed to derive albedo from space-based multiangular measurements and also serves as a guide for the use of the MODIS BRDF/albedo product.

Scene simulation has proved to be a valuable tool for analysing the images perceived by visible and infrared imaging systems. Accurate scene simulation requires accurate incorporation of the optical properties of all the materials within a scene, with reflectance incorporated with the bidirectional reflectance distribution function (BRDF) and emission incorporated through the directional emissivity or hemispherical directional reflectance (HDR). This paper compares the fit of various parameterised models to experimental BRDF data from a variety of surfaces representing the extremes of material properties found in the environment. One of the main aims is to infer the accuracy and validity of an in-house BRDF model called Mopaf using data representative of different sorts of isotropically reflecting materials. Where appropriate physical and semiempirical models and a novel parameter based BRDF model were compared with Mopaf and with BRDF data from a Surface Optics Corporation SOC-200 instrument. It was concluded that Mopaf might not be reliable for all the angular BRDF data, especially specularly reflecting surfaces or grazing incidence data. Likewise, the other BRDF models investigated tended to be limited to a range of physical conditions such as only diffuse reflection or to a range of surface roughness. It was shown that the proposed new BRDF model was more generally applicable from the visible to infrared wavelengths, over a wide range of reflection angles and for different sorts of surface material.

Real-time mid-wavelength infrared scene rendering with a feasible BRDF model