Assessing the radiometric consistency of Gaofen-6 WFV and Sentinel-2 MSI multispectral reflectance for harmonization

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.

Abstract. We investigated the effect of surface reflectance anisotropy, bidirectional reflectance distribution function (BRDF), on satellite retrievals of tropospheric NO2. We assume the geometry of geostationary measurements over Tokyo, which is one of the worst air-polluted regions in East Asia. We calculated air mass factors (AMF) and box AMFs (BAMF) for tropospheric NO2 to evaluate the effect of BRDF by using the radiative transfer model SCIATRAN. To model the BRDF effect, we utilized the Moderate Resolution Imaging Spectroradiometer (MODIS) products (MOD43B1 and MOD43B2), which provide three coefficients to express the RossThick–LiSparse reciprocal model, a semi-empirical and kernel-based model of BRDF. Because BRDF depends on the land cover type, we also utilized the High Resolution Land-Use and Land-Cover Map of the Advanced Land Observing Satellite (ALOS)/Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2), which classifies the ground pixels over Tokyo into six main types: water, urban, paddy, crop, deciduous forest, and evergreen forest. We first develop an empirical model of the three BRDF coefficients for each land cover type over Tokyo and then apply the model to the calculation of land-cover-type-dependent AMFs and BAMFs. Results show that the variability of AMF among the land types is up to several tens of percent, and if we neglect the reflectance anisotropy, the difference with AMFs based on BRDF reaches 10% or more. The evaluation of the BAMFs calculated shows that not considering BRDF will cause large errors if the concentration of NO2 is high close to the surface, although the importance of BRDF for AMFs decreases for large aerosol optical depth (AOD).

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.

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

Ground reference data are important for understanding and characterizing angular effects on the images acquired by satellite sensors with off-nadir capability. However, very few studies have considered image-based soil reference data for that purpose. Compared to non-imaging instruments, imaging spectrometers can provide detailed information to investigate the influence of spatial components on the bidirectional reflectance distribution function (BRDF) of a mixed target. This research reported in this paper investigated soil spectral reflectance changes as a function of surface roughness, scene components and viewing geometries, as well as wavelength. Soil spectral reflectance is of particular interest because it is an essential factor in interpreting the angular effects on images of vegetation canopies. BRDF data of both rough and smooth soil surfaces were acquired in the laboratory at 30° illumination angle using a Specim V10E imaging spectrometer mounted on the University of Lethbridge Goniometer System version 2.5 (ULGS-2.5). The BRDF results showed that the BRDF of the smooth soil surface was dominated by illuminated pixels, whereas the shaded pixels were a larger component of the BRDF of the rough surface. In the blue, green, red, and near-infrared (NIR), greater BRDF variation was observed for the rough than for the smooth soil surface. For both soil surface roughness categories, the BRDF exhibited a greater range of values in the NIR than in the blue, green, or red. The imaging approach allows the characterization of the impact of spatial components on soil BRDF and leads to an improved understanding of soil reflectance compared to non-imaging BRDF approaches. The imaging spectrometer is an important sensor for BRDF investigations where the effects of individual spatial components need to be identified.

The characterization and temporal analysis of multispectral and hyperspectral data to extract the biophysical information of the Earth's surface can be significantly improved by understanding its aniosotropic reflectance properties, which are best described by a Bi-directional Reflectance Distribution Function (BRDF). The advancements in the field of remote sensing techniques and instrumentation have made hyperspectral BRDF measurements in the field possible using sophisticated goniometers. However, natural surfaces such as forest canopies impose limitations on both the data collection techniques, as well as, the range of illumination angles that can be collected from the field. These limitations can be mitigated by measuring BRDF in a virtual environment. This paper presents an approach to model the spectral BRDF of a forest canopy using the Digital Image and Remote Sensing Image Generation (DIRSIG) model. A synthetic forest canopy scene is constructed by modeling the 3D geometries of different tree species using OnyxTree software. The field collected spectra from the Harvard forest is used to represent the optical properties of the tree elements. The canopy radiative transfer is estimated using the DIRSIG model for specific view and illumination angles to generate BRDF measurements. A full hemispherical BRDF is generated by fitting the measured BRDF to a semi-empirical BRDF model. The results from fitting the model to the measurement indicates a root mean square error of less than 5% (2 reflectance units) relative to the forest's reflectance in the VIS-NIR-SWIR region. The process can be easily extended to generate a spectral BRDF library for various biomes.

Information about the surface bi-directional reflectance distribution function (BRDF) and albedo is required as a boundary condition for radiative transfer modeling, aerosol retrievals, cloud retrievals, and atmospheric modeling. The typical spatial resolution provided by MODIS and MISR standard surface products (~1km) is insufficient to measure the BRDF of the pure surface types, because most pixels at this scale correspond to mixed classes. We present an approach for the retrieval of the basic surface BRDFs from the observations of MODIS/Terra and MISR using an angular unmixing method. Our analysis is focused on the Atmospheric Radiation Measurement (ARM) Program area in the Southern Great Planes (SGP) region, which is a predominantly agricultural area with a few major crop types. Pure surface classes were identified using high-resolution (30m) Landsat imagery and results of a ground survey. Assuming that the reflectance for each coarse pixel is a linear superposition of reflectances of basic surface types, it is possible to estimate the original BRDF parameters for each landcover type. In our case, three dominant classes were selected: wheat, grass, and baresoil. In the case of wheat and grass, the dispersion of the results is smaller than in the case of soil. This can be explained by the relatively low fractional coverage of the soil class within large pixels and by the significant variability of soil reflectance depending on wetness, soil type (sand, clay, etc.), and other factors. The correlation between the BRDF shape factors and the normalized difference vegetation index (NDVI) has also been analyzed. There is a high degree of correlation between the NDVI and BRDF isotropic factor (r0 in the case of MISR), while the correlation with other BRDF parameters was found to be smaller. In general, the NDVI can be used as a crude proxy for the BRDF shape.

The issue of bidirectional reflectance distribution function (BRDF) spatial variability on the landscape and its linkages to surface characteristics has received little attention. Data sets such as those collected by the Advanced Solid-State Array Spectroradiometer (ASAS), an airborne imaging spectroradiometer with along-track pointability, are ideal for such investigations. ASAS data collected during the Hydrologic and Atmospheric Pilot Experiment (HAPEX-Sahel) near Niamey, Niger were analyzed to address spatial variability with land cover type, the effects of scale on the directional reflectance trends, and how well a simple BRDF model accounted for the principal plane reflectance trends. A hypothesis was also tested that critical elements of the BRDF of the land cover types, as captured by the terms of a simple model, could be scaled up to the BRDF of the full scene using a linear mixture model.

Bidirectional reflectance distribution function (BRDF) offers complete description of the visual appearance properties of materials including the spectral and spatial characteristics. Numerous studies on BRDF have been performed for its important role in computer graphics, remote sensing, object characteristics analysis, and other fields. A few of these studies focus on the spectral polarized BRDF, which contains more optical information of materials than the unpolarized BRDF and the polarized BRDF at single wavelength. In this paper, the spectral polarized BRDF measurement of brass is studied based on a self-designed device in visible light band. The influences of wavelength, angle, polarization of incident, and reflected light on the BRDF are discussed in detail. The results indicate that the effect of wavelength on BRDF depends on the color of the sample. The polarization of incident light has little influence on the variation tendency of BRDF versus incident zenith angle and wavelength. In addition, the exponential model of BRDF is built according to material surface characteristics. The fitting results show that the calculated values are in good agreement with the measured values at different incident zenith angles. This work may be beneficial to the study of optical properties and process design of brass.

Satellite sensors, such as the AVHRR, SPOT and soon to be launched MODIS, MISR, VEGETATION and GLI acquire bidirectional reflectance data under different solar illumination angles. These systems will capture the strong anisotropic properties that vary with relative amounts and types of vegetation and soil within each pixel. Therefore, some knowledge of the bidirectional reflectance distribution function (BRDF) is a requirement for successful interpretation of directional reflectance data and vegetation indices, and derivation of land-cover-specific biophysical parameters. The objectives of this research were: (a) to parameterize empirical and semi-empirical BRDF models for different land cover types and MODIS spectral bands, (b) utilize the BRDF models to correct off-nadir measurements to nadir-equivalent values for vegetation index (VI) compositing and biophysical interpretation and (c) compare different vegetation index compositing scenarios. High spectral and spatial resolution bidirectional reflectance factor (BRF) measurements from the ASAS flown on the NASA C-130B aircraft were used for the analysis. Leaf area index (LAI) measurements were made concurrently at most of the study sites which included deciduous and coniferous forest, grassland and shrub savanna land covers. The normalized difference vegetation index (NDVI) and modified VI (MVI) were selected as classifiers in five different vegetation index composite scenarios: a maximum VI based on apparent reflectance data, a maximum VI based on at-surface reflectance data, a BRDF standardized VI, based on at-surface reflectances at nadir view angle, a BRDF normalized VI, based on at-surface reflectances at nadir view and nadir sun angles, a normalized bidirectional VI distribution function (BVIF). Nadir-equivalent VI accuracy and predictability were evaluated for all compositing scenarios using the measured nadir observations as a reference. The results of the analysis emphasize the importance of standardizing BRF for vegetation index compositing schemes and retrieval of biophysical parameters.

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.

The radiation reflected by any observed surface is highly dependent on both sun illumination and satellite observation angles. These two angles are also described, respectively, as incident and reflected angles. The geometry-dependence of surface reflectance is usually corrected by a tailored Bidirectional Reflectance Distribution Function (BRDF). It is the most common tool used to eliminate or to reduce the effects of sun-sensor geometry on the reflected radiation. Generally, BRDFs are derived empirically (or semi-empirically) for a specific land cover by analyzing a large set of observations (training set) made under different illumination and observation angles. This approach involves fitting the model to collected observations and inverting it. A strong BRDF model tailored to specific land cover characteristics of the UAE is especially needed for applications that use data acquired with variable sun-sensor geometry. In this paper, a neural-network-based tool "BRDF" was developed and applied to quantify the effect of sun illumination and SEVIRI-MSG observation angles on measured reflectance for both land (mostly desert) and coastal water pixels in the UAE.

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

The Bidirectional Reflectance Distribution Function (BRDF) of earth surface materials contains information relating to their physical structure and composition that cannot be inferred from their spectral properties alone. Knowledge of the BRDF is also critical to the accurate retrieval of earth surface albedo, since the BRDF describes the angular distribution of reflected radiation under given illumination conditions. Although the BRDF cannot be measured directly, it can be estimated using models of surface scattering in conjunction with reflectance data acquired at different viewing and illumination angles. The ability of a satellite sensor to characterise the BRDF of any point on the earth's surface is therefore dependent on (i) the range of view angles over which it is able to acquire data, (ii) the orbital characteristics of the satellite on which it is mounted, and (iii) the time period over which the data are recorded. This paper explores the BRDF sampling capabilities of several satellite sensors currently in operation (Landsat TM, SPOT HRV, NOAA AVHRR and ERS‐1 ATSR) or proposed for launch in the near future (MISR and MODIS). Sensors that are capable of off‐nadir viewing solely by virtue of having a wide field‐of‐view (e.g. NOAA‐AVHRR) or through across‐track pointing (e.g. SPOT‐HRV) provide a relatively sparse sample of the BRDF. On the other hand, future sensors with along‐track pointing, such as the MISR instrument of NASA's Earth Observing System (EOS), will provide a much more complete sample and are therefore better able to characterise the surface BRDF and albedo. Sensors such as these are also better equipped to obtain data at and around the ‘hot spot’ and, consequently, have the potential to extract detailed information on the biophysical properties of earth surface materials.

Abstract. One of the most challenging effects of remote sensing is landcover materials' Bidirectional Reflectance Distribution Function (BRDF). A wide range of approaches and measuring methods address the BRDF in various studies. However, there is a requirement for an accurate measurement setup and costly special equipment. Furthermore, the measurements and calculations are applied to model the BRDF for a single point on the object's surface. Considering these limitations, we propose a new modular framework and methodology for measuring, modeling, and analyzing the BRDF without the need for unique instruments. Instead, we suggest acquiring multiple overlapping images in a simple and time-saving way, sampling the desired object's Region Of Interest (ROI) in one image and automatically tracking it in the other images. Experimental results using laboratory data acquired under controlled conditions clearly show the advantages of our framework in retrieving the camera positions, tracking ROIs in the different images, and accurately measuring the BRDF of various land-cover types. Moreover, we observed the variability of the obtained measurements before and after applying the kernel-driven approach to minimize the BRDF effect. The results show that the applied correction reduces this variability significantly, indicating the high accuracy of measuring the directional reflectance using the proposed approach.