Surface Bidirectional Reflectance Distribution Function Research Articles

Influence of Target Surface BRDF on Non-Line-of-Sight Imaging

The surface material of an object is a key factor that affects non-line-of-sight (NLOS) imaging. In this paper, we introduce the bidirectional reflectance distribution function (BRDF) into NLOS imaging to study how the target surface material influences the quality of NLOS images. First, the BRDF of two surface materials (aluminized insulation material and white paint board) was modeled using deep neural networks and compared with a five-parameter empirical model to validate the method’s accuracy. The method was then applied to fit BRDF data for different common materials. Finally, NLOS target simulations with varying surface materials were reconstructed using the confocal diffusion tomography algorithm. The reconstructed NLOS images were classified via a convolutional neural network to assess how different surface materials impacted imaging quality. The results show that image clarity improves when decreasing the specular reflection and increasing the diffuse reflection, with the best results obtained for surfaces exhibiting a high diffuse reflection and no specular reflection.

Bidirectional Reflectance Distribution Function (BRDF) measurements, modeling and applications in space-agile Tx/Rx common aperture system

The Tx/Rx common aperture system not only needs to suppress out-of-field stray light, but also needs to suppress the influence of optical surface backscatter on detectivity. Backscatter generated by optical surfaces is often the dominant source of stray radiation that causes T/R isolation problems, and the black surfaces chosen for baffles also play a significant role in stray light suppression. Four commonly used optomechanical structure surfaces scattering BRDF data are measured, emulated and used. CFRP is selected as the baffle material, for the processing performance, cost performance and thermal control difficulty. A comparison is made between theoretical and experimental results for clean and particle contaminated ultra-smooth optical surface (CL= 800) scattering BRDF data. With the help of Mie theory and measured BRDF, the study improved the fitting accuracy of the Harvey-Shack model for the contaminated micro roughness surface. The Harvey-Shack model modeling coefficients of the particle contaminated micro-roughness surface are b = 0.12, l = 0.08, and s=-2.2. The scattering radiation introduced by particulates reduces the T/R isolation of the space-agile Tx/Rx common aperture system by 3.74 dB. Measurement results of particulates reducing the T/R isolation by 4.05 dB, verified the accuracy of particle contaminated surface BRDF modeling.

Retrieval of hourly aerosol single scattering albedo over land using geostationary satellite data

The single scattering albedo (SSA) of aerosol particles is one of the key variables that determine aerosol radiative forcing. Herein, an Algorithm for the retrieval of Single scattering albedo over Land (ASL) is proposed for application to full-disk data from the advanced Himawari imager (AHI) sensor flying on board the Himawari-8 satellite. In this algorithm, an atmospheric radiative transfer model known as the USM (the top of the atmosphere reflectance as the sum of Un-scattered, Single-scattered, and Multiple-scattered components) is used to calculate the SSA instead of predetermining the aerosol model; the USM is constrained by the surface bidirectional reflectance distribution function shape and aerosol optical depth (AOD) in the retrieval process. Combining two consecutive observations and a 2 * 2 pixel window, the optimal estimation algorithm is adopted to obtain the optimal solution for the aerosol SSA. These SSA results are evaluated by comparing with aerosol robotic network (AERONET) data. Linear regression shows that SSAASL = 0.60*SSSAERONET + 0.38, with a correlation coefficient (0.7284), mean absolute error (0.0319), mean bias error (0.00324), root mean square error (0.0427), and ~80.11% of the ASL SSA data within an uncertainty of ±0.05 of the AERONET data. A comparison of the ASL SSA products with collocated Himawari-8 SSA products (Version 03, officially released by the Japan Meteorological Agency (JMA), referred to herein as JMA SSA) shows that the accuracy of the ASL SSA is better than that of the JMA SSA products. For the SSA retrieval in large AODs (>0.4), the validation metrics vs. AERONET data are better.

Next-generation radiance unfiltering process for the Clouds and the Earth's Radiant Energy System instrument

Abstract. The filtered radiances measured by the Clouds and the Earth's Radiant Energy System (CERES) instruments are converted to shortwave (SW), longwave (LW), and window unfiltered radiances based on regressions developed from theoretical radiative transfer simulations to relate filtered and unfiltered radiances. This paper describes an update to the existing Edition 4 CERES unfiltering algorithm (Loeb et al., 2001), incorporating the most recent developments in radiative transfer modeling, ancillary input datasets, and increased computational and storage capabilities during the past 20 years. Simulations are performed with the updated Moderate Resolution Atmospheric Transmission (MODTRAN) 5.4 version. Over land and snow, the surface bidirectional reflectance distribution function (BRDF) is characterized by a kernel-based representation in the simulations, instead of the Lambertian surface used in the Edition 4 unfiltering process. Radiance unfiltering is explicitly separated into four seasonally dependent land surface groups based on the spectral radiation similarities of different surface types (defined by the International Geosphere-Biosphere Programme); over snow, it is separated into fresh snow, permanent snow, and sea ice. This differs from the Edition 4 unfiltering process where only one set of regressions was used for land and snow, respectively. The instantaneous unfiltering errors are estimated with independent test cases generated from radiative transfer simulations in which the “true” unfiltered radiances from radiative transfer simulations are compared with the unfiltered radiances calculated from the regressions. Overall, the relative errors are mostly within ±0.5 % for SW, within ±0.2 % for daytime LW, and within ±0.1 % for nighttime LW for both CERES Terra Flight Model 1 (FM1) and Aqua FM3 instruments. The unfiltered radiances are converted to fluxes and compared to CERES Edition 4 fluxes. The global mean instantaneous fluxes for Aqua FM3 are reduced by 0.34 to 0.45 W m−2 for SW and increased by 0.25 to 0.46 W m−2 for daytime LW; for Terra FM1, they are reduced by 0.24 to 0.34 W m−2 for SW and increased by 0.08 to 0.28 W m−2 for daytime LW. Nighttime LW flux differences are negligible for both instruments.

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.

Retrieving BRDFs from UAV-based radiometers for fiducial reference measurements: caveats and recommendations

Surface Bidirectional reflectance distribution function (BRDF) is a key intrinsic geophysical variable depending only on the characteristics of the observed medium. It is therefore the most suitable measurand to support the definition of fiducial reference measurements (FRM). Field acquisition of surface reflectance data relies on substantial assumptions and simplifications, often without accounting for their impact. For example, the BRDF is a theoretical concept and can never be measured in the field. In contrast, the hemispherical conical reflectance factor (HCRF), which is the measurand obtained during field campaigns, is impacted by all scene elements and is not intrinsic to the surface. This study analyses the impact of four parameters (atmospheric scattering, measurement device field of view cropping, acquisition duration, non-Lambertian reference panels) on HCRF estimation. Simulations are performed on a 3D vegetation scene, using the new radiative transfer model Eradiate. It is found that among the aforementioned parameters, atmospheric scattering alone leads to a relative root-mean-square error (RRMSE) of more than 10% between HCRF and reference Bidirectional reflectance factor (BRF).

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.

SCIATRAN software package (V4.6): update and further development of aerosol, clouds, surface reflectance databases and models

Abstract. Since the initiation of development at the Institute of Environmental Physics (IUP), University of Bremen, in 1994, the radiative transfer model SCIATRAN (formerly GOMETRAN) has been continuously improved and new versions have been released (Rozanov et al., 1997, 2002, 2005, 2014, 2017). In the course of development, the SCIATRAN software package became capable of simulating radiative transfer processes through the Earth's atmosphere or coupled atmosphere–ocean system with a variety of approaches to treat the sphericity of the atmosphere (plane-parallel, pseudo-spherical, approximately spherical and full-spherical solutions) in both scalar and vector modes. Supported by a variety of built-in databases and parameterizations, these capabilities made SCIATRAN widely used for various remote-sensing applications related to the retrieval of atmospheric trace gases and characteristics of aerosols, clouds and surfaces. This paper presents an overview of the cloud, aerosol and surface (CAS) databases and models implemented in the SCIATRAN software package (V4.6) and provides some recommendations on their usage. The new implementations offer potential users a flexible interface to perform radiative transfer simulations: (1) accounting for multilayer liquid water, ice and mixed-phase clouds; (2) employing typical aerosol-type parameterizations (including vertical variability) used in the satellite and model communities as well as updated databases; (3) including various surface bidirectional reflectance distribution function (BRDF) and albedo models for land, vegetation, ocean, snow and melt ponds on sea ice. The most recent version of the radiative transfer model SCIATRAN is freely available at the website of the IUP, University of Bremen: http://www.iup.physik.uni-bremen.de/sciatran (last access: November 2022).

A generalized photon-tracking approach to simulate spectral snow albedo and transmittance using X-ray microtomography and geometric optics

Abstract. A majority of snow radiative transfer models (RTMs) treat snow as a collection of idealized grains rather than an organized ice–air matrix. Here we present a generalized multi-layer photon-tracking RTM that simulates light reflectance and transmittance of snow based on X-ray microtomography images, treating snow as a coherent 3D structure rather than a collection of grains. The model uses a blended approach to expand ray-tracing techniques applied to sub-1 cm3 snow samples to snowpacks of arbitrary depths. While this framework has many potential applications, this study's effort is focused on simulating reflectance and transmittance in the visible and near infrared (NIR) through thin snowpacks as this is relevant for surface energy balance and remote sensing applications. We demonstrate that this framework fits well within the context of previous work and capably reproduces many known optical properties of a snow surface, including the dependence of spectral reflectance on the snow specific surface area and incident zenith angle as well as the surface bidirectional reflectance distribution function (BRDF). To evaluate the model, we compare it against reflectance data collected with a spectroradiometer at a field site in east-central Vermont. In this experiment, painted panels were inserted at various depths beneath the snow to emulate thin snow. The model compares remarkably well against the reflectance measured with a spectroradiometer, with an average RMSE of 0.03 in the 400–1600 nm range. Sensitivity simulations using this model indicate that snow transmittance is greatest in the visible wavelengths, limiting light penetration to the top 6 cm of the snowpack for fine-grain snow but increasing to 12 cm for coarse-grain snow. These results suggest that the 5 % transmission depth in snow can vary by over 6 cm according to the snow type.

Retrieving aerosols single scattering albedo from MODIS reflectances

Single scattering albedo (SSA) is a key parameter to estimate aerosol direct radiative forcing. However, retrieving SSA from satellite observations is still challenging. In this study, a new method optimizing the aerosol optical model based on four basic aerosol components is developed for retrieving SSA from Moderate Resolution Imaging Spectroradiometer (MODIS) data. The new method takes advantage of the relationship between aerosol composition and reflectance at multiple wavelengths to derive SSA, which considers the variation of aerosol species with time and location. A sensitivity study considering the uncertainties from the top of the atmosphere (TOA) apparent reflectance, aerosol optical depth (AOD) at 550 nm and land surface bidirectional reflectance distribution function (BRDF) parameters is performed to test the stability of the proposed method. The results demonstrate the high accuracy and robustness of the new method. The root mean square error (RMSE) is within 0.03 at 470 and 550 nm based on the sensitivity study. The aerosol components used in our method show better representativeness of real aerosol components than that used in the Second Simulation of a Satellite Signal in the Solar Spectrum - Vector (6SV) model. The aerosols SSA values are retrieved from real MODIS observations with the new method and compared with the AERONET SSA product at two polluted AERONET sites in Asia. A better consistency with the correlation coefficient (R) of 0.27– 0.55 and the RMSE within 0.04 at 470 and 550 nm wavelengths between MODIS SSA retrievals and the AERONET SSA product is obtained with the new method compared with that of deep blue (DB) SSA. The new method can help to better understand aerosol radiative forcing regionally and globally.

BRDF LABORATORY MEASUREMENTS USING A CAMERA-AIDED SPECTRORADIOMETER

Abstract. Numerous natural surfaces observed using Remote Sensing do not reflect light as Lambertian surfaces. Instead, their reflection is highly dependent on two main directions: the direction of the light source and the observation viewing angle, which characterize the Bidirectional Reflectance Distribution Function (BRDF). The BRDF is one of the challenging main effects of remote sensing. Thus, studying the BRDF of various land cover surfaces is essential, and researchers invest many efforts to fulfill this objective. However, measuring the BRDF is tricky and requires unique instruments, e.g., the Gonioreflectometer. Unfortunately, the availability of such instruments is deficient, and they are costly and hard to maintain. Considering these limitations, we present a study and a new approach for measuring the BRDF of surfaces with a camera-aided spectroradiometer that simultaneously acquires an RGB image from the sensor location beside the spectral measurement. Then, we feed the Structure From Motion (SFM) process with the RGM images to retrieve the sensor locations. Next, we convert the sensor locations into the quantities needed for the BRDF measurement, i.e., zenith angles and distances relative to the measured sample. Finally, we apply a set of measurements under controlled conditions in a dark room designed for hyperspectral remote sensing studies to evaluate the proposed methodology. In particular, we experimented with three different material surfaces. The results clearly show the highly accurate sensor position derived by SFM, providing zenith angles and distance from the scene’s center with mean errors around one degree and 2.5 centimeters, respectively. In addition, the obtained spectra tell that the proposed approach is suitable for multiangular measurements of reflected light and studying the BRDF.

A Novel Atmospheric Correction Algorithm to Exploit the Diurnal Variability in Hypertemporal Geostationary Observations

This study developed a new atmospheric correction algorithm, GeoNEX-AC, that is independent from the traditional use of spectral band ratios but dedicated to exploiting information from the diurnal variability in the hypertemporal geostationary observations. The algorithm starts by evaluating smooth segments of the diurnal time series of the top-of-atmosphere (TOA) reflectance to identify clear-sky and snow-free observations. It then attempts to retrieve the Ross-Thick–Li-Sparse (RTLS) surface bi-directional reflectance distribution function (BRDF) parameters and the daily mean atmospheric optical depth (AOD) with an atmospheric radiative transfer model (RTM) to optimally simulate the observed diurnal variability in the clear-sky TOA reflectance. Once the initial RTLS parameters are retrieved after the algorithm’s burn-in period, they serve as the prior information to estimate the AOD levels for the following days and update the surface BRDF information with the new clear-sky observations. This process is iterated through the full time span of the observations, skipping only totally cloudy days or when surface snow is detected. We tested the algorithm over various Aerosol Robotic Network (AERONET) sites and the retrieved results well agree with the ground-based measurements. This study demonstrates that the high-frequency diurnal geostationary observations contain unique information that can help to address the atmospheric correction problem from new directions.

Estimating Surface BRDF/Albedo Over Rugged Terrain Using an Extended Multisensor Combined BRDF Inversion (EMCBI) Model

Land surface albedo is a crucial variable of earth energy budget and global climate change. Rugged terrain significantly impacts surface bidirectional reflectance distribution function (BRDF) and the subsequent albedo retrieval using satellite remote sensing. Existing studies of estimating surface BRDF/albedo from satellite observations are limited to neglecting topographic impacts, resulting in large uncertainty in satellite albedo product, especially for low spatial resolution satellite sensors that are primarily regulated by subpixel-scale topographic effects. To fill this knowledge gap, we proposed an extended multisensor combined BRDF inversion (EMCBI) model to characterize subpixel-scale topographic effects, and applied this model to estimate BRDF/albedo from the Himawari-8 Advanced Himawari Imager (AHI) and Terra/Aqua moderate resolution imaging spectroradiometer (MODIS) data and finally validated the satellite-derived albedo with ground measurements of two stations located in Tibet plateau. Our results show that: 1) EMCBI can generate a daily BRDF/albedo dataset with more than 90% spatial coverage and 2) EMCBI-derived albedo agrees well with the referenced albedo corrected from ground measurement, with a root-mean-square-error (RMSE) of 0.0537 and 0.0608 for black-sky albedo (BSA) and white-sky albedo (WSA), and a mean absolute percentage error (MAPE) of 21.93% and 25.13% for BSA and WSA, respectively. These results demonstrate EMCBI has great potential for mapping large-scale high temporal resolution BRDF/albedo product over rugged terrain.

Land Surface Albedo Estimation With Chinese GF-1 WFV Data in Northwest China

Land surface albedo (LSA) is one of the driving factors in the energy balance of surface radiation and the interaction between the earth and atmosphere. LSA is an important parameter that is widely used in surface energy balance, medium- and long-term weather forecasting, and global change studies. GF-1 wide field view (WFV) data provide a spatial resolution of 16 m and temporally intensive land surface observations, but efficient algorithm was still lacking for quantitatively land surface parameters estimation. It is essential to improve the data use ability by generating efficient land surface parameter retrieval algorithms. This study proposed an LSA retrieval algorithm by using GF-1 WFV data. Land surface bidirectional reflectance distribution function characteristic parameters were used to represent the non-Lambertian characteristic of land surface. The top of atmosphere (TOA) reflectance is simulated by the 6S radiative transfer model by considering non-Lambertian land surfaces. Linear regression is applied in the TOA reflectance, and LSA is simulated with the surface bidirectional reflectance characteristic parameters to build a lookup table. The proposed algorithm can estimate LSA with high accuracy according to the TOA reflectance without the complex multistep inversion process. The validation results of ground measurements in Northwest China for different land cover types show that the algorithm is effective, and the overall root mean square error was 0.036 when compared with field observation. The algorithm also shows great consistency with Landsat albedo data. The proposed algorithm is of great significance for improving GF data utilization.

One-shot BRDF imaging system to obtain surface properties

The bidirectional reflectance distribution function (BRDF) that describes an angle-resolved distribution of surface reflectance is available for characterizing surface properties of a material. A one-shot BRDF imaging system can capture an in-plane color mapping of light direction extracted from a surface BRDF distribution. A surface roughness identification method is then proposed here using the imaging system. A difference between surface properties of a matt paper and a glossy paper is experimentally shown to be detected using the method. A surface reconstruction method of an axisymmetric micro-object using the imaging system is also proposed here. The imaging system experimentally shows that it can reconstruct an axisymmetric aluminium cone surface with a height of 37 μm.

Comparison of different methods of determining land surface reflectance for AOD retrieval

Determining land surface reflectance (LSR) is important for satellite retrieval of aerosol optical depth (AOD). Many studies have focused on the development of AOD retrieval through using different land surface reflectance determination algorithms. However, there is still lack of a comprehensive comparison among those algorithms. In this study, AOD retrieval algorithms with different land surface reflectance assumptions were compared and analyzed using Advanced Himawari Imager (AHI) data. The results demonstrate that the AOD retrieved by the algorithm considering the land surface bidirectional reflectance distribution function (BRDF) effects (Method 3) has a higher accuracy than that based on the dark target (Method 1) and second lowest reflectance (Method 2) methods. The overall correlation coefficient between the AOD retrievals considering land surface BRDF and AERONET AOD measurements is 0.93 and the Root Mean Square Error (RMSE) is 0.13. In addition, there are 68.91 % of AOD matchups falling within the expected error (±0.05 ± 0.2 × AOD AERONET). Method 3 shows a more robust performance than the other two methods for different seasons, normalized difference vegetation index (NDVI) values and scattering angles. It also shows the advantages of AOD retrieval in urban regions with relatively high LSR. The AOD retrievals based on Method 2 show the best agreement with MODIS 1 km AOD product, while Method 1 AODs are much closer to the MODIS 10 km AODs. The differences of annual average values of AHI AOD retrievals using different algorithms can be high to 0.13 at 11:00 (Beijing Time) over the Beijing-Tianjin-Hebei (BTH) region, which could cause a 23.6Wm-2 discrepancy when calculating the direct aerosol radiative effect (DARE) at the bottom of the atmosphere.

Quantifying uncertainties in nighttime light retrievals from Suomi-NPP and NOAA-20 VIIRS Day/Night Band data

Satellite observations of nighttime lights (NTL) from Suomi-NPP and NOAA-20 VIIRS Day/Night Band data have been widely used to estimate human activities. Long-term changes such as urban development and abrupt short-term changes such as power outages have been monitored from temporal NTL acquired by satellites. While high temporal NTL variation has been found across NTL data of varying temporal scale (e.g., daily, monthly, and annual composites), the sources of measurement error and uncertainty are poorly understood. This paper quantifies the sources of VIIRS-derived NTL uncertainty due to view-illumination geometry, surface Bidirectional Reflectance Distribution Function (BRDF)/albedo, and the effects of snow cover, lunar irradiance, aerosol loading, cloud mask, vegetation, geometry, and ephemeral artifacts (e.g., the Aurora Borealis). Based on this current assessment of NASA Black Marble retrievals (VNP46, Collection V001), we found that angular and atmospheric effects dominate retrieval uncertainty. Errors introduced by upstream data inputs (e.g., a coarser nighttime snow cover flag and misclassification errors in the existing VIIRS nighttime cloud mask) were also found to impact retrieval quality. Despite these challenges, a consistent daily NTL time series record can be routinely generated from top-of-atmosphere VNP46 radiances. Key recommendations include: (1) the use of lunar-BRDF adjusted and atmospherically corrected NTL (i.e., as identified as high-quality retrievals in the VNP46 QA fields), (2) development and improvement to the VIIRS snow cover and cloud masks algorithms to accurately reflect NTL retrieval conditions, (3) characterizing seasonal variations in NTL due to vegetation and snow, (4) reducing geometric effects due to the spatial mismatch of gridded pixel and observation footprint, (5) employing angularly-consistent NTL observations from multiple VIIRS instruments (i.e., Suomi-NPP and NOAA-20) to reduce pixel-based uncertainties and address persistent data gaps, and (6) being mindful of surface-reflected radiance from aurora events at mid-to-high latitudes.

Directionally dependent Lambertian-equivalent reflectivity (DLER) of the Earth's surface measured by the GOME-2 satellite instruments

Abstract. In this paper we introduce the new concept of directionally dependent Lambertian-equivalent reflectivity (DLER) of the Earth's surface retrieved from satellite observations. This surface DLER describes Lambertian (isotropic) surface reflection which is extended with a dependence on the satellite viewing geometry. We apply this concept to data of the GOME-2 satellite instruments to create a global database of the reflectivity of the Earth's surface, providing surface DLER for 26 wavelength bands between 328 and 772 nm as a function of the satellite viewing angle via a second-degree polynomial parameterisation. The resolution of the database grid is 0.25∘ by 0.25∘, but the real, intrinsic spatial resolution varies over the grid from 1.0∘ by 1.0∘ to 0.5∘ by 0.5∘ down to 0.25∘ by 0.25∘ by applying dynamic gridding techniques. The database is based on more than 10 years (2007–2018) of GOME-2 data from the MetOp-A and MetOp-B satellites. The relation between DLER and bi-directional reflectance distribution function (BRDF) surface reflectance is studied using radiative transfer simulations. For the shorter wavelengths (λ<500 nm), there are significant differences between the two. For instance, at 463 nm the difference can go up to 6 % at 30∘ solar zenith angle. The study also shows that, although DLER and BRDF surface reflectances have different properties, they are comparable for the longer wavelengths (λ>500 nm). Based on this outcome, the GOME-2 surface DLER is compared with MODIS surface BRDF data from MODIS band 1 (centred around 645 nm) using both case studies and global comparisons. The conclusion of this validation is that the GOME-2 DLER compares well to MODIS BRDF data and that it does so much better than the non-directional LER database. The DLER approach for describing surface reflectivity is therefore an important improvement over the standard isotropic (non-directional) LER approaches used in the past. The GOME-2 surface DLER database can be used for the retrieval of atmospheric properties from GOME-2 and from previous satellite instruments like GOME and SCIAMACHY. It will also be used to support retrievals from the future Sentinel-5 UVNS (ultraviolet, visible, near-infrared, and short-wave infrared) satellite instrument.

Vicarious Calibration of FengYun-3D MERSI-II at Railroad Valley Playa Site: A Case for Sensors with Large View Angles

Vicarious calibration, as one on-orbit calibration method, is a supplement to onboard calibration of sensors. The application of vicarious calibration, however, is greatly limited due to the time- and effort-consuming field measurements of atmosphere and surface. Fortunately, the Radiometric Calibration Network (RadCalNet) provides automated in situ data at multiple sites, thus increasing the opportunities to achieve ongoing ground-reference calibration of in-orbit instruments. The MEdium Resolution Spectrum Imager-II (MERSI-II) onboard FengYun-3D (FY) has the temporal, spectral, spatial, and radiometric capacity for image capture at a level on par with other sensors used worldwide, such as the Moderate Resolution Imaging Spectroradiometer (MODIS). Its on-orbit radiometric performance, however, is assessed in a limited manner. In this study, the reflectance-based vicarious calibration method was employed to calibrate the MERSI-II sensor using ground measurements from RadCalNet at the Railroad Valley Playa site. The calibration of the MERSI-II sensor with large view angles presents difficulties due to the uncertainties introduced by surface bidirectional reflectance distribution function (BRDF) effects. Thus, we performed BRDF correction to harmonize the sensor and ground measurements to consistent observation geometries, before the in-situ measurements were taken as inputs for the 6SV radiative transfer model to predict at-sensor radiance. The calibration results were comprehensively validated with ground data and MODIS benchmark datasets. The results show that accounting for BRDF correction could improve the accuracy of vicarious calibration and ensure inter-consistency between different sensors. An analysis of the vicarious calibration of FY-3D MERSI-II yielded uncertainties of <5% for solar reflective bands, which meets the radiometric accuracy requirements typical for land-monitoring space missions. The proposed approach is also applicable to the calibration of other large footprint sensors.

A Dual-Channel Aerosol Optical Depth Retrieval Algorithm Incorporating the BRDF Effect from AVHRR over Eastern Asia

A NOAA/AVHRR dual-channel method over land is proposed to simultaneously retrieve aerosol optical depth (AOD) at 0.55 μm, and surface reflectance at 0.63 and 0.85 μm. Compared with previous well-established one-channel retrieval algorithms, this algorithm takes advantage of the surface reflectance ratio between 0.63 and 0.85 μm in an attempt to account for the effect induced by the surface bidirectional reflectance distribution function (BRDF). This effect cannot be negligible due to the orbit drift and phasing running of NOAA satellites, both of which potentially cause large solar angular variation. Meanwhile, the observation posture change of AVHRR would cause large sensor angular variation in time series measurements. The used surface reflectance ratio based on dual channels at 0.63 and 0.85 μm is found more reasonable to be assumed as unchanged during a certain period of time, compared to the traditional ratio when addressing the BRDF issue. AOD retrievals have been carried out over Eastern Asia. Validation against aerosol robotic network (AERONET) measurements shows that up to 83% of AOD validation collocations are within error lines (±0.15 ± 0.15τ, τ is AOD) with an R of 0.88 and an root mean square error (RMSE) of 0.15. The dual-channel algorithm taking into account the surface BRDF effect is proved outperforming the conventional 0.63 μm-channel method. It indicates that our algorithm has the potential to be applied to the retrieval of long series of AOD, especially to the AOD retrieval of the sensors which lack a shortwave infrared channel required in the MODerate resolution Imaging Spectroradiometer (MODIS) dark target AOD algorithm.