Bidirectional Reflectance Distribution Function Parameters Research Articles

A Practical Approach to Improve the MODIS MCD43A Products in Snow-Covered Areas

The MODerate Resolution Imaging Spectroradiometer (MODIS) MCD43A products have been extensively applied in the remote sensing field, but recent researchers have demonstrated that these products still had the potential to be further improved by using the latest development of the kernel-driven model [RossThick-LiSparseReciprocal-Snow (RTLSRS)] in snow-covered areas, since the MCD43A product algorithm [RossThick-LiSparseReciprocal (RTLSR)] needed to be improved for the accurate simulation of snow bidirectional reflectance distribution function (BRDF) signatures. In this paper, we proposed a practical approach to improve the MCD43A products, which used the Polarization and Directionality of the Earth's Reflectance (POLDER) observations and random forest algorithm to establish the relationship between the BRDF parameters (MCD43A1) estimated by the RTLSR and RTLSRS models. We applied this relationship to correct the MCD43A1 product and retrieve the corresponding albedo (MCD43A3) and nadir reflectance (MCD43A4). The results obtained highlight several aspects: (a) The proposed approach can perform well in correcting BRDF parameters [root mean square error (RMSE) = ~0.04]. (b) The corrected BRDF parameters were then used to retrieve snow albedo, which matched up quite well with the results of the RTLSRS model. (c) Finally, the snow albedo retrieved by the proposed approach was assessed using ground-based albedo observations. Results indicated that the retrieved snow albedo showed a higher accuracy as compared to the station measurements (RMSE = 0.055, bias = 0.005), which was better than the results of the MODIS albedo product (RMSE = 0.064, bias = −0.018), especially at large angles. These results demonstrated that this proposed approach presented the potential to further improve the MCD43A products in snow-covered areas.

CANOPY STRUCTURAL EFFECTS ON BIDIRECTIONAL REFLECTANCE SIMULATED BY THE LESS MODEL: A CASE STUDY OF PICEA CRASSIFOLIA FORESTS

Abstract. Surface albedo is a dominant factor affecting the earth energy balance. Bidirectional reflectance distribution function (BRDF) describes the anisotropic surface reflection of solar radiation. Bidirectional effect is especially noticeable in the forest canopies with three-dimensional canopy structure. Thus, it is challenging to evaluate the canopy structural effects on BRDF using traditional radiative transfer modelling methods, where leaf in canopies is generally assumed to be homogeneous. LESS, a ray-tracing-based three-dimensional (3D) radiative transfer model can effectively simulate bidirectional reflectance factor while fully considering multi-component spectral and structural characteristics of vegetation. In this study, we applied the LESS model to evaluate the impacts of changes in leaf area index (LAI) on canopy reflectance in a Picea crassifolia forest. First, canopy structural parameters obtained from forest inventory were used to construct forest scenes and drive the LESS model. Then, we validated the simulation accuracy of the LESS model against BRDF calculated from MODIS BRDF parameter product using kernel-driven Ross-Li bi-directional reflectance function. Finally, we constructed forest scenes with different LAI to investigate the canopy structural effects on BRDF. We found that the BRDF simulated by the LESS model is in good accordance with the MODIS BRDF data in the red (R2 = 0.97, RMSE = 0.03) and NIR (R2 = 0.94, RMSE = 0.05) bands. The simulated canopy reflectance in the red band displayed ‘dome’ shape and decreased with increasing LAI. In contrast, canopy reflectance in the NIR band displayed ‘bowl’ shape and increased with increasing LAI. Our study highlighted the sensitivity of canopy BRDF to changes in canopy structure and have implications for retrieving LAI from BRDF data.

Geostationary operational environmental satellite-R advanced baseline imager reflective solar band absolute validation using Sonoran desert scenes

The advanced baseline imagers (ABIs) on board the geostationary operational environmental satellite (GOES)-R series, GOES-16 and GOES-17 satellites, have accumulated long data series that have been updated throughout their missions to enhance their utility for imaging the Earth’s weather, oceans, and environment. In preparing for a reprocessing effort aimed at improving the consistency and image quality and reducing radiometric uncertainties necessary for some Earth science applications, we developed a vicarious validation technique for five of the six reflective solar bands. This technique uses Sonoran desert surface reflectance data derived from the moderate resolution imaging spectrometer bidirectional reflectance distribution function parameters product along with other atmospheric products to generate a predicted at-sensor radiance for both GOES-16 and GOES-17 for comparison with ABI observations and to enable ABI intercomparisons. This technique identified known instrument biases and demonstrated absolute validation over the course of a year, showing its capability to detect changes, monitor stability, and assess radiometric uncertainties for operational data applications and future reprocessed data sets.

Estimating Physically-Based Reflectance Parameters From a Single Image With GAN-Guided CNN

We present a method that estimates the physically accurate reflectance of materials from a single image and reproduces real world materials which can be used in well-known graphics engines and tools. Recovering the BRDF (bidirectional reflectance distribution function) from a single image is an ill-posed problem due to the insufficient irradiance and geometry information as well as the insufficient samples on the BRDF parameters. The problem could be alleviated with a simplified representation of the surface reflectance such as Phong reflection model. Recent works have appealed that convolutional neural network successfully predicts parameters of empirical BRDF models for non-Lambertian surfaces. However, parameters of the physically-based model confront the problem of having non-orthogonal space, making it difficult to estimate physically meaningful results. In this paper, we propose a method to estimate parameters of a physically-based BRDF model from a single image. We focus on the metallic property of the physically-based model to enhance the estimation accuracy. Since metals and nonmetals have very different characteristics, our method processes them separately. Our method also generates auxiliary maps using a cGAN (conditional generative adversarial network) architecture to help in estimating more accurate BRDF parameters. Based on the experimental results, the auxiliary map is selected as an irradiance environment map for the metallic and a specular map for the nonmetallic. These auxiliary maps help to clarify the contributions of different actors, including light color, material color, specular component, and diffuse component, to the surface color. Our method first estimates whether the material on the input image is metallic or nonmetallic. Then, it estimates BRDF parameters using CNN (convolutional neural networks) architecture guided by generated auxiliary maps. Our results show that our method is effective to estimate BRDF parameters both on synthesized as well as real images.

Numerical approaches and analysis of optical measurements of laser radar cross-sections affected by aero-optical transmission

One of the concerns in the process of measuring laser radar cross-section (LRCS) using optical measurement methods is the aero-optical effect around a high-speed flow field. In this paper, a computational model for LRCS considering supersonic flow in a non-homogeneous medium is presented. First, by taking a typical 15° half-cone blunt cone as the research object, based on CFD calculation, the flow field distribution of the non-uniform medium around the high-speed vehicle is obtained. The ray-tracing method is used to calculate the light transmission in an aero-optical environment. An LRCS approach for arbitrarily complex targets in the aero-optical environment is then proposed, and the method is verified by analytical and numerical methods. Finally, the effects of incoming density, incoming Mach number, and material bidirectional reflectance distribution function (BRDF) parameters on LRCS measurements are investigated, and the errors caused by the steady aero-optical environment are analyzed. The results show that the maximum relative error of the measurement caused by the aero-optical environment in the direction of the shock-wave layer observation is significant when the Fresnel function approximation exponential function parameter is too large or when the root mean square (RMS) of the surface slope is too small in the BRDF parameters of the material. The influence of the aero-optical environmental flow field on the LRCS is characterized by a directional and regional distribution, determined by the incoming Mach number and shock-wave angle, with the influence of the incident flow density on the regional distribution being weakly correlated. The proposed method and calculation results are useful for the simulation, analysis, and calibration of LRCS measurements in complex aero-optical environments.

Investigation of light scattering properties based on the modified Li-Liang BRDF model

Light scattering property is an important indicator for the laser remote sensing applications. Targets surface features can be extracted from the intensity data through the light scattering property and the exploitation of laser intensity for a more effective target characterization has become a topic of interest. However, there isn’t an universal scattering model i. e. bidirectional reflectance distribution function (BRDF) to describe the multiple targets’ scattering properties and the surface features cannot be represented by the same types of parameters of a particular BRDF. In this paper, a modified Li-Liang BRDF model is proposed by adding the directional diffuse reflection to the Li-Liang BRDF model, to our best knowledge, it is the most suitable for describing a series of materials’ scattering properties with dual-wavelength (532 nm and 1064 nm) than other BRDF models. Based on which, a theoretical evaluation method of Gm-APD detection performance is proposed which can be used to find the optimal LiDAR system parameters. Through evaluation, to effectively detect 3 km building target, the transmitting energy is at least 10.29 μJ for 1064 nm laser, it is at least 80.7 μJ for 532 nm laser. The incident angle is a significant factor affecting the detection probability, and 1064 nm wavelength is a better choice for single-wavelength detection; however, the intensity of 532 nm transmitted laser should be relatively higher for the dual-wavelength detection. This paper offered a typical method for choosing LiDAR system parameter based on the accurate scattering properties.

Vegetation Structure Index (VSI): Retrieving Vegetation Structural Information from Multi-Angular Satellite Remote Sensing.

Utilization of the Bidirectional Reflectance Distribution Function (BRDF) model parameters obtained from the multi-angular remote sensing is one of the approaches for the retrieval of vegetation structural information. In this research, the potential of multi-angular vegetation indices, formulated by the combination of multi-spectral reflectance from different view angles, for the retrieval of forest above-ground biomass was assessed in the New England region. The multi-angular vegetation indices were generated by the simulation of the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo Model Parameters Product (MCD43A1 Version 6)-based BRDF parameters. The effects of the seasonal (spring, summer, autumn, and winter) composites of the multi-angular vegetation indices on the above-ground biomass, the angular relationship of the spectral reflectance with above-ground biomass, and the interrelationships between the multi-angular vegetation indices were analyzed. Among the existing multi-angular vegetation indices, only the Nadir BRDF-adjusted NDVI and Hot-spot incorporated NDVI showed significant relationship (more than 50%) with the above-ground biomass. The Vegetation Structure Index (VSI), newly proposed in the research, performed in the most efficient way and explained 64% variation of the above-ground biomass, suggesting that the right choice of the spectral channel and observation geometry should be considered for improving the estimates of the above-ground biomass. In addition, the right choice of seasonal data (summer) was found to be important for estimating the forest biomass, while other seasonal data were either insensitive or pointless. The promising results shown by the VSI suggest that it could be an appropriate candidate for monitoring vegetation structure from the multi-angular satellite remote sensing.

Optical characterization of fiber-reinforced thermoplastic tapes for laser-based composite manufacturing

The optical properties of unidirectional (UD) fiber reinforced thermoplastic (FRTP) tapes were characterized to enable a better description of the heating phase in laser-based manufacturing process of FRTP composites. The tapes included PP-GF (glass-fiber) 45% fiber volume content (FVC), PVDF-CF (carbon-fiber) 45% FVC, PVDF-CF 60% FVC and PA12-CF 60% FVC. The transmittance of the tapes was found to be 0.00–0.2% whereas the reflectance was 9.8–11.8% corresponding to a refractive index of 1.91–2.05. The anisotropic reflectance measurements, as obtained through a gonioreflectometry, were used to fit the bidirectional reflectance distribution function (BRDF) for the first time. The obtained BRDF parameters σt and σf had a range of 0.1–0.18 and 0.006–0.015, respectively, for different tapes. Employing the new BRDF parameters empowers a more accurate prediction and optimization of the process settings of laser-based composite manufacturing.Laser-assisted tape placement/winding (LATP/LATW), anisotropic reflection, unidirectional tape (UD), bidirectional reflection modelling.

Reconstruction of Spatiotemporally Continuous MODIS-Band Reflectance in East and South Asia from 2012 to 2015

To reconstruct Moderate Resolution Imaging Spectroradiometer (MODIS) band reflectance with optimal spatiotemporal continuity, three bidirectional reflectance distribution function (BRDF) models—the Ross-Thick-Li-Sparse Reciprocal (RTLSR) model, Gao model, and adjusted BF model—were used to retrieve MODIS-band reflectance for cloudy MODIS pixels according to different inversion conditions with a proposed filling algorithm. Then, a spatiotemporally continuous MODIS-band reflectance dataset for most of Asia with more than 98% spatiotemporal coverage was reconstructed from 2012 to 2015. The validation highlighted an evident improvement in filling cloudy MODIS observations; a reasonable spatial distribution, such as in South Asia and Southeast Asia; and acceptable precision for the filled MODIS pixels, with the root mean square error percentage (RMSE%) at 9.7–9.8% and 12–16% for the Gao and adjusted BF models, respectively. In the course of reconstructing the spatiotemporal continuous MODIS-band reflectance, the differences among the three models were discussed further. For a 16-day period with a stable and unchanged land surface, the RTLSR model, as a basic model, accurately derived land surface reflectance (no more than 10% RMSE% for MCD43C1 V006 band 1) and outperformed the other two models. When the inversion period is sufficiently long (e.g., 108 days, 188 days, 268 days, or a full year), the Gao/adjusted BF model provides better precision than the RTLSR model by considering the normalized difference vegetation index (NDVI) and soil moisture/NDVI as intermediate variables used to adjust the BRDF parameters in real time. The Gao model is optimal when the inversion period is sufficiently long. Based on combining the RTLSR model and Gao/adjusted BF model, we proposed a filling algorithm to derive a dataset of MODIS-band reflectance with optimal spatiotemporal continuity.

A Hyperspectral Bidirectional Reflectance Model for Land Surface.

A hyperspectral bidirectional reflectance (HSBR) model for land surface has been developed in this work. The HSBR model includes a very diverse land surface bidirectional reflectance distribution function (BRDF) database with ~40,000 spectra. The BRDF database is saved as Ross-Li parameters, which can generate hyperspectral reflectance spectra at different sensor and solar observation geometries. The HSBR model also provides an improved method for generating hyperspectral surface reflectance using multiband satellite measurements. It is shown that the land surface reflective spectrum can be easily simulated using BRDF parameters or reflectance at few preselected wavelengths. The HSBR model is validated using the U.S. Geological Survey (USGS) vegetation database and the AVIRIS reflectance product. The simulated reflective spectra fit the measurements very well with standard deviations normally smaller than 0.01 in the unit of reflectivity. The HSBR model could be used to significantly improve the quality of the reflectance products of satellite and airborne sensors. It also plays important role for intercalibration among space-based instruments and other land surface related applications.

Development of the Direct-Estimation Albedo Algorithm for Snow-Free Landsat TM Albedo Retrievals Using Field Flux Measurements

Anisotropy information from moderate-to-coarse-resolution sensors [e.g., 500-m Moderate Resolution Imaging Spectroradiometer (MODIS)] is widely applied to estimate high-resolution surface albedo. Simulated albedos using MODIS bidirectional reflectance distribution function (BRDF) parameters as prior knowledge based on the kernel-driven model are employed to build and assess the lookup table (LUT) of the direct-estimation method, which is then used to estimate high-resolution albedos directly from top-of-atmosphere (TOA) reflectance data (e.g., Landsat albedo). Previously, the errors in the simulated albedos were not considered in building and assessing the LUT. In this article, daytime time-series (30 min) of snow-free albedo measurements with sufficient solar zenith angles (SZAs) were introduced to build the LUT for snow-free Landsat TM surface shortwave broadband albedo (TM albedo) retrievals, together with TOA-simulated reflectance by concurrent daily MODIS BRDF parameters. The assessment utilizes an independent data set and shows larger discrepancies between the estimated and measured albedos [root-mean-square errors (RMSEs) of >0.03 at SZAs ≥ 60°] than those in previous articles. To reduce inconsistencies between the MODIS BRDF parameters and the observed albedos, as well as possible spatial resolution differences between the MODIS and Landsat data, we adopted a correction strategy that first linearly adjusts the MODIS BRDF parameters to match the albedo measurements by a magnitude method, and second, the TOA reflectance simulations were further corrected by concurrent TM reflectances. The developed algorithm shows a significant improvement after using such corrections as a priori (RMSE < 0.02 at SZA ≤ 75°). The validation indicates improved accuracies in the TM albedo estimation. These improvements may provide potential albedo estimations for nadir-viewing high-resolution sensors using coarse-resolution anisotropy information.

Estimating Forest Canopy Height Using MODIS BRDF Data Emphasizing Typical-Angle Reflectances

Forest-canopy height is an important parameter for the estimation of forest biomass and terrestrial carbon flux and climate-change research at regional and global scales. Currently, various methods combining Light Detection and Ranging (LiDAR) data with various auxiliary data, particularly satellite remotely sensed reflectances, have been widely used to produce spatially continuous canopy-height products. However, current methods in use for remote sensing reflectances mainly focus on the nadir view direction, while anisotropic reflectances, which are theoretically more sensitive to the forest canopy height in the multiangle remote sensing field, have rarely been explored. Here, we attempted to examine the potential of using modeled multiangle reflectances at three typical viewing angles (i.e., from the hotspot, darkspot, and nadir directions) to estimate forest-canopy height as auxiliary data sources. First, the sensitivities of the typical angular reflectances as a function of forest canopy height were fully examined using the Extended Fourier Amplitude Sensitivity Test (EFAST) method based on the 4-scale Bidirectional Reflectance Distribution Function (BRDF) model simulations. This indicated that reflectances in the off-nadir viewing directions are generally sensitive to canopy-height variations. Then, the canopy heights were extracted from airborne Laser Vegetation Imaging Sensor (LVIS) data, which were further divided into training and validation data. Moderate Resolution Imaging Spectroradiometer (MODIS) multiangle reflectances at typical viewing angles were calculated from the MODIS BRDF parameter product (MCD43A1, version 6) as partial training-input data, based on a hotspot-adjusted, kernel-driven linear BRDF model. Subsequently, the Random Forest (RF) machine learning model was trained to acquire the relationship between the extracted canopy heights and the corresponding MODIS typical viewing reflectances. The trained model was further applied to estimate the canopy height metrics in the study areas of Howland Forest, Harvard Forest, and Bartlett Forest. Finally, the estimated canopy heights were independently validated by canopy heights extracted from the LVIS data. The results indicate that the canopy heights modeled through this method exhibit generally high accordance with the LVIS-derived canopy heights (R = 0.65−0.67; RMSE = 3.63−5.78). The results suggest that the MODIS multiangle reflectance data at typical observation angles contain important information regarding forest canopy height and can, therefore, be used to estimate forest canopy height for various ecological applications.

A geometry-dependent surface Lambertian-equivalent reflectivity product for UV–Vis retrievals – Part 1: Evaluation over land surfaces using measurements from OMI at 466 nm

Abstract. The anisotropy of the Earth's surface reflection has implications for satellite-based retrieval algorithms that utilize climatological surface reflectivity databases that do not depend upon the observation geometry. This is the case for most of the current ultraviolet and visible (UV–Vis) cloud, aerosol, and trace-gas algorithms. The illumination–observation dependence of surface reflection is described by the bidirectional reflectance distribution function (BRDF). To account for the BRDF effect, we use the concept of geometry-dependent surface Lambertian-equivalent reflectivity (GLER), which is derived from the top-of-atmosphere (TOA) radiance computed with Rayleigh scattering and surface BRDF for the exact geometry of a satellite-based pixel. We present details on the implementation of land and water surface BRDF models, and we evaluate our GLER product over land surfaces using observed Sun-normalized radiances at 466 nm. The input surface BRDF parameters for computing TOA radiance are derived from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations. The observed TOA radiance for comparison is from the Ozone Monitoring Instrument (OMI). The comparison shows good agreement between observed and calculated OMI reflectivity in 2006 in typical geographical regions, with correlation coefficients greater than 0.8 for some regions. Seasonal variations of clear-sky OMI reflectivity (i.e., with minimum clouds and aerosols) closely follow those computed using MODIS-derived GLER over land. GLER also captures the cross-track dependence of OMI-derived LER, though the latter is slightly higher than the former presumably owing to residual cloud and aerosol (nonabsorbing) contamination, particularly over dark surfaces (heavily vegetated regions such as mixed forest, croplands, and grasslands). Calibration differences between OMI and MODIS may also be responsible for some of this bias. The standard OMI climatological surface reflectivity database predicts higher radiances than GLER and OMI observations with different seasonal variation over most regions and does not have any angular-dependent variation. Overall, our evaluation demonstrates that the GLER product adequately accounts for surface BRDF effects while at the same time simplifying the surface BRDF implementation within the existing OMI retrieval infrastructure; use of our GLER product requires changes only to the input surface reflectivity database.

A Method for Landsat and Sentinel 2 (HLS) BRDF Normalization

The Harmonized Landsat/Sentinel-2 (HLS) project aims to generate a seamless surface reflectance product by combining observations from USGS/NASA Landsat-8 and ESA Sentinel-2 remote sensing satellites. These satellites’ sampling characteristics provide nearly constant observation geometry and low illumination variation through the scene. However, the illumination variation throughout the year impacts the surface reflectance by producing higher values for low solar zenith angles and lower reflectance for large zenith angles. In this work, we present a model to derive the bidirectional reflectance distribution function (BRDF) normalization and apply it to the HLS product at 30 m spatial resolution. It is based on the BRDF parameters estimated from the MODerate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product (M{O,Y}D09) at 1 km spatial resolution using the VJB method (Vermote et al., 2009). Unsupervised classification (segmentation) of HLS images is used to disaggregate the BRDF parameters to the HLS spatial resolution and to build a BRDF parameters database at HLS scale. We first test the proposed BRDF normalization for different solar zenith angles over two homogeneous sites, in particular one desert and one Peruvian Amazon forest. The proposed method reduces both the correlation with the solar zenith angle and the coefficient of variation (CV) of the reflectance time series in the red and near infrared bands to 4% in forest and keeps a low CV of 3% to 4% for the deserts. Additionally, we assess the impact of the view zenith angle (VZA) in an area of the Brazilian Amazon forest close to the equator, where impact of the angular variation is stronger because it occurs in the principal plane. The directional reflectance shows a strong dependency with the VZA. The current HLS BRDF correction reduces this dependency but still shows an under-correction, especially in the near infrared, while the proposed method shows no dependency with the view angles. We also evaluate the BRDF parameters using field surface albedo measurements as a reference over seven different sites of the US surface radiation budget observing network (SURFRAD) and five sites of the Australian OzFlux network.

Improving the AVHRR Long Term Data Record BRDF Correction

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

Toward Landsat and Sentinel-2 BRDF Normalization and Albedo Estimation: A Case Study in the Peruvian Amazon Forest

The Amazon forest has been the focus of study by the science community during the last few decades. Remote sensing data analysis is the only way to study such a large geographical extent during an extended period of time. Since the launch in 2015 of Sentinel 2 and its increase in temporal resolution through the combination with Landsat sensors, a strong emphasis has been put on exploiting these data. Though these satellites provide near nadir observations, surface reflectance time series are affected by illumination variability throughout the year. These effects can be corrected using a Bidirectional Reflectance Distribution Function (BRDF) model. Franch et al. (2014a) developed a methodology to derive Landsat surface albedo and BRDF. It is based on the BRDF parameters from the MODerate Resolution Imaging Spectroradiometer (MODIS) which are disaggregated at Landsat spatial resolution (30 m). In this work, we apply the Franch et al. (2014a) method to normalize the surface reflectance for BRDF effects using the NASA's Harmonized Landsat Sentinel-2 (HLS) product. We apply this method to the Tambopata region in Peru from 2013 to 2017 and validate it using ground-based albedometer measurements. The results show that the near infrared reflectance can increase up to 0.06 (20%) for low solar angles while the impact on the red range and the NDVI is minor (<0.01). The evaluation of the surface albedo against field measurements shows an error of 0.01.

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.

Measurement and modeling of bidirectional reflectance distribution function (BRDF) on cutting surface based on the coaxial optical microscopic imaging

In this study, a new bidirectional reflectance distribution function (BRDF) measurement and modeling method are proposed for cutting surfaces to address problems in the use of the current measuring method and model to obtain BRDF: the range of measuring angle is limited, and more optical reflection information cannot be accurately described. The measuring method and principle of the BRDF are presented under the condition of coaxial optical microscopic imaging. The BRDF measuring platform comprised confocal laser scanning microscope, and BRDF measurements of different cutting sample surfaces were measured. The measured result demonstrates that the trend of variation in BRDF is related to the roughness and microtopography of the sample’s surface. The parameters of the BRDF model are based on the sample surface reflection characteristics. Based on the genetic algorithm, the model parameters of BRDF were obtained through fitting processing of measured data, and the maximum fitting error was 9.97%. The result demonstrates that the BRDF model is feasible and can accurately describe the reflection characteristics of the cutting surface. The study will lay a foundation for the three-dimensional reconstruction of the cutting surface under the condition of microscopic imaging.

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).

Estimation of all-sky instantaneous surface incident shortwave radiation from Moderate Resolution Imaging Spectroradiometer data using optimization method

Surface incident shortwave radiation (ISR) is a crucial parameter in the land surface radiation budget. Many reanalysis, observation-based, and satellite-derived global radiation products have been developed but often have insufficient accuracy and spatial resolution for many applications. In this paper, we propose a method based on a radiative transfer model for estimating surface ISR from Moderate Resolution Imaging Spectroradiometer (MODIS) Top of Atmosphere (TOA) observations by optimizing the surface and atmospheric variables with a cost function. This approach consisted of two steps: retrieving surface bidirectional reflectance distribution function parameters, aerosol optical depth (AOD), and cloud optical depth (COD); and subsequently calculating surface ISR. Validation against measurements at seven Surface Radiation Budget Network (SURFRAD) sites resulted in an R2 of 0.91, a bias of −6.47 W/m2, and a root mean square error (RMSE) of 84.17 W/m2 (15.12%) for the instantaneous results. Validation at eight high-latitude snow-covered Greenland Climate Network (GC-Net) sites resulted in an R2 of 0.86, a bias of −21.40 W/m2, and an RMSE of 84.77 W/m2 (20.96%). These validation results show that the proposed method is much more accurate than the previous studies (usually with RMSEs of 80-150 W/m2). We further investigated whether incorporating additional satellite products, such as the MODIS surface broadband albedo (MCD43), aerosol (MOD/MYD04), and cloud products (MOD/MYD06), as constraints in the cost function would improve the accuracy. When the AOD and COD estimates were constrained, RMSEs were reduced to 62.19 W/m2 (12.12%) and 71.70 W/m2 (17.74%) at the SURFRAD and GC-Net sites, respectively. This algorithm could estimate surface ISR with MODIS TOA observations over both snow-free and seasonal/permanent snow-covered surfaces. The algorithm performed well at high-latitude sites, which is very useful for radiation budget research in the polar regions.