Bidirectional Reflectance Distribution Function Shape Research Articles

The Description and Application of BRDF Based on Shape Vectors for Typical Landcovers

As the inherent attribute of land cover, anisotropy leads to the heterogeneity of directional reflection; meanwhile, it creates the opportunity for retrieving characteristics of land surface based on multi-angle observations. BRDF (Bidirectional Reflectance Distribution Function) is the theoretical expression of anisotropy and describes the reflectance in terms of incident-view geometry. Prior BRDF knowledge is used to achieve the multi-angle retrieval for earth observation systems with a narrow FOV (Field of View). Shape indicators are a feasible way to capture the characteristics of BRDF or to build an a priori database of BRDF. However, existing shape indicators based on the ratio of reflectance or the weight of scattering effects are too rough to describe the BRDF’s shape. Thus, it is necessary to propose new shape vectors to satisfy the demand. We selected six typical land covers from MODIS-MCD12 on the homogeneous underlayers as the study sites in North America. The daily BRDF is retrieved by MODIS-BRDF parameters and the RossThick-LiSparseR model. When the SZA (Solar Zenith Angle) is set at 45°, seven directions (−70°, −45°, −20°, 0°, 20°, 45°, and 70°) including edge spot, zenith spot, hot spot and approximate dark spot of the BRDF principal plane were selected to construct two vectors by the change rate of reflectance and angle formulation: Partial Anisotropic Vector (PAV) and Angular Effect Vector (AEV). Then, we assessed the effectiveness of PAV and AEV compared with ANIX (Anisotropic Index), ANIF (Anisotropic Factor) and AFX (Anisotropic Flat Index) by two typical BRDF shapes. The representativeness of PAV and AEV for the original BRDF was also assessed by cosine similarity and error transfer function. Lastly, the application of hot spot components in AEV for land cover classification, the monitoring of land cover in mining areas and the adjustment effect by NDVI (Normalized Difference Vegetation Index) were investigated. The results show that (1) the shape vectors have good representativeness compared with original BRDF. The representativeness of PAV assessed by cosine similarity is 0.980, 0.979 and 0.969, and the representativeness of AEV assessed by error transfer function is 0.987, 0.991 and 0.994 in the three MODIS broadbands of Near Infrared (NIR, 0.7–5.0 µm), Short Wave (SW, 0.3–5.0 µm) and Visible (VIS, 0.3–0.7 µm). (2) Some components of shape vectors have high correlation with AFX. The correlation coefficient between hot spot components in AEV and AFX is 0.936, 0.945 and 0.863, respectively, in NIR, SW and VIS bands. (3) The shape vectors show potentiality for land cover classification and the monitoring of land cover in mining areas. The correlation coefficients of hot spot components in AEV for MODIS-pixels with the same types (0.557, 0.561, 0.527) are significantly higher than MODIS-pixels with various types (0.069, 0.055, 0.051) in NIR, SW and VIS bands. The coefficients of variation for hot spot components are significantly higher after land reclamation (0.0071, 0.0099) than before land reclamation (0.0020, 0.0028). (4) The correlation between NDVI and the BRDF shapes is poor in three MODIS broad bands. The correlation coefficients between NDVI and the BRDF shapes in three temporal scales of annual, seasonal and monthly phases are only 0.134, 0.063 and 0.038 (NIR), 0.199, 0.185 and 0.165 (SW), and 0.323, 0.320 and 0.337 (VIS), on average.

Evaluation of the Snow Albedo Retrieved from the Snow Kernel Improved the Ross-Roujean BRDF Model

The original kernel-driven bidirectional reflectance distribution function (BRDF) models were developed based on soil-vegetation systems. To further improve the ability of the models to characterize the snow surface scattering properties, a snow kernel was derived from the asymptotic radiative transfer (ART) model and used in the kernel-driven BRDF model framework. However, there is a need to further evaluate the influence of using this snow kernel to improve the original kernel-driven models in snow albedo retrieval applications. The aim of this study is to perform such an evaluation using a variety of snow BRDF data. The RossThick-Roujean (RTR) model is used as a framework for taking in the new snow kernel (hereafter named the RTS model) since the Roujean geometric-optical (GO) kernel captures a neglectable hotspot effect and represents a more prominent dome-shaped BRDF, especially at a small solar zenith angle (SZA). We obtained the following results: (1) The RTR model has difficulties in reconstructing the snow BRDF shape, especially at large SZAs, which tends to underestimate the reflectance in the forward direction and overestimate reflectance in the backward direction for various data sources. In comparison, the RTS model performs very well in fitting snow BRDF data and shows high accuracy for all data. (2) The RTR model retrieved snow albedos at SZAs = 30°–70° are underestimated by 0.71% and 0.69% in the red and near-infrared (NIR) bands, respectively, compared with the simulation results of the bicontinuous photon tracking (bic-PT) model, which serve as “real” values. However, the albedo retrieved by the RTS model is significantly improved and generally agrees well with the simulation results of the bic-PT model, although the improved model still somewhat underestimates the albedo by 0.01% in the red band and overestimates the albedo by 0.05% in the NIR band, respectively, at SZAs = 30°–70°, which may be negligible. (3) The albedo derived by these two models shows a high correlation (R2 > 0.9) between the field-measured and Polarization and Directionality of the Earth's Reflectances (POLDER) data, especially for the black-sky albedo. However, the albedo derived using the RTR model is significantly underestimated compared with the RTS model. The RTR model underestimates the black-sky albedo (white-sky albedo) retrievals by 0.62% (1.51%) and 0.93% (2.08%) in the red and NIR bands, respectively, for the field-measured data. The shortwave black-sky and white-sky albedos derived using the RTR model for the POLDER data are underestimated by 1.43% and 1.54%, respectively, compared with the RTS model. These results indicate that the snow kernel in the kernel-driven BRDF model frame is more accurate in snow albedo retrievals and has the potential for application in the field of the regional and global energy budget.

An assessment of the performance of two snow kernels in characterizing snow scattering properties

ABSTRACTThe kernel-driven RossThick-LiSparseReciprocal (RTLSR) bidirectional reflectance distribution function (BRDF) model has been widely used in the quantitative remote sensing community. However, the performance of this model is challenged when modelling the optical scattering properties of pure snow surfaces. Recently, two snow kernels have been developed to improve the snow anisotropic reflectance in the kernel-driven RTLSR model framework. However, the performances of these two snow kernels must be assessed to identify their potential applications. Therefore, we assess the performances of these two kernels using various BRDF data sources. Our findings demonstrate their differences in several aspects. (1) These two kernels differ in characterizing the variability in BRDF shape as a function of the solar zenith angle (SZA). As the SZA increases, the shape of snow kernel derived by the asymptotic radiative transfer (ART) model (hereinafter named the ART method) changes from a dome shape to bowl shape, which agrees well with the simulation data of the bicontinuous photon tracking (bic-PT) model. The shape of the snow kernel proposed by Qu et al. (hereinafter named the Qu method) based on the Rahman-Pinty-Verstraete (RPV) model maintains a bowl shape for all SZAs. These differences in the kernel performances affect their abilities to fit snow BRDF data with different SZAs. (2) The corrected RTLSR models, with their respective snow kernels, are generally able to model the forward-scattering properties of snow surfaces compared with the original RTLSR model. However, the ART method performs better in capturing the BRDF variations in snow surfaces than the Qu method. This assessment provides an improved understanding of the performance of these two snow kernels and, thus, suggests further applications for the ART snow kernel in the kernel-driven BRDF model framework in the near future.

Improving the AVHRR Long Term Data Record BRDF Correction

The Long Term Data Record (LTDR) project has the goal of developing a quality and consistent surface reflectance product from coarse resolution optical sensors. This paper focuses on the Advanced Very High Resolution Radiometer (AVHRR) part of the record, using the Moderate Resolution Imaging Spectrometer (MODIS) instrument as a reference. When a surface reflectance time series is acquired from satellites with variable observation geometry, the directional variation generates an apparent noise which can be corrected by modeling the bidirectional reflectance distribution function (BRDF). The VJB (Vermote, Justice and Bréon, 2009) method estimates a target’s BRDF shape using 5 years of observation and corrects for directional effects maintaining the high temporal resolution of the measurement using the instantaneous Normalized Difference Vegetation Index (NDVI). The method was originally established on MODIS data but its viability and optimization for AVHRR data have not been fully explored. In this study we analyze different approaches to find the most robust way of applying the VJB correction to AVHRR data, considering that high noise in the red band (B1) caused by atmospheric effect makes the VJB method unstable. Firstly, our results show that for coarse spatial resolution, where the vegetation dynamics of the target don’t change significantly, deriving BRDF parameters from 15+ years of observations reduces the average noise by up to 7% in the Near Infrared (NIR) band and 6% in the NDVI, in comparison to using 3-year windows. Secondly, we find that the VJB method can be modified for AVHRR data to improve the robustness of the correction parameters and decrease the noise by an extra 8% and 9% in the red and NIR bands with respect to using the classical VJB inversion. We do this by using the Stable method, which obtains the volumetric BRDF parameter (V) based on its NDVI dependency, and then obtains the geometric BRDF parameter (R) through the inversion of just one parameter.

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.

The influence of spatial resolution on the angular variation patterns of optical reflectance as retrieved from MODIS and POLDER measurements

Multiangle remote sensing plays a central role in the development of algorithms for the retrieval of various surface biophysical parameters that are influenced by the reflectance anisotropy. Surface reflectance anisotropy is characterized by the Bidirectional Reflectance Distribution Function (BRDF). Within the past decade, space-borne multiangle observations acquired by the MODerate resolution Imaging Spectroradiometer (MODIS) sensor (which has a gridded spatial resolution of 500 m) and by the POLarization and Directionality of Earth Reflectances (POLDER) sensor (which has a spatial resolution of 6 × 7 km) have been used for a wide variety of global applications. However, it is necessary to fully understand the variability inherent in the surface BRDF information as retrieved from MODIS and POLDER at these two spatial resolutions to optimize their use. In this study, we make use of extensive POLDER Bidirectional Reflectance Factors (BRFs) selected from the entire archive of the POLDER BRDF database and standard MODIS BRDF parameter products (MCD43A1, Collection V005) that were geolocated within the same spatial extents as the POLDER data. The variability in surface BRDF is characterized by investigation of three BRDF model parameters as retrieved from MODIS and POLDER and a comprehensive index indicating the variations in the primary dome-bowl BRDF patterns (the anisotropic flat index (AFX)). The principal information content contained in these BRDF data is characterized by the general BRDF shapes (the BRDF archetypes) that account for >90% of the total variance in these BRDF data. A hotspot-revised BRDF model is used directly on top of the retrieved model BRDF parameters to capture the hotspot effect associated with these BRDF parameters. The main findings of this study show that the variability in surface BRDF, as extracted from the MODIS and POLDER datasets, shares six reciprocal BRDF archetypes. However, the 500-m MODIS BRDF data can uniquely capture some additional extreme BRDF shapes mainly due to the data's finer pixel scales. These original findings are very important, because subsequent albedo retrievals can be significantly impacted by the use of BRDFs of different resolutions. This study provides evidence concerning the influence of spatial resolution on angular variation patterns of optical reflectance as retrieved from the MODIS and POLDER BRDF products.

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.

Synergy of MODIS and AATSR for better retrieval of aerosol optical depth and land surface directional reflectance

This paper presents a new algorithm to simultaneously retrieve Aerosol Optical Depth (AOD) and land surface Bidirectional Reflectance Distribution Function (BRDF) from Advanced Along-Track Scanning Radiometer (AATSR) by adopting gradient optimization method. Different from traditional method the approach presented here can perform simultaneous retrieval from each individual AATSR swath rather than multiple days. A theoretical sensitivity study proves the proposed method is insensitive to the distortion of initial BRDF. The presented algorithm is tested on AATSR data around four different Aerosol Robotic Network (AERONET) sites representing various types of land surface. Compared with the four selected AERONET sites' AOD and BRDF-derived albedo from AERONET-based Surface Reflectance Validation Network (ASRVN) data in corresponding four AERONET sites, the presented algorithm proves considerable accuracy for various type of land surface with correlation of AOD ranging from 0.647 to 0.911 and correlation of BRDF-derived albedo ranging from 0.483 to 0.944. The intersensor comparison with Moderate Resolution Imaging Spectroradiometer (MODIS) 3km AOD dark target product reveals high coverage rate of the presented method especially in bright surface or nonvegetation area and the correlation between the two sensors reaches up to 0.967. The improved estimation of BRDF from AATSR retrieval in AERONET Beijing site is compared with MODIS MCD43B1 product. The relative differences in hemispherical albedo calculated from average BRDF shape function parameters between AATSR and MODIS product are 1.33%, 1.52%, 2.60% and 4.28% at 550nm, 670nm, 870nm and 1600nm respectively.

Surface roughness retrieval by inversion of the Hapke model: A multiscale approach

Surface roughness is a key property of soils that controls many surface processes and influences the scattering of incident electromagnetic waves at a wide range of scales. Hapke (2012b) designed a photometric model providing an approximate analytical solution of the Bidirectional Reflectance Distribution Function (BRDF) of a particulate medium: he introduced the effect of surface roughness as a correction factor of the BRDF of a smooth surface. This photometric roughness is defined as the mean slope angle of the facets composing the surface, integrated over all scales from the grain size to the local topography. Yet its physical meaning is still a question at issue, as the scale at which it occurs is not clearly defined. This work aims at better understanding the relative influence of roughness scales on soil BRDF and to test the ability of the Hapke model to retrieve a roughness that depicts effectively the ground truth. We apply a wavelet transform on millimeter digital terrain models (DTM) acquired over volcanic terrains. This method allows splitting the frequency band of a signal in several sub-bands, each corresponding to a spatial scale. We demonstrate that sub-centimeter surface features dominate both the integrated roughness and the BRDF shape. We investigate the suitability of the Hapke model for surface roughness retrieval by inversion on optical data. A global sensitivity analysis of the model shows that soil BRDF is very sensitive to surface roughness, nearly as much as the single scattering albedo according to the phase angle, but also that these two parameters are strongly correlated. Based on these results, a simplified two-parameter model depending on surface albedo and roughness is proposed. Inversion of this model on BRDF data simulated by a ray-tracing code over natural targets shows a good estimation of surface roughness when the assumptions of the model are verified, with a priori knowledge on surface albedo.

Analysis of Extracting Prior BRDF from MODIS BRDF Data

Many previous studies have attempted to extract prior reflectance anisotropy knowledge from the historical MODIS Bidirectional Reflectance Distribution Function (BRDF) product based on land cover or Normalized Difference Vegetation Index (NDVI) data. In this study, the feasibility of the method is discussed based on MODIS data and archetypal BRDFs. The BRDF is simplified into six archetypal BRDFs that represent different reflectance anisotropies. Five-year time series of MODIS BRDF data over three tiles are classified into six BRDF archetype classes according to the Anisotropy Flat indeX (AFX). The percentage of each BRDF archetype class in different land cover classes or every 0.1-NDVI interval is determined. Nadir BRDF-Adjusted Reflectances (NBARs) and NDVIs simulated from different archetypal BRDFs and the same multi-angular observations are compared to MODIS results to study the effectiveness of the method. The results show that one land cover type, or every 0.1-NDVI interval, contains all the potential BRDF shapes and that one BRDF archetypal class makes up no more than 40% of all data. Moreover, the differences between the NBARs and NDVIs simulated from different archetypal BRDFs are insignificant. In terms of the archetypal BRDF method and MODIS BRDF product, this study indicates that the land cover or NDVI is not necessarily related to surface reflectance anisotropy.

A general method to normalize Landsat reflectance data to nadir BRDF adjusted reflectance

Abstract The Landsat satellites have been providing spectacular imagery of the Earth's surface for over 40 years. However, they acquire images at view angles ± 7.5° from nadir that cause small directional effects in the surface reflectance. There are also variations with solar zenith angle over the year that can cause apparent change in reflectance even if the surface properties remain constant. When Landsat data from adjoining paths, or from long time series are used, a model of the surface anisotropy is required to adjust all Landsat observations to a uniform nadir view (primarily for visual consistency, vegetation monitoring, or detection of subtle surface changes). Here a generalized approach is developed to provide consistent view angle corrections across the Landsat archive. While this approach is not applicable for generation of Landsat surface albedo, which requires a full characterization of the surface bidirectional reflectance distribution function (BRDF), or for correction to a constant solar illumination angle across a wide range of sun angles, it provides Landsat nadir BRDF-adjusted reflectance (NBAR) for a range of terrestrial monitoring applications. The Landsat NBAR is derived as the product of the observed Landsat reflectance and the ratio of the reflectances modeled using MODIS BRDF spectral model parameters for the observed Landsat and for a nadir view and fixed solar zenith geometry. In this study, a total of 567 conterminous United States (CONUS) January and July 2010 Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper (ETM +) images that have swath edge overlapping paths sensed in alternating backscatter and forward scattering orientations were used. The average difference between Landsat 5 TM and Landsat 7 ETM + surface reflectance in the forward and backward scatter directions at the overlapping Landsat scan edges was quantified. The CONUS July view zenith BRDF effects were about 0.02 in the Landsat visible bands, and about 0.03, 0.05 and 0.06, in the 2.1 μm, 1.6 μm and near-infrared bands respectively. Comparisons of Landsat 5 TM and Landsat 7 ETM + NBAR derived using MODIS BRDF spectral model parameters defined with respect to different spatial and temporal scales, and defined with respect to different land cover types, were undertaken. The results suggest that, because the BRDF shapes of different terrestrial surfaces are sufficiently similar over the narrow 15° Landsat field of view, a fixed set of MODIS BRDF spectral model parameters may be adequate for Landsat NBAR derivation with little sensitivity to the land cover type, condition, or surface disturbance. A fixed set of BRDF spectral model parameters, derived from a global year of highest quality snow-free MODIS BRDF product values, are provided so users may implement the described Landsat NBAR generation method.

Evaluation of Medium Spatial Resolution BRDF-Adjustment Techniques Using Multi-Angular SPOT4 (Take5) Acquisitions

High-resolution sensor Surface Reflectance (SR) data are affected by surface anisotropy but are difficult to adjust because of the low temporal frequency of the acquisitions and the low angular sampling. This paper evaluates five high spatial resolution Bidirectional Reflectance Distribution Function (BRDF) adjustment techniques. The evaluation is based on the noise level of the SR Time Series (TS) corrected to a normalized geometry (nadir view, 45° sun zenith angle) extracted from the multi-angular acquisitions of SPOT4 over three study areas (one in Arizona, two in France) during the five-month SPOT4 (Take5) experiment. Two uniform techniques (Cst, for Constant, and Av, for Average), relying on the Vermote–Justice–Bréon (VJB) BRDF method, assume no variation in space of the BRDF shape. Two methods (VI-dis, for NDVI-based disaggregation and LC-dis, for Land-Cover based disaggregation) are based on disaggregation of the MODIS-derived BRDF VJB parameters using vegetation index and land cover, respectively. The last technique (LUM, for Look-Up Map) relies on the MCD43 MODIS BRDF products and a crop type data layer. The VI-dis technique produced the lowest level of noise corresponding to the most effective adjustment: reduction from directional to normalized SR TS noises by 40% and 50% on average, for red and near-infrared bands, respectively. The uniform techniques displayed very good results, suggesting that a simple and uniform BRDF-shape assumption is good enough to adjust the BRDF in such geometric configuration (the view zenith angle varies from nadir to 25°). The most complex techniques relying on land cover (LC-dis and LUM) displayed contrasting results depending on the land cover.

Measuring the Directional Variations of Land Surface Reflectance From MODIS

The directional variation of land surface reflectance generates an apparent noise in the time series acquired from satellites with variable observation geometries, which can be corrected through appropriate modeling of the bidirectional reflectance distribution function (BRDF). In a previous paper, we described and validated the VJB method that estimates a target BRDF shape and corrects for directional effects and yet retains the high temporal resolution of the measurement. Here, we analyze its potential to measure the BRDF of targets at the 0.5-km resolution of Moderate Resolution Imaging Spectroradiometer (MODIS). The description of the BRDF in the NASA MCD43A1 product shows very large temporal variations that are unrealistic. However, the reflectance time series, normalized to a standard observation geometry using this modeling, have a similar quality as those derived using VJB. Conversely, the MCD43A1 modeled reflectances for a nonstandard geometry are unrealistically variable. These results indicate that the standard BRDF model inversion used to derive the MCD43A1 product is underconstrained due to the limited directional sampling of the 16-day composite period. The apparent noise in the corrected reflectance time series is significantly larger than the one obtained at lower spatial resolution, and is very much a function of the spatial heterogeneity of the area surrounding the target. These results strongly indicate that the multitemporal MODIS measurement at high spatial resolution (0.5 km) is affected by a change in the effective resolution for off-nadir observation and by inaccurate registration. The resulting noise in the measurements precludes an accurate measurement of the BRDF at such a scale.

Modeling Land Surface Reflectance Coupled BRDF for HJ-1/CCD Data of Rugged Terrain in Heihe River Basin, China

Rugged terrain significantly affects the Huan Jing (HJ)-1/CCD reflectance at the earth's surface because the sloping surfaces change the sun-surface-sensor geometry. It is necessary to consider the land surface directional reflectance and reduce topographic effects to obtain the correct reflectance. An atmospheric model 6S (second simulation of the satellite signal in the solar spectrum) coupled with bidirectional reflectance distribution function (BRDF) shape, which is well suited to estimate the HJ-1/CCD land surface reflectance of rugged terrain and flat surface (BRATC, BRDF-based atmospheric and topographic correction), is reformulated in this paper. The BRDF shape, a statistics-based MODIS (moderate-resolution imaging spectroradiometer) BRDF prior-knowledge look-up table (LUT) stored in this algorithm, is applied to the HJ-1/CCD reflectance estimation that covers Heihe River Basin, China. The results of the indirect validation of the visual image and the linear relationship between the reflectance and the cosine of the solar relative incident angle show that the algorithm effectively reduces topographic effects. Compared with three land cover field measurement reflectances, the HJ-1/CCD-corrected reflectance is consistently good with an overall RMSE as low as 0.0128. The proposed method could be designed for an operational system of HJ-1/CCD reflectance products.

An approach for the long-term 30-m land surface snow-free albedo retrieval from historic Landsat surface reflectance and MODIS-based a priori anisotropy knowledge

Land surface albedo has been recognized by the Global Terrestrial Observing System (GTOS) as an essential climate variable crucial for accurate modeling and monitoring of the Earth's radiative budget. While global climate studies can leverage albedo datasets from MODIS, VIIRS, and other coarse-resolution sensors, many applications in heterogeneous environments can benefit from higher-resolution albedo products derived from Landsat. We previously developed a “MODIS-concurrent” approach for the 30-meter albedo estimation which relied on combining post-2000 Landsat data with MODIS Bidirectional Reflectance Distribution Function (BRDF) information. Here we present a “pre-MODIS era” approach to extend 30-m surface albedo generation in time back to the 1980s, through an a priori anisotropy Look-Up Table (LUT) built up from the high quality MCD43A BRDF estimates over representative homogenous regions. Each entry in the LUT reflects a unique combination of land cover, seasonality, terrain information, disturbance age and type, and Landsat optical spectral bands. An initial conceptual LUT was created for the Pacific Northwest (PNW) of the United States and provides BRDF shapes estimated from MODIS observations for undisturbed and disturbed surface types (including recovery trajectories of burned areas and non-fire disturbances). By accepting the assumption of a generally invariant BRDF shape for similar land surface structures as a priori information, spectral white-sky and black-sky albedos are derived through albedo-to-nadir reflectance ratios as a bridge between the Landsat and MODIS scale. A further narrow-to-broadband conversion based on radiative transfer simulations is adopted to produce broadband albedos at visible, near infrared, and shortwave regimes. We evaluate the accuracy of resultant Landsat albedo using available field measurements at forested AmeriFlux stations in the PNW region, and examine the consistency of the surface albedo generated by this approach respectively with that from the “concurrent” approach and the coincident MODIS operational surface albedo products. Using the tower measurements as reference, the derived Landsat 30-m snow-free shortwave broadband albedo yields an absolute accuracy of 0.02 with a root mean square error less than 0.016 and a bias of no more than 0.007. A further cross-comparison over individual scenes shows that the retrieved white sky shortwave albedo from the “pre-MODIS era” LUT approach is highly consistent (R2=0.988, the scene-averaged low RMSE=0.009 and bias=−0.005) with that generated by the earlier “concurrent” approach. The Landsat albedo also exhibits more detailed landscape texture and a wider dynamic range of albedo values than the coincident 500-m MODIS operational products (MCD43A3), especially in the heterogeneous regions. Collectively, the “pre-MODIS” LUT and “concurrent” approaches provide a practical way to retrieve long-term Landsat albedo from the historic Landsat archives as far back as the 1980s, as well as the current Landsat-8 mission, and thus support investigations into the evolution of the albedo of terrestrial biomes at fine resolution.

An Anisotropic Flat Index (AFX) to derive BRDF archetypes from MODIS

Spectral vegetation indices can be generalized as a function of surface reflectance with respect to wavelength. However, there is significant information on vegetation structure embedded in the anisotropic effects of the target. In this study, we describe and characterize a new vegetation index, the Anisotropic Flat Index (AFX) that captures this anisotropic scattering information and can be derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF) product suite (MOD43A1). The AFX is created by normalization of net scattering magnitude (obtained from volumetric and geometric-optical scattering) with the isotropic scattering. The AFX summarizes the variability of basic dome-bowl anisotropic reflectance patterns of the terrestrial surface. A classification scheme for BRDF typology is created based on AFX archetypes that capture characteristic BRDF shape types. This study fully characterizes AFX in a number of steps. First, sensitivity to random noise and observation geometries is explored by comparing the AFX with other variables derived from field measurements that comprehensively sample the viewing hemisphere. Second, AFX is compared with normalized difference vegetation index (NDVI) values using field measurements from many ground campaigns, as well as global MODIS observations from EOS Land Validation Core Sites (LVCS). Third, a BRDF typology is developed by classification of an a priori database of BRDF archetypes from field measurements, and from MODIS observations that cover the full range of vegetation types from grasslands to closed forest (MCD43A). Fourth, the response of AFX to the parameter variability of canopy architectures and background optical properties for three vegetation types with discontinuous woody canopies is investigated through the use of a 5-Scale BRDF model simulation. Finally, global BRDF archetypes are mapped and discussed through the use of a global high-quality MODIS BRDF/albedo gap filled product (MCD43GF). The results show that the AFX summarizes BRDF archetypes and provides additional information on vegetation structure and other anisotropic reflectance characteristics of the land surface.

Aerosol optical depth (AOD) retrieval using simultaneous GOES-East and GOES-West reflected radiances over the western United States

Abstract. Aerosol optical depth (AOD) in the western United States is observed independently by both the (Geostationary Operational Environmental Satellites) GOES-East and GOES-West imagers. The GASP (GOES Aerosol/Smoke Product) aerosol optical depth retrieval algorithm treats each satellite as a unique sensor and thus obtains two separate aerosol optical depth values at the same time for the same location. The TOA (the top of the atmosphere) radiances and the associated derived optical depths can be quite different due to the different viewing geometries with large difference in solar-scattering angles. In order to fully exploit the simultaneous observations and generate consistent AOD retrievals from the two satellites, the authors develop a new "hybrid" aerosol optical depth retrieval algorithm that uses data from both satellites. The algorithm uses both GOES-East and GOES-West visible channel TOA reflectance and daily average AOD from GOES Multi-Angle Implementation of Atmospheric Correction (GOES-MAIAC) on low AOD days (AOD less than 0.3), when diurnal variation of AOD is low, to retrieve surface BRDF (Bidirectional Reflectance Distribution Function). The known BRDF shape is applied on subsequent days to retrieve BRDF and AOD. The algorithm is validated at three AERONET sites over the western US. The AOD retrieval accuracy from the "hybrid" technique using the two satellites is similar to that from one satellite over UCSB (University of California Santa Barbara) and Railroad Valley, Nevada. Improvement of the accuracy is observed at Boulder, Colorado. The correlation coefficients between the GOES AOD and AERONET AOD are in the range of 0.67 to 0.81. More than 74% of AOD retrievals are within the error of ±(0.05 + 0.15 τ) compared to AERONET AOD. The hybrid algorithm has more data coverage compared to the single satellite retrievals over surfaces with high surface reflectance. For single observation areas the number of valid AOD data increases from the use of two-single satellite algorithms by 5–80% for the three sites. With the application of the new algorithm, consistent AOD retrievals and better retrieval coverages can be obtained using the data from the two GOES satellite imagers.

Multi-angle implementation of atmospheric correction for MODIS (MAIAC): 3. Atmospheric correction

This paper describes the atmospheric correction (AC) component of the Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC) which introduces a new way to compute parameters of the Ross-Thick Li-Sparse (RTLS) Bi-directional reflectance distribution function (BRDF), spectral surface albedo and bidirectional reflectance factors (BRF) from satellite measurements obtained by the Moderate Resolution Imaging Spectroradiometer (MODIS). MAIAC uses a time series and spatial analysis for cloud detection, aerosol retrievals and atmospheric correction. It implements a moving window of up to 16days of MODIS data gridded to 1km resolution in a selected projection. The RTLS parameters are computed directly by fitting the cloud-free MODIS top of atmosphere (TOA) reflectance data stored in the processing queue. The RTLS retrieval is applied when the land surface is stable or changes slowly. In case of rapid or large magnitude change (as for instance caused by disturbance), MAIAC follows the MODIS operational BRDF/albedo algorithm and uses a scaling approach where the BRDF shape is assumed stable but its magnitude is adjusted based on the latest single measurement. To assess the stability of the surface, MAIAC features a change detection algorithm which analyzes relative change of reflectance in the Red and NIR bands during the accumulation period. To adjust for the reflectance variability with the sun-observer geometry and allow comparison among different days (view geometries), the BRFs are normalized to the fixed view geometry using the RTLS model. An empirical analysis of MODIS data suggests that the RTLS inversion remains robust when the relative change of geometry-normalized reflectance stays below 15%. This first of two papers introduces the algorithm, a second, companion paper illustrates its potential by analyzing MODIS data over a tropical rainforest and assessing errors and uncertainties of MAIAC compared to conventional MODIS products.

Improving MODIS land cover classification by combining MODIS spectral and angular signatures in a Canadian boreal forest

This study explores the use of reflectance anisotropy as described by the Bidirectional Reflectance Distribution Function (BRDF) as an additional source of information to improve land surface classification accuracies in a Canadian boreal forest region through the use of a decision tree classifier (C4.5). This effort primarily uses a daily rolling version of the operational algorithm developed for Direct Broadcast to generate 500 m 16-day daily rolling data sets in the study region. Descriptive statistic and statistically rigorous techniques are used to assess classification accuracies based on confusion matrices and a 10-fold cross-validation method. The results show that the inclusion of additional 7-band model anisotropic parameter group (volumetric (VOL) plus geometric (GEO)) with spectral feature group (nadir BRDF-adjusted reflectance (NABR) plus Enhanced Vegetation Index (EVI)) is most useful in classification, increasing overall accuracies by 5.68%. The most improvements of per-class accuracies are seen for Wetland shrub class with user's and producer's accuracies increasing by up to 17.7% and 11.3%, respectively. Increases on the order of 5% to 15% are encountered for the classes of Wetland herb, Wetland tree, Coniferous dense, and Coniferous open with no detriments to other candidate classes. The inclusion of the 2-band BRDF shape indicator group in the classification is, however, not as useful as inclusion of the 7-band model anisotropic parameter group in improving the classification accuracies. A further investigation of the classification accuracies regarding reflectance anisotropy for the sampling pixels within each class shows that land cover types that are dominated by geometric-optical scattering type or a mixture of scattering types are relatively difficult to be classified with spectral feature group alone, and the inclusion of additional BRDF features can significantly improve classification accuracies for these land cover types. However, despite their use as ancillary data, this study also confirms that the spectral feature group provided with NBAR and EVI captures the major information content regarding land cover types, exceeding the information content contained in the model anisotropic parameter group provided with the 7-band VOL and GEO parameters of RossThick-LiSparse-Reciprocal (RTLSR) models.

An Evaluation of the Use of Atmospheric and BRDF Correction to Standardize Landsat Data

Normalizing for atmospheric and land surface bidirectional reflectance distribution function (BRDF) effects is essential in satellite data processing. It is important both for a single scene when the combination of land covers, sun, and view angles create anisotropy and for multiple scenes in which the sun angle changes. As a consequence, it is important for inter-sensor calibration and comparison. Procedures based on physics-based models have been applied successfully with the Moderate Resolution Imaging Spectroradiometer (MODIS) data. For Landsat and other higher resolution data, similar options exist. However, the estimation of BRDF models using internal fitting is not available due to the smaller variation of view and solar angles and infrequent revisits. In this paper, we explore the potential for developing operational procedures to correct Landsat data using coupled physics-based atmospheric and BRDF models. The process was realized using BRDF shape functions derived from MODIS with the MODTRAN 4 radiative transfer model. The atmospheric and BRDF correction algorithm was tested for reflectance factor estimation using Landsat data for two sites with different land covers in Australia. The Landsat reflectance values had a good agreement with ground based spectroradiometer measurements. In addition, overlapping images from adjacent paths in Queensland, Australia, were also used to validate the BRDF correction. The results clearly show that the algorithm can remove most of the BRDF effect without empirical adjustment. The comparison between normalized Landsat and MODIS reflectance factor also shows a good relationship, indicating that cross calibration between the two sensors is achievable.