Bidirectional Reflectance Distribution Function Product Research Articles

Directional Applicability Analysis of Albedo Retrieval Using Prior BRDF Knowledge

Surface albedo measures the proportion of incoming solar radiation reflected by the Earth’s surface. Accurate albedo retrieval from remote sensing data usually requires sufficient multi-angular observations to account for the surface reflectance anisotropy. However, most middle and high-resolution remote sensing satellites lack the capability to acquire sufficient multi-angular observations. Existing algorithms for retrieving surface albedo from single-direction reflectance typically rely on land cover types and vegetation indices to extract the corresponding prior knowledge of surface anisotropic reflectance from coarse-resolution Bidirectional Reflectance Distribution Function (BRDF) products. This study introduces an algorithm for retrieving albedo from directional reflectance based on a 3 × 3 BRDF archetype database established using the 2015 global time-series Moderate Resolution Imaging Spectro-radiometer (MODIS) BRDF product. For different directions, BRDF archetypes are applied to the simulated MODIS directional reflectance to retrieve albedo. By comparing the retrieved albedos with the MODIS albedo, the BRDF archetype that yields the smallest Root Mean Squared Error (RMSE) is selected as the prior BRDF for the direction. A lookup table (LUT) that contains the optimal BRDF archetypes for albedo retrieval under various observational geometries is established. The impact of the number of BRDF archetypes on the accuracy of albedo is analyzed according to the 2020 MODIS BRDF. The LUT is applied to the MODIS BRDF within specific BRDF archetype classes to validate its applicability under different anisotropic reflectance characteristics. The applicability of the LUT across different data types is further evaluated using simulated reflectance or real multi-angular measurements. The results indicate that (1) for any direction, a specific BRDF archetype can retrieve a high-accuracy albedo from directional reflectance. The optimal BRDF archetype varies with the observation direction. (2) Compared to the prior BRDF knowledge obtained through averaging method, the BRDF archetype LUT based on the 3 × 3 BRDF archetype database can more accurately retrieve the surface albedo. (3) The BRDF archetype LUT effectively eliminates the influence of surface anisotropic reflectance characteristics in albedo retrieval across different scales and types of data.

Asymmetry in the Diurnal Variation of Land Surface Albedo and Its Impacts on Daily Mean Albedo Estimation

AbstractDaily mean albedo, a crucial variable of the earth radiation budget, is significantly affected by the diurnal variation of land surface albedo (DVLSA). The DVLSA typically exhibits asymmetry, thereby affecting the estimation of the daily mean albedo. However, the asymmetry in the DVLSA is generally ignored in daily mean albedo estimation. In this study, we investigated the influencing factors of the asymmetry in the DVLSA and evaluated its impacts on estimating the daily mean albedo based on field observations and simulated data. Our findings reveal that the asymmetry in the DVLSA varies among land cover types, with forests exhibiting more pronounced asymmetry compared to croplands, grasslands, and bare soil. The diurnal variation of the atmospheric conditions is the primary factor controlling the asymmetry in the DVLSA, with that of land surface conditions being a secondary factor. Neglecting the asymmetry in the DVLSA leads to estimation error in daily mean albedo, particularly pronounced during winter. The relative error of daily mean albedo can exceed 10% when the mean asymmetry index of diffuse irradiance fraction reaches 40%. However, the DVLSA retrieved from the satellite Bidirectional Reflectance Distribution Function product inadequately captures asymmetry, resulting in a relative error of approximately 13.7% in estimating daily mean albedo.

First retrieval of daily 160 m aerosol optical depth over urban areas using Gaofen-1/6 synergistic observations: Algorithm development and validation

The satellite-based aerosol optical depth (AOD), which can provide continuous spatial observations of aerosol loadings, is widely adopted to estimate atmospheric environmental quality and evaluate its risk for human health. However, current satellite-retrieved AOD products characterized by a comparatively coarse spatial resolution (≥1 km) can hardly analyze the structure of atmospheric pollution or its correlation with urban landscapes over populated urban areas. Existed studies have tried to address this deficiency by retrieving high-resolution AOD using Landsat images, whose long revisit period (16 days nominally), however, largely limits its applications related to urban air pollution research. To achieve both high spatial resolution and expected revisit period from satellite observation, in this study, a comprehensive AOD retrieval framework is developed for Wide-Field-of-View (WFV) satellite sensors to yield a daily AOD dataset over urban areas with 160 m spatial resolution, based on the Gaofen-1 and Gaofen-6 synergistic observations. To address the crucial challenge that the high spatial resolution and complex urban landscape both contribute a dramatic variation of land surface bidirectional reflectance, a Simulated-Annealing-coupled Semiempirical Modified Rahman-Pinty-Verstraete (SAS-MRPV) scheme is proposed to model and estimate the land surface reflectance (LSR) in the AOD retrieval framework, where the SAS-MRPV scheme is implemented using simulated annealing iteration initialized by the bidirectional reflectance distribution function (BRDF) products from the Moderate Resolution Imaging Spectrometer (MODIS) as a priori knowledge. The validation results demonstrate that the Gaofen WFV AOD retrievals exhibit good agreement with the ground-based AErosol RObotic NETwork (AERONET) and SONET (Sun-sky radiometer Observation NETwork) AOD, demonstrating the correlation coefficient and the expected error (EE)±(0.05 + 0.15AODAERONET/SONET) ratio respectively reaching up to 0.97 and over 80 %, and only slight bias fluctuations are found under different land cover types, aerosol loading, and seasonal conditions. Moreover, the validation results of Gaofen WFV AOD retrievals, with LSR estimated based on the SAS-MRPV scheme, the MODIS BRDF Products, and the Minimum Reflectance Technique (MRT) method, demonstrate better accuracy and completeness of AOD retrievals using the SAS-MPRV scheme over both natural and impervious land surfaces, indicating the stability and reliability of proposed SAS-MRPV LSR determination scheme in WFV AOD retrieval. In addition to that, the error analyses and quality control results demonstrate that the SAS-MRPV scheme is effective and necessary to guarantee the reliability and accuracy of land surface bidirectional reflectance estimation by identifying and excluding the land surface pixels unsuitable for LSR modeling, which mitigates the uncertainty and yield better accuracy in the final WFV AOD products. Furthermore, the inter-comparison between WFV-derived AOD against the operational MODIS Deep Blue (10 km), Dark Target (3 km), and Multi-Angle Implementation of Atmospheric Correction (MAIAC, 1 km) AOD products demonstrates that the 160 m WFV AOD retrievals, on the basis of obtaining similar aerosol overall descriptions as MODIS AOD retrievals, possess the capability to characterize the spatial distribution of atmospheric pollution at a finer scale with smoother variation under both clean and polluted conditions. With outstanding accuracy and reliable performance, the developed comprehensive high-resolution AOD retrieval framework exhibits substantial potential for operational AOD retrieval of Gaofen WFV satellite observations, which could be directly applied to other urban areas supporting further studies related to air pollution emission management and health risk assessment at extra-fine spatial scale.

Retrieving land surface reflectance anisotropy with Sentinel-3 observations and prior BRDF model constraints

Sentinel-3 supports governmental policies and European programmes such as the Copernicus Global Land Service with daily monitoring of the land surface by measuring the Earth's surface with its multi-band wide-swath visible and near-infrared radiometers. To fully exploit Sentinel-3 data sets, performing a correction of directional effects caused by the land surface reflectance anisotropy properties is mandatory. This correction requires first an estimation of the bidirectional reflectance distribution function (BRDF) to be properly achieved. The quality and robustness of global estimates of key land surface variables derived from space-borne sensors depend clearly on an accurate assessment of the spectral BRDF.Here, we present an algorithm aiming at harnessing the Sentinel-3's wide imaging swath to retrieve the land surface BRDF, approximating it via a kernel-driven semi-empirical model which can then be used to normalise the observations to a common Sun-sensor geometry. Our algorithm, named ReBeLS (Regularised BRDF inversion for Land Surface), uses a temporally regularised BRDF inversion approach and, together with prior knowledge of the land surface BRDF obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors, assimilates Sentinel-3 surface reflectance observations. We focus on the results derived from observations provided by the Sentinel-3 Ocean and Land Colour Instrument (OLCI) sensors; however, the algorithm can also be applied to reflectance observations acquired by the Sea and Land Surface Temperature Radiometer (SLSTR). The retrieved BRDF model parameters reproduce within uncertainties the directional signature as seen by the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor. The generated Sentinel-3 normalised surface reflectance products are a significant advance compared with time series of directional surface reflectance and maximum value composite images, showing a statistically significant (p≪0.05) correlation (r>0.9) with VIIRS nadir BRDF-adjusted surface reflectance. Furthermore, the ReBeLS Sentinel-3 BRDF products offer enough flexibility to allow users to compute spectral surface albedos or to normalise surface reflectance to any Sun-sensor configuration.

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

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

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

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

Retrieval and validation of forest background reflectivity from daily Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF) data across European forests

Abstract. Information about forest background reflectance is needed for accurate biophysical parameter retrieval from forest canopies (overstory) with remote sensing. Separating under- and overstory signals would enable more accurate modeling of forest carbon and energy fluxes. We retrieved values of the normalized difference vegetation index (NDVI) of the forest understory with the multi-angular Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF)/albedo data (gridded 500 m daily Collection 6 product), using a method originally developed for boreal forests. The forest floor background reflectance estimates from the MODIS data were compared with in situ understory reflectance measurements carried out at an extensive set of forest ecosystem experimental sites across Europe. The reflectance estimates from MODIS data were, hence, tested across diverse forest conditions and phenological phases during the growing season to examine their applicability for ecosystems other than boreal forests. Here we report that the method can deliver good retrievals, especially over different forest types with open canopies (low foliage cover). The performance of the method was found to be limited over forests with closed canopies (high foliage cover), where the signal from understory becomes too attenuated. The spatial heterogeneity of individual field sites and the limitations and documented quality of the MODIS BRDF product are shown to be important for the correct assessment and validation of the retrievals obtained with remote sensing.

Radiometric Cross-Calibration of Large-View-Angle Satellite Sensors Using Global Searching to Reduce BRDF Influence

Satellite sensors with large view angles can provide wide-swath imaging for earth observations, which pose new challenges for cross-calibration due to bidirectional reflectance distribution function (BRDF). To address these challenges, we adopted global searching (GS) algorithms to “search” calibration coefficients with BRDF considered. The GS-based methods were implemented to calibrate two Chinese large-view-angle sensors: the Gaofen-1 first wide-field-of-view (WFV1) camera and Gaofen-4 panchromatic multispectral sensor (PMS) with Landsat-8/Operational Land Imager (OLI) as references. Validations were conducted by evaluating the top of atmosphere (TOA) radiance and surface reflectance using synchronous OLI data. The mean relative biases (MRBs) of the GS-derived TOA radiance for the WFV1 were smaller than 5.5% compared with the OLI-simulated TOA radiance, while those of using official coefficients were up to 13%. The performances of the GS method and traditional method using the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF products for BRDF correction are comparable. The GS-based scheme has the potential to correct BRDF during cross-calibration and thus free cross-calibration of large-view-angle sensors from BRDF models and products.

Assessment of bidirectional reflectance effects on desert and forest for radiometric cross-calibration of satellite sensors

Cross-calibration is a wildly used approach to radiometrically calibrate satellite sensors to meet uncertainty requirements. However, its accuracy is greatly influenced by bidirectional reflectance distribution function (BRDF) effects. To understand such influences, we analyzed the long-term BRDF characteristics of two commonly used cross-calibration targets (‘bright’ desert and ‘dark’ forest) using long-term (2002 – 2016) Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF products. BRDF characteristics derived by MODIS observations of both land cover types had 365-day cycles. Furthermore, both solar and sensor zenith angles showed more influence on BRDF than relative azimuth angles, and BRDF variations were approximately 10 times sensitive when solar or sensor zenith angles surpassed the boundary of 75°, which is therefore not recommended for selecting image pairs between target and reference satellite sensors for cross-calibration. Due to cloud cover, three prevailing interpolation methods (nearest, linear, and spline interpolation) were also assessed in their capacity to fill temporally missing BRDF products. Linear interpolation was preferred when valid MODIS scenes were more than two in five days, while nearest interpolation outperformed the other two methods for no more than two valid MODIS scenes in five days. In addition, BRDF look up tables (LUTs) were established for radiometrically cross-calibrating satellite sensors when BRDF products were unavailable. The LUTs were further validated with Landsat-8/Operational Land Imager (OLI) top of atmosphere (TOA) radiance simulated by the Second Simulation of the Satellite Signal in the Solar Spectrum model (6S) with BRDF effects corrected using the BRDF LUTs and MODIS BRDF products for typical geometries at two sites. High consistency was achieved with mean biases less than 0.3%, which suggests that BRDF LUTs could be used as alternatives when BRDF products are unavailable. These results provide guidelines to improve the accuracy of cross-calibration including image-matchup selecting criteria, efficient BRDF effect removal, and selection of potential calibration sites.

Global 500 m clumping index product derived from MODIS BRDF data (2001–2017)

The vegetation clumping index (CI) quantifies the degree to which foliage deviates from a random distribution, and therefore, is an important canopy structural parameter that governs the photosynthesis and evapotranspiration processes in terrestrial ecosystems. While the temporal variation of CI has been investigated in many field studies, there has not been a global CI product that captured its seasonal and inter-annual variations. In this study, a look-up table (LUT) method was developed based on an improved Normalized Difference between Hotspot and Darkspot (NDHD) method to estimate the daily CI from the MODIS V006 MCD43 BRDF (bidirectional reflectance distribution function) product. This new CI product is consistent with field measurements at 33 sites distributed globally (R2 = 0.38, RMSE = 0.04, and bias = −0.01). CI shows stronger seasonal variations for deciduous broadleaf forest (DBF) and mixed forests than other vegetation types. The large seasonal differences between evergreen and deciduous vegetation, especially for DBF and deciduous shrubs, allow the CI to be used to detect seasonal structural changes and classify vegetation. The global CI shows a negative correlation to the leaf area index (LAI) for all land cover types, especially forests. The global CI demonstrates an inter-annual variation that correlated with global precipitation (R2 = 0.34). In addition, the global CI shows a global decreasing trend from 2001 to 2017 (−0.007/decade) that was consistent with the increasing LAI (0.049/decade). The time series of CI provides a new perspective for understanding the structural characteristics of vegetation in global ecosystem studies.

Influence of Snow on the Magnitude and Seasonal Variation of the Clumping Index Retrieved from MODIS BRDF Products

The foliage Clumping Index (CI) is an important vegetation structure parameter that allows for the accurate separation of sunlit and shaded leaves in a canopy. The CI and its seasonality are critical for global Leaf Area Index (LAI) estimating and ecological modelling. However, the cover of snow tends to reduce the reflectance anisotropy of the vegetation canopy and thus probably influences CI estimates. In this paper, we investigate the influence of snow on the magnitude and seasonal variation of the CI retrieved from Moderate-resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF) products based on field-measured CI and statistics from the global MODIS CI product. We find that the backup algorithm can effectively correct abnormally large CI values and obtain more reasonable CI retrievals than the main algorithm without any constraints in snow-covered areas. Validation indicates that the time-series CI product shows the potential in investigating the trajectories of the clumping effect in snow seasons. For evergreen forests, the clumping effect is relatively stable throughout the year; however, for deciduous vegetation types, CI values tend to display significant seasonal variations. This study suggests that the latest version of the global MODIS CI product, in which the backup algorithm is used to process the snow-covered pixels, has improved accuracy for CI retrievals in snow-covered areas and thus is probably more suitable as the input parameter for ecological and meteorological models.

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.

ANALYSIS OF ACCURACY OF MODIS BRDF PRODUCT (MCD43 C6) BASED ON MISR LAND SURFACE BRF PRODUCT – A CASE STUDY OF THE CENTRAL PART OF NORTHEAST ASIA

Abstract. EOS/MODIS land surface Bi-directional Reflectance Distribution Function (BRDF) product (MCD43), with the latest version C6, is one of the most important operational BRDF products with global coverage. The core sub-product MCD43A1 stores 3 parameters of the RossThick-LiSparseR semi-empirical kernel-driven BRDF model. It is important for confident use of the product to evaluate the accuracy of bi-directional reflectance factor (BRF) predicted by MCD43A1 BRDF model (mBRF). A typical region in the central part of Northeast Asia is selected as the study area. The performance of MCD43A1 BRDF model is analyzed in various observation geometries and phenological phases, using Multi-angle Imaging SpectroRadiometer (MISR) land-surface reflectance factor product (MILS_BRF) as the reference data. In addition, MODIS products MCD12Q1 and MOD/MYD10A1 are used to evaluate the impacts of land cover types and snow covers on the model accuracy, respectively. The results show an overall excellent performance of MCD43A1 in representing the anisotropic reflectance of land surface, with root mean square error (RMSE) of 0.0262 and correlation coefficient (R) of 0.9537, for all available comparable samples of MILS_BRF and mBRF pairs. The model accuracy varies in different months, which is related to the phenological phases of the study area. The accuracy for pixels labelled as ‘snow’ by MCD43 is obviously low, with RMSE/R of 0.0903/0.8401. Ephemeral snowfall events further decrease the accuracy, with RMSE/R of 0.1001/0.7715. These results provide meaningful information to MCD43 users, especially those, whose study regions are subject to phenological cycles as well as snow cover and change.

An algorithm for the retrieval of the clumping index (CI) from the MODIS BRDF product using an adjusted version of the kernel-driven BRDF model

The clumping index (CI) characterizes the grouping of foliage relative to a random spatial distribution of leaves and is an important structural parameter for plant canopies that can influence canopy radiation regimes. Consequently, the CI is very useful for ecological and meteorological models. One method used to retrieve the CIs of plant canopies is to construct a linear relationship between the CI and the normalized difference between hotspot and dark spot (NDHD) angular index. This method requires a particularly accurate reconstruction of hotspot signatures, which are difficult to measure. In this study, we propose a framework to retrieve CIs from Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF) parameters, which are generally based on linear CI-NDHD equations. The main algorithm is designed to retrieve CIs in the closed interval [0.33, 1.00]. This range is derived from the CI-NDHD equations and is thus called as the physical range here, although a modified lower boundary can be implemented in the future if necessary. If CIs are outside of this range, then a backup algorithm is designed to reprocess these so-called outlier CIs. The hotspot-adjusted version of the RossThick-LiSparseReciprocal (RTLSR) model (i.e., the RTCLSR model) is employed to reconstruct the hotspot signatures for the MODIS BRDF parameters. This method simplifies the hotspot reconstruction by using two hotspot parameters that are not distinctly scale-dependent particularly in the context of an inhomogeneous coarse spatial resolution. To evaluate this algorithm framework, we collect dozens of global field-measured CIs and calculate their determination coefficient (R2), root mean square error (RMSE) and bias relative to MODIS CIs derived using both the main algorithm and the backup algorithm. Our results show that this framework can derive MODIS CIs with a high accuracy (i.e., R2 = 0.80 (0.72), RMSE = 0.07 (0.12), bias = −0.03 (−0.10)) using the main (backup) algorithms and that it shows promise for various ecological applications, especially in combination with the leaf area index (LAI).

Evaluation of the VIIRS BRDF, Albedo and NBAR products suite and an assessment of continuity with the long term MODIS record

Bidirectional Reflectance Distribution Function (BRDF) model parameters, Albedo quantities, and Nadir BRDF Adjusted Reflectance (NBAR) products derived from the Visible Infrared Imaging Radiometer Suite (VIIRS), on the Suomi-NPP (National Polar-orbiting Partnership) satellite are evaluated with spatially representative in situ tower albedometer measurements and through comparison with the MODerate Resolution Imaging Spectroradiometer (MODIS) long term record. White Sky Albedo (WSA), Black Sky Albedo (BSA), NBAR, and Quality Assurance (QA) results show that the VIIRS algorithm is performing well at the global scale and is able to provide global data products comparable with the heritage MODIS (with an absolute bias of 0.0069 for shortwave broadband WSA) despite the spectral, angular, and effective spatial resolution differences between the two sensors. Both VIIRS and MODIS albedo are shown to agree well with in situ albedo measurements at a number of spatially representative sites. This evaluation provides confidence that the high quality, daily VIIRS BRDF model parameters, Albedo, and NBAR products will be able to provide the long term continuity required by the modeling and monitoring communities.

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.

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.

New approaches in multi-angular proximal sensing of vegetation: Accounting for spatial heterogeneity and diffuse radiation in directional reflectance distribution models

The development of tower-mounted automated multi-angular hyperspectral systems has brought new opportunities and challenges for the characterization of the Bidirectional Reflectance Distribution Function (BRDF) on a continuous basis. This study describes the deployment of one of these systems in a Mediterranean savanna ecosystem (AMSPEC-MED), and proposes new approaches for modeling of directional effects. In this study, a Hemispherical-Directional Reflectance Distribution Function (HDRDF) was introduced in order to quantify the effect of diffuse radiation on the estimation of BRDF. The HDRDFs of the two covers of the ecosystem - trees and grasses - were un-mixed using a 3-Dimensional (3-D) model of the observed scene. Up-scaling HDRDF estimates to MODIS BRDF product (r2∈[0.74, 0.86]) and down-scaling to hand held spectral measurements (r2=0.88) achieved a reasonable accuracy (RMSE∈[1.81, 3.14]). Despite the uncertainties in the estimation of diffuse irradiance and the 3-D representation of the scene, HDRDF un-mixing demonstrates the potential of automated multi-angular proximal sensing to study vegetation properties in heterogeneous ecosystems and the correction of directional effects of different sources.

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.

Radiometric cross-calibration of Gaofen-1 WFV cameras using Landsat-8 OLI images: A solution for large view angle associated problems

Abstract Four wide-field-of-view (WFV) instruments are onboard the Gaofen-1 (or GF-1) satellite, providing a combined swath of ~ 800 km. However, observations with large view angles pose new challenges for radiometric cross-calibration with a simple image-based method when using Landsat-8 Operational Land Imager (OLI) data as a reference. Based on radiative transfer modeling, a novel radiometric cross-calibration method has been proposed in this study to solve large view angle-associated problems. The Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products were used to simulate the top of atmosphere (TOA) signal of the reference and target instruments, and the unequal bidirectional effects were corrected using MODIS bi-directional reflectance distribution function (BRDF) products. Extensive validations with both satellite data and in situ measurements revealed an uncertainty of ~ 8% for the newly produced cross-calibration coefficients when they were used to calibrate TOA reflectance for both close-nadir and off-nadir instruments. The improvements are discernable when compared with the official provided coefficients and that were derived using the image-based cross-calibration method. This study demonstrated not only the usefulness of Landsat-8 OLI data in sensor radiometric calibration but also the impressive accuracy of the MODIS BRDF and aerosol products in radiative transfer simulations. The proposed method can be used in the future to monitor and correct potential radiometric degradations of the GF-1 WFV instruments, and it also can be easily extended to other similar satellite missions to conduct radiometric cross-calibrations.