Bidirectional Reflectance Distribution Function Parameters Research Articles

Potential Investigation of Linking PROSAIL with the Ross-Li BRDF Model for Vegetation Characterization

Methods that link different models for investigating the retrieval of canopy biophysical/structural variables have been substantially adopted in the remote sensing community. To retrieve global biophysical parameters from multiangle data, the kernel-driven bidirectional reflectance distribution function (BRDF) model has been widely applied to satellite multiangle observations to model (interpolate/extrapolate) the bidirectional reflectance factor (BRF) in an arbitrary direction of viewing and solar geometries. Such modeled BRFs, as an essential information source, are then input into an inversion procedure that is devised through a large number of simulation analyses from some widely used physical models that can generalize such an inversion relationship between the BRFs (or their simple algebraic composite) and the biophysical/structural parameter. Therefore, evaluation of such a link between physical models and kernel-driven models contributes to the development of such inversion procedures to accurately retrieve vegetation properties, particularly based on the operational global BRDF parameters derived from satellite multiangle observations (e.g., MODIS). In this study, the main objective is to investigate the potential for linking a popular physical model (PROSAIL) with the widely used kernel-driven Ross-Li models. To do this, the BRFs and albedo are generated by the physical PROSAIL in a forward model, and then the simulated BRFs are input into the kernel-driven BRDF model for retrieval of the BRFs and albedo in the same viewing and solar geometries. To further strengthen such an investigation, a variety of field-measured multiangle reflectances have also been used to investigate the potential for linking these two models. For simulated BRFs generated by the PROSAIL model at 659 and 865 nm, the two models are generally comparable to each other, and the resultant root mean square errors (RMSEs) are 0.0092 and 0.0355, respectively, although some discrepancy in the simulated BRFs can be found at large average leaf angle (ALA) values. Unsurprisingly, albedos generated by the method are quite consistent, and 99.98% and 97.99% of the simulated white sky albedo (WSA) has a divergence less than 0.02. For the field measurements, the kernel-driven model presents somewhat better model-observation congruence than the PROSAIL model. The results show that these models have an overall good consistency for both field-measured and model-simulated BRFs. Therefore, there is potential for linking these two models for looking into the retrieval of canopy biophysical/structural variables through a simulation method, particularly from the current archive of the global routine MODIS BRDF parameters that were produced by the kernel-driven BRDF model; however, erectophile vegetation must be further examined.

Adjustment of Sentinel-2 Multi-Spectral Instrument (MSI) Red-Edge Band Reflectance to Nadir BRDF Adjusted Reflectance (NBAR) and Quantification of Red-Edge Band BRDF Effects

Optical wavelength satellite data have directional reflectance effects over non-Lambertian surfaces, described by the bidirectional reflectance distribution function (BRDF). The Sentinel-2 multi-spectral instrument (MSI) acquires data over a 20.6° field of view that have been shown to have non-negligible BRDF effects in the visible, near-infrared, and short wave infrared bands. MSI red-edge BRDF effects have not been investigated. In this study, they are quantified by an examination of 6.6 million (January 2016) and 10.7 million (April 2016) pairs of forward and back scatter reflectance observations extracted over approximately 20° × 10° of southern Africa. Non-negligible MSI red-edge BRDF effects up to 0.08 (reflectance units) across the 290 km wide MSI swath are documented. A recently published MODIS BRDF parameter c-factor approach to adjust MSI visible, near-infrared, and short wave infrared reflectance to nadir BRDF-adjusted reflectance (NBAR) is adapted for application to the MSI red-edge bands. The red-edge band BRDF parameters needed to implement the algorithm are provided. The parameters are derived by a linear wavelength interpolation of fixed global MODIS red and NIR BRDF model parameters. The efficacy of the interpolation is investigated using POLDER red, red-edge, and NIR BRDF model parameters, and is shown to be appropriate for the c-factor NBAR generation approach. After adjustment to NBAR, red-edge MSI BRDF effects were reduced for the January data (acquired close to the solar principal where BRDF effects are maximal) and the April data (acquired close to the orthogonal plane) for all the MSI red-edge bands.

Assessment of MODIS BRDF/Albedo Model Parameters (MCD43A1 Collection 6) for Directional Reflectance Retrieval

Measurements of solar radiation reflected from Earth’s surface are the basis for calculating albedo, vegetation indices, and other terrestrial attributes. However, the “bi-directional” geometry of illumination and viewing (i.e., the Bi-directional Reflectance Distribution Function (BRDF)) impacts reflectance and all variables derived or estimated based on these data. The recently released MODIS BRDF/Albedo Model Parameters (MCD43A1 Collection 6) dataset enables retrieval of directional reflectance at arbitrary solar and viewing angles, potentially increasing precision and comparability of data collected under different illumination and observation geometries. We quantified the ability of MCD43A1 Collection 6 for retrieving directional reflectance and compared the daily Collection 6 retrievals to those of MCD43A1 Collection 5, which are retrieved on an eight-day basis. Correcting MODIS-based estimates of surface reflectance from the illumination and viewing geometry of the Terra satellite (MOD09GA) to that of the MODIS Aqua (MYD09GA) overpass, as well as MCD43A4 Collection 6 and Landsat-5 TM images show that the BRDF correction of MCD43A1 Collection 6 results in greater consistency among datasets, with higher R2 (0.63–0.955), regression slopes closer to unity (0.718–0.955), lower root mean squared difference (RMSD) (0.422–3.142), and lower mean absolute error (MAE) (0.282–1.735) compared to the Collection 5 data. Smaller levels of noise (observed as high-frequency variability within the time series) in MCD43A1 Collection 6 in comparison to Collection 5 corroborates the improvement of BRDF parameters time series. These results corroborates that the daily MCD43A1 Collection 6 product represents the anisotropy of surface features and results in more precise directional reflectance derivation at any solar and viewing geometry than did the previous Collection 5.

Improving BRDF normalisation for Landsat data using statistical relationships between MODIS BRDF shape and vegetation structure in the Australian continent

The intra- and inter-annual variability of the Moderate Resolution Imaging Spectroradiometer (MODIS) surface Bidirectional Reflectance Distribution Function (BRDF) products in Australia have been analysed using 10 years (2002−2011) of MODIS BRDF Collection 6 data. Root Mean Square (RMS) and Anisotropic Flat Index (AFX) parameters were used as BRDF shape indices to represent the overall BRDF shape. Australian vegetation structure and agro-climate regions were used to separate and analyse different BRDF shape patterns. The results confirmed earlier findings that the intra-annual variation of MODIS BRDF shape is stronger than the inter-annual. High variance in the shape parameters of the base BRDF model as well as poor quality data regions suggest that for spectral data correction, the data are better used as a spatial average of BRDF shape (e.g. over a vegetation structure/climate class patch). This can reduce the spatial variance of shape to similar levels observed in the local variance of spectral data. The within- and between-class variations as well as correlations between some BRDF shape indices and normalized difference vegetation index (NDVI) show that inter-annual relationships between BRDF parameters can be separated using strata based on vegetation structure (basically height, cover and dominant growth form). However, due to the different climate drivers involved, such as rainfall, light and temperature, the intra-annual variation is better analysed if the patches of vegetation structure classes are identified by climate zone for climatic homogeneity. It is found that if long term averages are used to represent stable focal points in the time series, significant relationships exist between BRDF indices and spectral indices such as NDVI. However, there is no satisfactory way to directly relate short term fluctuations of (e.g.) NDVI with short term BRDF changes. All of the indices are significantly related to rainfall, structural characteristics and biomass with lagged relationships providing a more suitable pathway to analysing short variations. It is concluded that for the situation where a high quality BRDF product is available, the best strategy would involve use of a set of vegetation structure class patches to increase the resolution of local differences in BRDF shape for inhomogeneous scenes as well as control the high variance in MODIS BRDF model shape parameters. The partial relationships found between some BRDF indices (in particular RMS) and average rainfall or stable (longer term) NDVI also provide a potential for empirically updating BRDF shape for climate variation in periods before MODIS data were available.

Joint DEnKF-albedo assimilation scheme that considers the common land model subgrid heterogeneity and a snow density-based observation operator for improving snow depth simulations

For the large-area snow depth (SD) data sets with high spatial resolution in the Altay region of Northern Xinjiang, China, we present a deterministic ensemble Kalman filter (DEnKF)-albedo assimilation scheme that considers the common land model (CoLM) subgrid heterogeneity. In the albedo assimilation of DEnKF-albedo, the assimilated albedos over each subgrid tile are estimated with the MCD43C1 bidirectional reflectance distribution function (BRDF) parameters product and CoLM calculated solar zenith angle. The BRDF parameters are hypothesized to be consistent over all subgrid tiles within a specified grid. In the SCF assimilation of DEnKF-albedo, a DEnKF combining a snow density-based observation operator considers the effects of the CoLM subgrid heterogeneity and is employed to assimilate MODIS SCF to update SD states over all subgrid tiles. The MODIS SCF over a grid is compared with the area-weighted sum of model predicted SCF over all the subgrid tiles within the grid. The results are validated with <italic<in situ</italic< SD measurements and AMSR-E product. Compared with the simulations, the DEnKF-albedo scheme can reduce errors of SD simulations and accurately simulate the seasonal variability of SD. Furthermore, it can improve simulations of SD spatiotemporal distribution in the Altay region, which is more accurate and shows more detail than the AMSR-E product.

Reliable estimation of anisotropic BRDF

Bidirectional reflectance distribution function (BRDF) estimation is one of the fundamental problems in computer graphics, computer vision and related fields, yet most work relies on conventional local minimisation. Two global optimisation methods for estimating the anisotropic Ward BRDF model and its variants under an L 2-cost function are introduced. The presented algorithms are based on branch-and-bound search which guarantees to provide globally optimal solutions. In contrast to the traditional local optimisation, the methods mentioned here are not affected by the setting of initial BRDF parameter values. Experiments have been carried out to validate the presented methods using anisotropic materials from the UTIA BRDF database.

Branch and bound algorithm for accurate estimation of analytical isotropic bidirectional reflectance distribution function models.

We present a reliable and accurate global optimization framework for estimating parameters of isotropic analytical bidirectional reflectance distribution function (BRDF) models. This approach is based on a branch and bound strategy with linear programming and interval analysis. Conventional local optimization is often very inefficient for BRDF estimation since its fitting quality is highly dependent on initial guesses due to the nonlinearity of analytical BRDF models. The algorithm presented in this paper employs L1-norm error minimization to estimate BRDF parameters in a globally optimal way and interval arithmetic to derive our feasibility problem and lower bounding function. Our method is developed for the Cook-Torrance model but with several normal distribution functions such as the Beckmann, Berry, and GGX functions. Experiments have been carried out to validate the presented method using 100 isotropic materials from the MERL BRDF database, and our experimental results demonstrate that the L1-norm minimization provides a more accurate and reliable solution than the L2-norm minimization.

Bidirectional reflectance distribution function modeling of one-dimensional rough surface in the microwave band

In this study, the bidirectional reflectance distribution function (BRDF) of a one-dimensional conducting rough surface and a dielectric rough surface are calculated with different frequencies and roughness values in the microwave band by using the method of moments, and the relationship between the bistatic scattering coefficient and the BRDF of a rough surface is expressed. From the theory of the parameters of the rough surface BRDF, the parameters of the BRDF are obtained using a genetic algorithm. The BRDF of a rough surface is calculated using the obtained parameter values. Further, the fitting values and theoretical calculations of the BRDF are compared, and the optimization results are in agreement with the theoretical calculation results. Finally, a reference for BRDF modeling of a Gaussian rough surface in the microwave band is provided by the proposed method.

Intercomparison of Landsat albedo retrieval techniques and evaluation against in situ measurements across the US SURFRAD network

Surface albedo is an essential parameter not only for developing climate models, but also for most energy balance studies. While climate models are usually applied at coarse resolution, the energy balance studies, which are mainly focused on agricultural applications, require a high spatial resolution. In this context Landsat is one of the most used remote sensing sensors.The albedo, estimated through the angular integration of the Bidirectional Reflectance Distribution Function (BRDF), requires an appropriate angular sampling of the surface. However, Landsat sampling characteristics, with nearly constant observation geometry and low illumination variation, prevent from deriving a surface albedo product.In this paper we present an algorithm to derive a Landsat surface albedo based on the BRDF parameters estimated from the MODerate Resolution Imaging Spectroradiometer (MODIS) Climate Modeling Grid (CMG) surface reflectance product (M{O,Y}D09) using the VJB method (Vermote, Justice, & Bréon, 2009). We base our method on Landsat unsupervised classification to disaggregate the BRDF parameters to the Landsat spatial resolution. We tested the proposed algorithm over five different sites of the US Surface Radiation (SURFRAD) network and inter-compare our results with Shuai, Masek, Gao, and Schaaf (2011) method, which also provides Landsat albedo. The results show that with the proposed method we can derive the surface albedo with a Root Mean Square Error (RMSE) of 0.015 (7%). This result supposes an improvement of 5% in the RMSE compared to Shuai et al.'s (2011) method (with a RMSE of 0.024, 12%) that is mainly determined by the correction of the negative bias (lower retrieved albedo than in situ data).

Testing the local applicability of MODIS BRDF parameters for correcting Landsat TM imagery

Landsat Thematic Mapper (TM) imagery is widely used for large scale multi-temporal monitoring of the land surface. One source of variability in this imagery, which is not related to variation in the land surface, is the result of the bi-directional reflectance distribution function (BRDF). This causes variation in the measured reflectance as a function of the angular configuration of the sun and sensor. To remove this variation from the imagery, a correction can be applied for BRDF. Some authors have proposed the use of the BRDF parameterization derived for the Moderate Resolution Imaging Spectroradiometer (MODIS; NASA, Washington, DC, USA) instrument as a means of correction for BRDF in Landsat TM imagery. However, since the spatial scale of the two instruments is substantially different, it is not clear up to what extent the MODIS BRDF parameters are applicable to Landsat TM. This study provides a direct empirical test of this, over the eastern Australian landscape, and tests whether the MODIS BRDF parameters are applicable to characterize BRDF at a local scale of a Landsat TM pixel, or simply at a global scale. The results suggest that the MODIS BRDF parameters effectively characterize the BRDF of the Landsat imagery at a global average scale, but, at least in the Australian context, do not capture any further information about the BRDF of more local regions, or individual Landsat pixels.

Measurement and modeling of Bidirectional Reflectance Distribution Function (BRDF) on material surface

Measurement and modeling of Bidirectional Reflectance Distribution Function (BRDF) is made to describe optical scattering characteristics of the material surface. A measurement device of BRDF with the ability to real-time measurement is developed by the micro fiber spectrometer and three-dimensional turntables. The measured spectral range is from visible to near infrared with 2.4nm wavelength resolution. The measured angular range is 0–360° in azimuth angle and 0–85° in zenith angle with 0.01° angle resolution. BRDF measurement on tinfoil and ceramic tile is made and the measurement error is no more than 6.05%. A six-parameter model of BRDF is built according to material surface characteristics. Based on the simulated annealing algorithm, the model parameters of BRDF are obtained through fitting processing of measured data and the maximal fitting error is 9.94%. The results show that the measuring accuracy of BRDF depends on the precision of measuring platform, the sensitivity of measuring instrument and the stability of illuminating light source, and the modeling accuracy of BRDF is closely related to the measured data and optimization modeling method. The measurement and modeling of BRDF may benefit future optical remote sensing, computer graphics and environmental surveillance.

Foliage Clumping Index Over China's Landmass Retrieved From the MODIS BRDF Parameters Product

The three-dimentional plant canopy architecture is often characterized using the foliage clumping index useful for ecological and land surface modeling. In this paper, an algorithm is developed to retrieve the foliage clumping index with the Moderate Resolution Imaging Spectroradiometer bidirectional reflectance distribution function (BRDF) parameter product (MCD43A1), which is generated using the RossThick-LiSparse Reciprocal (Ross-Li) model. First, the Ross-Li model is modified to improve the simulation of the reflectance at hotspot using the Polarization and Directionality of Earth Reflectance measurements as benchmarks to determine BRDF parameters. Then, the modified model (Ross-Li-H) is used to simulate the reflectance at hotspot and darkspot, which is used to calculate the normalized difference between hotspot and darkspot (NDHD). With the relationship between clumping index and NDHD simulated by the 4-Scale geometrical model, the clumping index over China's landmass at 500-m resolution is retrieved every 8 days during the period from 2003 to 2008. Finally, The effect of topography on the retrieved clumping index is corrected using a topographic compensation function calculated from the digital elevation model at 90-m resolution. The topographically corrected clumping index values correlate well with field measurements at five sites over China, indicating the feasibility of the algorithm for retrieving the clumping index from the MCD43A1 product.

Seasonal and inter-annual variation in view angle effects on MODIS vegetation indices at three forest sites

The bi-directional reflectance distribution function (BRDF) has been widely studied across different vegetation types. However, these studies generally report values for only one point in time. We were interested in the potential for seasonal and inter-annual variation in BRDF parameters. NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on board the EOS satellites has now been collecting data for 10 years. Since BRDF parameters are reported for the individual spectral bands, these data can be used to examine intra-annual variation. However, MODIS BRDF parameters are not calculated for the various vegetation indices which are derived from the spectral bands. Our objective in this study was to use the 10 years of MODIS data now available to examine seasonal and inter-annual variation in the view angle sensitivity of three vegetation indices; the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), and the photochemical reflectance index (PRI) at 3 flux tower sites (Harvard Forest, Howland Forest and Morgan Monroe State Forest). For these 3 sites, only EVI was significantly affected by view angle. There was also a substantial variation in the view angle sensitivity of EVI across seasons and this variation was different for backscatter vs. forward scatter data. It is possible that differences in the scattering of radiation between the spring and the fall are responsible for the seasonal difference in view angle sensitivity. There were also complimentary variations in forward and backscatter view angle sensitivity of EVI across years. The greater view angle sensitivity of EVI, as opposed to NDVI, suggests that greater care must be taken to correct for BRDF effects when using this vegetation index.

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

A regularized inversion method is presented to invert the bidirectional reflectance distribution function (BRDF) model for albedo calculations. It is known as discrete ill-posed inverse problem. Essential issues that must be considered include solution existence, solution uniqueness, instability of the solution process, and geophysical meanings of solution. In order to robustly estimate BRDF models parameters, a spectrum cutoff technique is employed to suppress spurious oscillations due to poor angular distribution, sparse sample size, or both. We studied difficulties in estimating BRDF parameters and analyzed the cause of the instability in the solution, and then presented a novel algorithm. The proposed RSC (Regularized Spectrum Cutoff) method is based on improving the spectrum of the BRDF kernel matrix and can always find suitable BRDF parameters for sparse satellite observations. The algorithm is compared with the MODIS (Moderate Resolution Imaging Spectroradiometer) full retrieval (FR) algorithm, and it is tested to be suitable for the case of sufficient observations. Numerical experiments with POLDER (Polarization and Directionality of the Earth's Reflectances) BRDF datasets and MODIS images suggest that the developed spectrum cutoff method is adaptive and robust, and it is valuable for retrieving land surface BRDF and albedo.

Accounting for surface reflectance anisotropy in satellite retrievals of tropospheric NO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;

Abstract. Surface reflectance is a key parameter in satellite trace gas retrievals in the UV/visible range and in particular for the retrieval of nitrogen dioxide (NO2) vertical tropospheric columns (VTCs). Current operational retrievals rely on coarse-resolution reflectance data and do not account for the generally anisotropic properties of surface reflectance. Here we present a NO2 VTC retrieval that uses MODIS bi-directional reflectance distribution function (BRDF) data at high temporal (8 days) and spatial (1 km × 1 km) resolution in combination with the LIDORT radiative transfer model to account for the dependence of surface reflectance on viewing and illumination geometry. The method was applied to two years of NO2 observations from the Ozone Monitoring Instrument (OMI) over Europe. Due to its wide swath, OMI is particularly sensitive to BRDF effects. Using representative BRDF parameters for various land surfaces, we found that in July (low solar zenith angles) and November (high solar zenith angles) and for typical viewing geometries of OMI, differences between MODIS black-sky albedos and surface bi-directional reflectances are of the order of 0–10% and 0–40%, respectively, depending on the position of the OMI pixel within the swath. In the retrieval, black-sky albedo was treated as a Lambertian (isotropic) reflectance, while for BRDF effects we used the kernel-based approach in the MODIS BRDF product. Air Mass Factors were computed using the LIDORT radiative transfer model based on these surface reflectance conditions. Differences in NO2 VTCs based on the Lambertian and BRDF approaches were found to be of the order of 0–3% in July and 0–20% in November with the extreme values found at large viewing angles. The much larger differences in November are mainly due to stronger BRDF effects at higher solar zenith angles. To a smaller extent, they are also caused by the typically more pronounced maximum of the NO2 a priori profiles in the boundary layer during the cold season, which make the retrieval more sensitive to radiation changes near the surface. However, BRDF impacts vary considerably across Europe due to differences in land surface type and increasing solar zenith angles at higher latitude. Finally, we compare BRDF-based NO2 VTCs with those retrieved using the GOME/TOMS Lambertian equivalent reflectance (LER) data set. The relative differences are mostly below 15% in July but in November the NO2 VTCs from TOMS/GOME are lower by 20–60%. Our results indicate that the specific choice of albedo data set is even more important than accounting for surface BRDF effects, and this again demonstrates the strong requirement for more accurate surface reflectance data sets.

Extracting Realistic Textures from Reference Spheres

PDF HTML阅读 XML下载 导出引用 引用提醒 利用采样球体提取真实感纹理 DOI: 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: Supported by the National Basic Research Program of China under Grant No.2006CB303105 (国家重点基础研究发展计划(973)) Extracting Realistic Textures from Reference Spheres Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:提出了一种从真实物体中提取纹理的方法.利用具有复杂纹理的参考球体作为被采样物体,计算其组成材质的BRDF(bidirectional reflectance distribution function)模型参数以及各点由不同材质构成的比例,形成一幅材质权重图.该图作为纹理映射到3D物体上后,配合BRDF模型参数进行渲染,形成一种适用于重光照(relighting)的纹理.被渲染物体可根据自身方位以及光源亮度/方位呈现出自然的光影变化,达到较为逼真的外观效果. Abstract:A method for extracting realistic textures from real-world reference spheres is proposed in this paper. The BRDF (bidirectional reflectance distribution function) parameters of fundamental materials, as well as a material weight map for the sphere are obtained through a non-linear optimization process. The material weight map can be used as a conventional texture for relighting. With the BRDF models recovered, the rendered 3D objects can exhibit real and complex appearances with varying light direction/intensity, view point and surface orientations. 参考文献 相似文献 引证文献

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.

Detecting vegetation structure using a kernel-based BRDF model

The magnitude of the anisotropy of vegetation is mainly determined by its spectral and structural features. It can be described by the bidirectional reflectance distribution function (BRDF). The parameters of physical BRDF models are related to the biophysical structural information. However, for a semiempirical kernel-based BRDF model, the relationship between BRDF parameters and vegetation structure is no longer as clear as with a physical BRDF model. To reveal this relationship, a structural scattering index (SSI) and a relative structural scattering index (RSSI) are derived based on the BRDF parameters in this paper. The investigation of SSI and RSSI show that they have both theoretical and practical meaning and can be used to distinguish different land cover types or to detect structural changes.

A new approach to the retrieval of surface properties from earthshine measurements

Instruments such as the MODIS and MISR radiometers on EOS AM-1, and POLDER on ADEOS have been deployed for the remote sensing retrieval of surface properties. Typically, retrieval algorithms use linear combinations of semi-empirical bidirectional reflectance distribution function (BRDF) kernels to model surface reflectance. The retrieval proceeds in two steps; first, an atmospheric correction relates surface BRDF to top-of-atmosphere (TOA) reflectances, then regression is used to establish the linear coefficients used in the kernel combination. BRDF kernels may also depend on a number of physical or empirical non-linear parameters (e.g. ocean wind speed for a specular BRDF); such parameters are usually assumed known. A major source of error in this retrieval comes from lack of knowledge of planetary boundary layer (PBL) aerosol properties. In this paper, we present a different approach to surface property retrieval. For the radiative transfer simulations, we use the discrete ordinate LIDORT model, which has the capability to generate simultaneous fields of radiances and weighting functions in a multiply scattering multi-layer atmosphere. Surface–atmosphere coupling due to multiple scattering and reflection effects is treated in full; the use of an atmospheric correction is not required. Further, it is shown that sensitivities of TOA reflectances to both linear and non-linear surface BRDF parameters may be established directly by explicit analytic differentiation of the discrete ordinate radiative transfer equations. Surface properties may thus be retrieved directly and conveniently from satellite measurements using standard non-linear fitting methods. In the fitting for BRDF parameters, lower-boundary aerosol properties can either be retrieved as auxiliary parameters, or they can be regarded as forward model parameter errors. We present examples of simulated radiances and surface/aerosol weighting functions for combinations of multi-angle measurements at several different wavelengths, and we perform some examples of self-consistent non-linear fitting to demonstrate feasibility for this kind of surface property retrieval.

First operational BRDF, albedo nadir reflectance products from MODIS

With the launch of NASA's Terra satellite and the MODerate Resolution Imaging Spectroradiometer (MODIS), operational Bidirectional Reflectance Distribution Function (BRDF) and albedo products are now being made available to the scientific community. The MODIS BRDF/Albedo algorithm makes use of a semiempirical kernel-driven bidirectional reflectance model and multidate, multispectral data to provide global 1-km gridded and tiled products of the land surface every 16 days. These products include directional hemispherical albedo (black-sky albedo), bihemispherical albedo (white-sky albedo), Nadir BRDF-Adjusted surface Reflectances (NBAR), model parameters describing the BRDF, and extensive quality assurance information. The algorithm has been consistently producing albedo and NBAR for the public since July 2000. Initial evaluations indicate a stable BRDF/Albedo Product, where, for example, the spatial and temporal progression of phenological characteristics is easily detected in the NBAR and albedo results. These early beta and provisional products auger well for the routine production of stable MODIS-derived BRDF parameters, nadir reflectances, and albedos for use by the global observation and modeling communities.