Bidirectional Reflectance Distribution Function Retrieval Research Articles

Analyzing GOES-R ABI BRDF-adjusted EVI2 time series by comparing with VIIRS observations over the CONUS

The Advanced Baseline Imager (ABI) instruments onboard the Geostationary Operational Environmental Satellite-R series (GOES-R) provide new opportunities to expand their applications in global terrestrial ecology and environmental sciences. It offers a 5–15 min temporal resolution and 0.5–1 km spatial resolution that greatly benefit those environmental applications where spatial resolution can be compromised in favor of more frequent imagery. However, ABI captures reflected radiation at varying sun positions throughout the day, causing significant diurnal variations. The varying off-nadir view across ABI full disk presents further challenges for generating comparable and consistent vegetation observations, such as the two-band Enhanced Vegetation Index (EVI2). To address these challenges, three Bidirectional Reflectance Distribution Function (BRDF) models (RossThick-LiSparse-Reciprocal (RTLSR), Walthall, and Tian models) are examined to generate high-quality BRDF-adjusted EVI2 based on GeoNEX GOES-16 ABI surface reflectance. The ABI BRDF retrievals are then systematically compared to the corresponding VIIRS (Visible Infrared Imaging Radiometer Suite) Nadir BRDF-Adjusted Reflectance (NBAR) products (VNP43 V001) retrieved from RTLSR model. The viewing geometry effects are further analyzed over various land cover types using time series analysis, Pearson Correlation Coefficients (PCC), and Relative Difference (RD) between ABI and VIIRS high-quality time series inversions. The results indicate that the RTLSR model fits well the diurnal ABI observed reflectance and EVI2 values (even for the hot-spot phenomena), and outperforms the Walthall and Tian models for generating ABI high-quality BRDF retrievals with consistently low Root Mean Square Error (RMSE<0.02) and Weight of Determination (WoD < 0.5) across the CONUS. The annual proportion of ABI high-quality BRDF retrievals with a 3-day moving window using the RTLSR model is spatially and statistically comparable to that of VIIRS NBAR with a 16-day retrieval period. Compared to the time series of high-quality VIIRS NBAR EVI2, the BRDF adjusted ABI EVI2 time series still show considerable impacts of view zenith angles over evergreen forest, shrubland, and savanna pixels. However, such impacts are not significant over spatially homogeneous pixels such as cropland and deciduous forests. These results emphasize the need for BRDF correction when considering the impact of off-nadir observations from geostationary satellites on vegetation monitoring. They also demonstrate the advantages of geostationary satellites in capturing rapid changes in greenness trajectories.

Multisensor Data Synergy of Terra-MODIS, Aqua-MODIS, and Suomi NPP-VIIRS for the Retrieval of Aerosol Optical Depth and Land Surface Reflectance Properties

A novel multisensor synergy method, using data from Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua as well as Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership, is presented to retrieve optical atmosphere-surface properties. By adopting three-sensor observations’ synergy, the proposed method can grasp the multitemporal characteristics of aerosol optical depth (AOD) and the multidirectional characteristics of surface reflectance. In addition, the bidirectional reflectance distribution function (BRDF) can be derived at daily scale by adopting the novel shape function constrained BRDF retrieval (SFCBR) method. The 550-nm AOD retrieval result of the proposed method is validated by AErosol RObotic NETwork (AERONET) measurement at Beijing, XiangHe, Noto, and Gwangju_GIST sites with $R^{2}$ equaling to 0.78, 0.75, 0.70, and 0.75, respectively. Compared with MODIS/VIIRS AOD official product, the proposed method shows higher coverage rate (especially in AERONET Beijing site with approximately 50% increase) with comparative accuracy. The expected error of the retrieved AOD from the proposed method is estimated as $\Delta \tau = \pm 0.05 \pm 0.24\tau $ . The correlation coefficients of BRDF-derived albedo time series between the proposed method and MODIS BRDF/Albedo product can reach up to 0.85 with an obvious improvement in temporal resolution by adopting an SFCBR method. The average relative differences of BRDF shape function between the retrieval result and MODIS BRDF/Albedo product in all directions equal to 0.026, 0.036, 0.037, and 0.016 in AERONET Beijing, XiangHe, Noto, and Gwangju_GIST sites, respectively.

An Iterative BRDF/NDVI Inversion Algorithm Based on &lt;italic&gt;A Posteriori&lt;/italic&gt; Variance Estimation of Observation Errors

Current bidirectional reflectance distribution function (BRDF) inversions using ordinary least squares (OLS) criterion can be easily contaminated by observations with residual cloud and undetected high aerosols, which leads to abrupt fluctuations in the normalized difference vegetation index (NDVI) time series. The OLS criterion assumes the noise has Gaussian distribution, which is often violated due to positive noise biases caused by clouds and high aerosols. A changing-weight iterative BRDF/NDVI inversion algorithm (CWI) based on a posteriori variance estimation of observation errors is presented to explicitly consider the asymmetrically distributed noise and observations with unequal accuracy in the BRDF retrieval. CWI employs a posteriori variance estimation and an NDVI-based indicator to iteratively adjust the weight of each observation according to its noise level. The validation results suggest CWI performs better than the Li–Gao and OLS approaches. The rmse was reduced from 0.074 to 0.028, and the relative error decreased from 13.4% to 3.8% at the U.S. Department of Agriculture Beltsville Agricultural Research Center site. Similarly, at the Harvard Forest site, the rmse was reduced from 0.086 to 0.031, and the relative error decreased from 9.5% to 2.7%. The average noise and relative noise of the CWI NDVI time series over ten EOS Land Validation Core Sites from 2003–2009 was smaller (0.028, 3.7%) than those of MOD13A2 (0.041, 5.2%), MYD13A2 (0.039, 4.9%) and MCD43B4 (0.030, 4.4%). The results demonstrate the robustness of the CWI approach in suppressing the influence of contaminated observations in BRDF retrievals by producing results that are less affected by undetected clouds and high aerosols.

Albedo and reflectance anisotropy retrieval from AVHRR operated onboard NOAA and MetOp satellites: Algorithm performance and accuracy assessment for Europe

In this study, the land surface albedo together with its Bidirectional Reflectance Distribution Function (BRDF) are retrieved for the years 2000 to 2012 from Local Area Coverage (LAC) surface reflectance data gathered by the Advanced Very High Resolution Radiometer (AVHRR) over Europe. For the retrieval the MODerate resolution Imaging Spectroradiometer (MODIS) BRDF/albedo processing scheme is employed. The comparatively high revisit frequency and high variability in angular sampling of the AVHRR sensors operated simultaneously onboard the different National Oceanic and Atmospheric Administration (NOAA) and Meteorological Operational (MetOp) satellites contribute substantially to the success and quality of BRDF retrieval. The performance of the BRDF model for AVHRR data is assessed by comparison to MODIS BRDF retrievals, and the AVHRR BRDF reflectance data is validated against BRDF reflectance data from MODIS and in situ data gathered at three field sites in Switzerland. The comparison shows that the AVHRR BRDF retrievals are of high quality across most of Europe. The higher angular sampling of AVHRR allows for more full model retrievals of best quality and generates less fill values compared to MODIS. For most of the years investigated, the absolute accuracy of the AVHRR albedos is found to be within 0.05 throughout the complete seasonal cycle with a minimum bias at the peak of the growing season. However, they systematically underestimate the field-measured albedos, predominantly in winter due to spatial scale mismatch in combination with site heterogeneity and because the expression for the calculation of satellite-based blue-sky albedo does not account for multiple scattering. A slightly increased underestimation also occurs during vegetation senescence, presumably because of the narrow to broadband conversion employing only two bands. Overall, the results confirm the potential of AVHRR to produce multi-decadal data sets of reflectance anisotropy and albedo for use in climate monitoring and modeling studies. This offers a promising and unique opportunity to produce a BRDF/albedo climate data record from AVHRR dating back to 1985.

The Normalization of Surface Anisotropy Effects Present in SEVIRI Reflectances by Using the MODIS BRDF Method

A modified version of the MODerate resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF) algorithm is presented for use in the angular normalization of surface reflectance data gathered by the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) aboard the geostationary Meteosat Second Generation (MSG) satellites. We present early and provisional daily nadir BRDF-adjusted reflectance (NBAR) data in the visible and near-infrared MSG channels. These utilize the high temporal resolution of MSG to produce BRDF retrievals with a greatly reduced acquisition period than the comparable MODIS products while, at the same time, removing many of the angular perturbations present within the original MSG data. The NBAR data are validated against reflectance data from the MODIS instrument and in situ data gathered at a field location in Africa throughout 2008. It is found that the MSG retrievals are stable and are of high-quality across much of the SEVIRI disk while maintaining a higher temporal resolution than the MODIS BRDF products. However, a number of circumstances are discovered whereby the BRDF model is unable to function correctly with the SEVIRI observations—primarily because of an insufficient spread of angular data due to the fixed sensor location or localized cloud contamination.

The Angular and Spectral Kernel Model for BRDF and Albedo Retrieval

Albedo may be derived from clear-sky remote-sensing images through inversion of a bidirectional reflectance distribution function (BRDF) model and angular integration. This paper proposes a new multi-angular and multi-spectral BRDF model (ASK Model) based on the kernel-driven conception and gives an effective algorithm for broadband albedo retrieval. By adding component spectra into kernels as prior known driven variables, the new model expresses BRDF as a linear combination of wavelength-independent kernel coefficients and kernels expressed as functions of both observation geometry and wavelength. In this way, the new model brings advantages in two aspects. On the one hand, for model inversion, the new angular and spectral kernels allow combination of observations not only at different viewing and illumination angles, but also at different wavebands, which give more reliable inversion results especially when the angular data are limited. On the other hand, different from traditional narrowband-to-broadband conversion, which gives empirical weights at several available bands, the new algorithm derives broadband albedo as a weighted linear combination of kernel integrations both in angular and wavelength domains. As model validation, ground-based measurements in Heihe Field Campaign have been chosen. Results show that the new model can accurately rebuild BRDF and derive broadband albedo. Furthermore, the new model and algorithm are demonstrated using CHRIS and EOS-MODIS data. The retrieved broadband albedos have been compared with MODIS BRDF/albedo product and the <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> measurements. Results show that the presented algorithm can be employed to retrieve broadband albedo from multisource satellite observations.

Calibration and characterization of a digital camera for bidirectional reflectance distribution function retrieval of vicarious calibration sites

This work describes the calibration and characterization of a commercial digital camera that is being evaluated as a potential inexpensive imaging system to measure the bidirectional reflectance factor of the test site surfaces that are used by the Remote Sensing Group for the ground-based vicarious calibration of airborne and spaceborne systems. The main advantage to using a lightweight digital camera in combination with a fisheye lens is that a test site can be simultaneously spatially and spectrally sampled with greater ease. The motivation for using an inexpensive, lightweight digital camera to measure surface bidirectional distribution factor is the ability to spatially sample many points on the ground. An additional benefit to such cameras is their ability to operate without the need for cooling, which was required on a previous camera system developed at the Remote Sensing Group. Laboratory measurements include the gain and offset, spectral responsivity, linearity, cosine response, and point spread function. The results show that the uniformity of the Nikon detector array ranges from 0.7%-1.2%. The spectral bandwidth is approximately 90 nm for the blue channel, and 110 nm for the green, and 75 nm for the red channel. Linearity measurements show that the camera responsivity is nonlinear due to the internal image-processing algorithms, and a logarithmic fitting function is suggested. Measurements of the camera response versus the angle of incidence reveal that the camera does not exhibit a cos4 falloff on detector irradiance as one would expect, which is another consequence of the internal algorithms.

Use of a Kalman filter for the retrieval of surface BRDF coefficients with a time-evolving model based on the ECOCLIMAP land cover classification

The goal of this study is to demonstrate the asset in using a Kalman filter to improve the spatial coherence and time consistency of surface Bidirectional Reflectance Distribution Function (BRDF) and albedo retrievals from moderate resolution sensor data sets. For this purpose, we use a simple surface model describing BRDF seasonal evolution for the land cover classes of the ECOCLIMAP database. The application of temporal composition windows used so far for BRDF retrieval is limited in regions characterized by a high frequency of cloud coverage, which induces a lot of gaps in the temporal series. Instead, the present method ensures a continuous production of surface BRDF parameters thanks to the Kalman filter recursive data processing. An application of the method is performed with SPOT/VEGETATION data over the western Africa equatorial region for the year 2003. Compared to presently available products from VEGETATION and MODIS instruments, this new approach allows to fill the gaps and improves the retrieved parameters time consistency. Another interesting possibility of the Kalman filter is the production of surface biophysical variables in quasi-real-time for applications that require a frequent update of the surface parameters.

Spectral Normalization and Fusion of Optical Sensors for the Retrieval of BRDF and Albedo: Application to VEGETATION, MODIS, and MERIS Data Sets

This paper aims at demonstrating the possibility of merging data from various medium-resolution spaceborne sensors to produce a consistent time series of surface bidirectional reflectance distribution function (BRDF) and albedo products. The spectral, directional, temporal, and spatial aspects of the multisensor fusion are presented. Emphasis is then given on the spectral normalization for the fusion of Medium Resolution Imaging Spectrometer Instrument (MERIS) data with Moderate Resolution Imaging Spectroradiometer (MODIS) and VEGETATION (VGT) data. Two methods are evaluated: a simple statistical method, which relies on a linear regression using all the available spectral bands, and a more innovative method called the spectral mode method, which is based on the restitution of the surface spectral signature by a combination of universal spectral functions. Analysis with Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral data and satellite products reveals that the spectral mode method is more efficient. This approach is used to merge top-of-canopy bidirectional reflectances from MERIS and VGT for the restitution of BRDF and albedo over a subset of West Africa. Compared to the products obtained with MERIS alone, the fusion with VGT demonstrates an improvement of the spatial coverage and a reduction of product uncertainty by about a third

Toward a direct comparison of field and laboratory goniometer measurements

Field and laboratory goniometers are widely used in the remote sensing community to assess spectrodirectional reflection properties of selected targets. Even when the same target and goniometer system are used, field and laboratory results cannot directly be compared due to inherent differences, mainly in the illumination conditions since actual goniometers measure a hemispherical-conical reflectance in the field and a biconical reflectance in the lab. Yet, the ability to compare and combine measurements from different instrumental designs is critical to ensure sensor cross-calibration and for all applications that rely on measurements obtained with both types of instruments. One approach to this problem consists in retrieving the bidirectional reflectance distribution function (BRDF) of the targets of interest for each experimental setup and to compare these, since theoretically they are independent of the particular conditions of illumination and observation. This involves a correction for diffuse incoming radiation in the case of field measurements, and a correction for conicity and inhomogeneity of illumination in the case of laboratory measurements. In this paper, we present a novel BRDF retrieval scheme for typical laboratory goniometers and compare it with the usual correction method assuming Lambertian behavior. We then discuss the first results of measurements and BRDF retrievals using the field and laboratory goniometer systems of the Remote Sensing Laboratories of the University of Zurich, which share the exact observation geometry, on the same inert, highly anisotropic target.

Development of an operational procedure to estimate surface albedo from the SEVIRI/MSG observing system by using POLDER BRDF measurements: II. Comparison of several inversion techniques and uncertainty in albedo estimates

This paper develops a concept related to the significant information extracted from the bidirectional reflectance distribution function (BRDF) of the terrestrial targets. The main issues are: the choice of the BRDF model, the solution to the inverse problem, and the accuracy assessment of estimated albedo. The present concept is based on the fact that the exact solution to the inverse problem belongs to a statistically significant region centered on the least squares solution (LSS). Nonetheless, LSS may be useless if the matrix inversion yields an ill-posed problem. It is then recommended to seek an alternative solution, which will yield a similar confidence interval but will be more physically sound. A list of 15 kernels entering in a basic model is examined by means of factor analysis performed in vector space, which is spanned at all known kernels. The application is carried out with synthetic angular data generated for the SEVIRI/MSG observing system. Models are evaluated based on statistical results—minimum of squared sum of residuals (SSR) and maximum of explained variance—after adjustment on reflectance data corresponding to a wide set of land cover types. Since the matrix of the model is almost singular, we identified an optimal subset model consisting of eight kernels, which has higher conditioned index and falls within the 95% confidence interval. It was found that the reflectance predicted by multi-kernel model is consistent with measurements. The idea in opting for a multi-kernel approach comes from the necessity to perform a higher angular resolution for the BRDF retrieval. Inversion experiments confirmed an advantage of the composite model over conventional three-parameter models in accuracy assessment of reflectance and albedo in the case of uniform and restricted angular samplings. Three methods are considered: statistical inversion (provided by the LSS), ridge regression and statistical regularization. The two latter are advised to solve the ill-conditioned inverse problem. Statistical regularization uses a priori statistical information. The inversion numerical experiment with SEVIRI/MSG angular geometry shows that only ridge regression provides a reasonable solution when a composite model is used. In addition, ridge regression and statistical regularization methods provide physically acceptable solutions in terms of BRDF and albedo predictability, even for three-parameter models. It is advised that LSS be implemented only at the middle of the summer season in the Northern Hemisphere. Otherwise, the use of ridge regression and statistical regularization is recommended to retrieve BRDF and albedo at other time periods in extra-tropical latitudes.

A fast, accurate algorithm to account for non‐Lambertian surface effects on TOA radiance

Surface bidirectional reflectance distribution function (BRDF) influences both the radiance just above the surface and that emerging from the top of the atmosphere (TOA). In this study we propose a new, fast, and accurate algorithm CASBIR (correction for anisotropic surface bidirectional reflection) to account for such influences on TOA radiance. This new algorithm is based on four‐stream theory that separates the radiation field into direct and diffuse components in both upwelling and downwelling directions. Such a separation is important because the direct component accounts for a substantial portion of incident radiation under a clear sky, and the BRDF effect is strongest in the reflection of the incident direct radiation. The model is validated by comparison with a full‐scale, vector radiative transfer model for the atmosphere‐surface system [Ahmad and Fraser, 1982] for wavelengths from UV to near‐IR over three typical but very different surface types. The result demonstrates that CASBIR is accurate for all solar and viewing zenith and azimuth angles considered, with overall relative difference of less than 0.7%. Application of this algorithm includes both accounting for non‐Lambertian surface scattering on the emergent radiation above TOA and developing a more effective approach for surface BRDF retrieval from satellite‐measured radiance. Comparison with the result from the Lambertian model indicates that surface BRDF influence on TOA radiance is both angle and wavelength dependent. It increases as solar zenith angle decreases or wavelength increases and becomes strongest in the view directions where the surface reflection is most anisotropic (such as in the hot spot or Sun glint regions).

A priori knowledge accumulation and its application to linear BRDF model inversion

A priori knowledge can significantly improve the retrieval of surface bidirectional reflectance and spectral albedo from satellite observations. Here a priori knowledge takes the form of field measurements of bidirectional reflectance factors for various surface cover types in red and near‐infrared bands. Bidirectional reflectance and albedo retrieval refers to inversion of a kernel‐driven bidirectional reflectance distribution function (BRDF) model using surface reflectance observations derived from orbiting spacecraft. A priori knowledge is applied when noise and poor angular sampling reduce the accuracy of model inversion given a limited number of observations. In such cases, a priori knowledge can indicate when retrieved kernel weights or albedos are outside expected bounds, leading to a closer examination of data. If data are noisy, a priori knowledge can be used to smooth the data. If the data exhibit poor angular sampling, a priori knowledge can be used according to Bayesian inference theory to yield a posteriori estimates of unknown kernel weights. In the latter application, Bayes theory is applied in data space rather than in parameter space. Extensive study and simulation using 73 sets of field observations and 395 spaceborne observation sets from the POLDER instrument validates the importance of a priori information in improving inversions and BRDF retrievals.

The interrelationship of atmospheric correction of reflectances and surface BRDF retrieval: a sensitivity study

This paper systematically studies the interrelationship between surface bidirectional reflectance distribution function (BRDF) retrieval and atmospheric correction. The study uses the atmospheric correction scheme of the Moderate Resolution Imaging Spectroradiometer (MODIS) and angular sampling expected for MODIS and the Multiangle Imaging SpectroRadiometer (MISR) for different land cover types and optical depths of aerosols. The results show the following two points. 1) Even for a nonturbid atmosphere, the assumption of a Lambertian surface in atmospheric correction causes relative errors in the retrieved surface reflectances that average from 2 to 7% in the red and near-infrared bands, with worst cases showing errors of up to about 15% for turbid conditions. Consequently, it is necessary for improved accuracy to consider surface anisotropy in atmospheric correction. 2) Surface BRDF retrieval and atmospheric correction can be coupled in a converging iteration loop that improves the quality of atmospheric correction and subsequent BRDF retrievals. For example, performing two steps of the iteration loop is already sufficient to obtain mean relative errors of less than 1% in the retrieved surface reflectances even for an atmospheric aerosol optical depth of 0.4. As BRDF retrieval accuracies improve, so do bihemispherical albedo retrieval accuracies, with mean relative errors being 1-5% when using a Lambertian assumption and less than 1% after two iteration steps.

Variability in Leaf and Litter Optical Properties: Implications for BRDF Model Inversions Using AVHRR, MODIS, and MISR

Canopy radiative transfer models simulate the bidirectional reflectance distribution function (BRDF) of vegetation covers with differing leaf and soil spectral and canopy structural characteristics. Numerical inversion of these models has provided estimates of vegetation structural and biophysical characteristics from multiangle, remotely sensed optical data. The number of angularly unique observations compared to BRDF model parameters largely determines the accuracy of retrievals. To increase this ratio, additional observations of a target must be acquired and the BRDF models and inversions must be simplified. The former will occur when the EOS instruments become available. Previous studies suggest that simplification of BRDF model inversions may best be accomplished by constraining the leaf optical parameters. This study focused on full-range (400–2500 nm) leaf and litter spectral properties convolved to AVHRR, MODIS, and MISR optical channels. Using a diverse array of woody plant and grass species, we found robust and readily usable interrelationships among spectra through correlation, regression, and principal components analyses. Significant differences between green leaf and litter optical properties and their sensor-specific interrelationships indicate that green leaf optical constraints may be useful with BRDF retrievals to detect the onset of canopy senescence. These findings will provide increased efficiency in canopy BRDF model inversions by decreasing the number of observations required to retrieve canopy structural and biophysical information from multiangle remotely sensed data.

Retrieval of surface BRDF from multiangle remotely sensed data

The bidirectional reflectance distribution function (BRDF) is the intrinsic measure of the directional properties of the Earth's surface. However, it cannot be measured directly by remote sensing. A procedure using the optimum technique is applied to retrieve surface BRDF parameters from multiangle remotely sensed data in which a statistical BRDF model and an analytical model of atmospheric radiative transfer are coupled. The atmospheric model decomposes radiant intensity received by a detector into four parts: scattered only by the (non-Lambertian) surface; singly scattered by the atmosphere; multiply scattered to the surface but unscattered to the detector; and multiply scattered before and after surface scattering. Analytical solutions are derived for the first two. The third is derived from a two-stream model, but incorporates the surface BRDF explicitly. The fourth uses a two-stream model under conditions of azimuthal independence. The six-parameter surface BRDF is modeled as the sum of a modified limacon function, which describes the basic bowl-shape of the BRDF, and a negative exponential, which describes the hotspot. Multiangle observations from the ASAS and PARABOLA instruments are analyzed using the coupled models. Experiments show that the simple statistical BRDF can summarize typical directional observations with errors in the range of 3–10%.