Extending a Linear Kernel-Driven BRDF Model to Realistically Simulate Reflectance Anisotropy Over Rugged Terrain

Multi-angle remote sensing observations play an important role in the remote sensing of solar radiation absorbed by land surfaces. Currently, the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) teams have successively applied the Ross–Li kernel-driven bidirectional reflectance distribution function (BRDF) model to integrate multi-angle observations to produce long time series BRDF model parameter products (MCD43 and VNP43), which can be used for the inversion of various surface parameters and the angle correction of remote sensing data. Even though the MODIS and VIIRS BRDF products originate from sensors and algorithms with similar designs, the consistency between BRDF parameters for different sensors is still unknown, and this likely affects the consistency and accuracy of various downstream parameter inversions. In this study, we applied BRDF model parameter time-series data from the overlapping period of the MODIS and VIIRS services to systematically analyze the temporal and spatial differences between the BRDF parameters and derived indices of the two sensors from the site scale to the region scale in the red band and NIR band, respectively. Then, we analyzed the sensitivity of the BRDF parameters to variations in Normalized Difference Hotspot–Darkspot (NDHD) and examined the spatiotemporal distribution of zero-valued pixels in the BRDF parameter products generated by the constraint method in the Ross–Li model from both sensors, assessing their potential impact on NDHD derivation. The results confirm that among the three BRDF parameters, the isotropic scattering parameters of MODIS and VIIRS are more consistent, whereas the volumetric and geometric-optical scattering parameters are more sensitive and variable; this performance is more pronounced in the red band. The indices derived from the MODIS and VIIRS BRDF parameters were compared, revealing increasing discrepancies between the albedo and typical directional reflectance and the NDHD. The isotropic scattering parameter and the volumetric scattering parameter show responses that are very sensitive to increases in the equal interval of the NDHD, indicating that the differences between the MODIS and VIIRS products may strongly influence the consistency of NDHD estimation. In addition, both MODIS and VIIRS have a large proportion of zero-valued pixels (volumetric and geometric-optical parameter layers), whereas the spatiotemporal distribution of zero-valued pixels in VIIRS is more widespread. While the zero-valued pixels have a minor influence on reflectance and albedo estimation, such pixels should be considered with attention to the estimation accuracy of the vegetation angular index, which relies heavily on anisotropic characteristics, e.g., the NDHD. This study reveals the need in optimizing the Clumping Index (CI)-NDHD algorithm to produce VIIRS CI product and highlights the importance of considering BRDF product quality flags for users in their specific applications. The method used in this study also helps improve the theoretical framework for cross-sensor product consistency assessment and clarify the uncertainty in high-precision ecological monitoring and various remote sensing applications.

Anisotropic canopy reflectance plays a crucial role in estimating vegetation biophysical parameters, whereas soil reflectance anisotropy affects canopy reflectance. However, woodland canopy bidirectional reflectance distribution function (BRDF) models considering soil anisotropy are far from universal, especially for the BRDF models of mountain forest. In this study, a mountain forest canopy model, named geometric-optical and mutual shadowing and scattering from arbitrarily inclined-leaves model coupled with topography (GOSAILT), was extended to consider the soil anisotropic reflectance characteristics by introducing the simple soil directional (SSD) reflectance model. The modified GOSAILT model (named GOSAILT-SSD) was evaluated using unmanned aerial vehicle (UAV) field observations and discrete anisotropic radiative transfer (DART) simulations. Then, the effects of Lambertian soil assumption on simulating the vi-directional reflectance factor (BRF) were evaluated across different fractions of vegetation cover (Cv), view zenith angles (VZA), solar zenith angles (SZA), and spectral bands with the GOSAILT-SSD model. The evaluation results, with the DART simulations, show that the performance of the GOSAILT-SSD model in simulating canopy BRF is significantly improved, with decreasing RMSE, from 0.027 to 0.017 for the red band and 0.051 to 0.037 for the near-infrared (NIR) band. Meanwhile, the GOSAILT-SSD simulations show high consistency with UAV multi-angular observations (R2 = 0.97). Besides, it is also found that the BRF simulation errors caused by Lambertian soil assumption are too large to be neglected, with a maximum relative bias of about 45% for the red band. This inappropriate assumption results in a remarkable BRF underestimation near the hot spot direction and an obvious BRF overestimation for large VZA in the solar principal plane (PP). Meanwhile, this simulation bias decreases with the increase of fraction of vegetation cover. This study provides an effective technique to improve the capability of the mountain forest canopy BRDF model by considering the soil anisotropic characteristics for advancing the modeling of radiative transfer (RT) processes over rugged terrain.

Rugged terrain, including mountains, hills, and some high lands are typical land surfaces around the world. As a physical parameter for characterizing the anisotropic reflectance of the land surface, the importance of the bidirectional reflectance distribution function (BRDF) has been gradually recognized in the remote sensing community, and great efforts have been dedicated to build BRDF models over various terrain types. However, on rugged terrain, the topography intensely affects the shape and magnitude of the BRDF and creates challenges in modeling the BRDF. In this paper, after a brief introduction of the theoretical background of the BRDF over rugged terrain, the status of estimating land surface BRDF properties over rugged terrain is comprehensively reviewed from a historical perspective and summarized in two categories: BRDFs describing solo slopes and those describing composite slopes. The discussion focuses on land surface reflectance retrieval over mountainous areas, the difference in solo slope and composite slope BRDF models, and suggested future research to improve the accuracy of BRDFs derived with remote sensing satellites.

The accuracy of hotspot signatures is crucial to the development of algorithms for the retrieval of various biophysical parameters of terrestrial surface targets. The hotspot effect determined by the kernel-driven RossThick-LiSparseReciprocal (RTLSR) Bidirectional Reflectance Distribution Function (BRDF) model fully relies on the performance of the two kernels in modeling the hotspot effect. Previously, a method has been developed to correct the volumetric scattering component of the RTLSR model (Jiao et al., 2016); however, in few cases that the weight of the volumetric scattering component (f vol ) is no longer significant (e.g., f vol = 0) in the framework of the RTLSR model, the slight underestimation of the hotspot effect still exists. In this study, we propose a method to enhance the overlap function inherent in the geometric-optical (GO) kernel using a physical hotspot factor. The hotspot observations extracted from the entire archive of the POLDER-3 BRDF database are used to determine two parameters of this hotspot factor. Result shows that the proposed method further improves the model-observation fits in the vicinity of hotspot direction, particularly in some extreme cases where the GO scattering component is fully dominant in the multiangle measurements. Such an improved GO kernel, combining with the hotspot adjustment method for the volumetric scattering kernel, reconstructs hotspot effect more accurately; therefore, necessarily further improves the performance of the kernel-driven BRDF model particularly for the retrieval of the canopy structure parameters that is associated with the hotspot effect.

Previous work has successfully demonstrated the application of a priori knowledge to bidirectional reflectance distribution function (BRDF) model inversion. The a priori knowledge used in the tests is the general knowledge which can be applied to all land cover types. One of the advantages of general knowledge is that it can be applied independently and doesn't need more information. However, the inversion accuracy may be low, compared to results using correct specific knowledge. This paper mainly focuses on the accumulation of a priori knowledge for the linear BRDF model in both general and specific realms. The authors first provide some specific knowledge based on ground and spaceborne BRDF measurements for BRDF model parameters.

A modified version of the kernel-driven model for correcting the diffuse light of ground multi-angular measurements

Abstract. The term “hotspot” refers to the sharp increase in the reflectance occurring when incident (solar) and reflected (viewing) directions almost coincide in the backscatter direction. The accurate simulation of hotspot directional signatures is important for many remote sensing applications. The RossThick–LiSparse–Reciprocal (RTLSR) bidirectional reflectance distribution function (BRDF) model is widely used in radiative transfer simulations, and the hotspot model mostly used is from Maignan–Bréon, but it typically requires large values of numerical quadrature and Fourier expansion terms in order to represent the hotspot accurately for its use coupled with atmospheric radiative transfer modeling (RTM). In this paper, we have developed a modified version based on the Maignan–Bréon's hotspot BRDF model that converges much faster numerically, making it more practical for use in the RTMs that require Fourier expansion of BRDF to simulate the top-of-atmosphere (TOA) hotspot signatures, such as in the RTM models using the Doubling–Adding or discrete ordinate method. Using the vector linearized discrete ordinate radiative transfer model (VLIDORT), we found that reasonable TOA–hotspot accuracy can be obtained with just 23 Fourier terms for clear atmosphere and 63 Fourier terms for atmosphere with aerosol scattering. In order to study the impact of molecular and aerosol scattering on hotspot signatures, we carried out a number of hotspot signature simulations with VLIDORT. We confirmed that (1) atmospheric molecule scattering and the existence of aerosol tend to smooth out the hotspot signature at the TOA and that (2) the hotspot signature at the TOA in the near-infrared is larger than in the visible, and its impact by surface reflectance is more significant. As the hotspot amplitude at the TOA with aerosol scattering included is smaller than that with molecular scattering only, the amplitude of hotspot signature at the surface is likely underestimated in the previous analysis based on the POLDER measurements, where the atmospheric correction was based on a single-scatter Rayleigh-only calculation. This modified model can calculate the amplitude of the hotspot accurately, and, as it agrees very well with the original RossThick model away from the hotspot region, this model can be simply used in conditions with and without hotspots. However, there are some differences in this modified model compared to the original Maignan–Bréon model for the scattering angles close to the hotspot point; thus, it may not be appropriate for those who need an exact representation of the hotspot angular signature.

This paper explores snow bidirectional reflectance distribution function (BRDF) properties over some snow covered regions using Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-angle Imaging SpectroRadiometer (MISR) surface reflectance products. In the visible and near infrared (NIR) region, MODIS and MISR surface bidirectional reflectance factors (BRFs) over snow are accumulated to extract snow BRDF properties. Five surface BRDF models are concerned to simulate snow surface reflectance shape. All the models capture the distribution of snow BRFs with limit of accuracy. The simulated BRFs from several models have similar distribution trend and different details. The BRDF properties discussed can be used as background in the snow BRDF retrieval from spaceborne measurements.

An optimized Back-Propagation network (BP network) based on Genetic Algorithm (GA) was introduced to construct bidirectional reflectance distribution function (BRDF) model. To verify the performance of GA-BP network, two different kinds of space target materials were used for experiment. Based on the experimental data, we used GA to simulate the undetermined parameters of a five-parameter BRDF model, and used GA-BP network and BP network to construct a new BRDF model respectively. The fitting results manifest that the GA-BP network is suitable for construct a new BRDF model and outperforms the five-parameter BRDF model in speed and accuracy under the same condition.

The bidirectional reflectance distribution function (BRDF) is a physical quantity that represents the change of surface reflection with the Sun and the direction of observation, which is of great significance to the study of surface anisotropic reflection characteristics. In this paper, based on MODIS (Moderate Resolution Imaging Spectroradiometer) BRDF model parameters products (MCD43A1), we utilize the Ross-Li model to simulate the surface reflectance of the four land surface types in North China: vegetation, bare soil, cropland, and urban, and comparatively analyze the seasonal variation of their surface anisotropic reflection characteristics. Therefore, this study can provide reliable scientific basis for improving land surface process model, promoting surface-atmosphere interaction and global climate change research. The results show that: (1) The backscattering of the four land surface types is greater than the forward scattering, and the larger the scattering angle is, the larger the bidirectional reflectance will be. The distribution trend of bidirectional reflectance of different surface types is quite different in different bands and seasons. (2) The bidirectional reflectance of the four land surface types varies with the wavelength roughly the same in spring, summer, and autumn. In winter, due to snow covering the ground, the bidirectional reflectance of vegetation, cropland, and urban is higher in visible and near-infrared bands. Due to the fixed simulation angle, the distribution trend of the bidirectional reflectance of bare soil in four seasons is multipeak in the multi-band range.

The evaluation of the first available satellite‐based global albedo product at 1‐km resolution is essential for its application in climate studies. We evaluate the accuracy of the Moderate‐Resolution Imaging Spectroradiometer (MODIS) albedo product using available field measurements at Surface Radiation Budget Network (SURFRAD) and Cloud and Radiation Testbed–Southern Great Plains (CART/SGP) stations and examine the consistency between the MODIS surface albedos and the Clouds and Earth's Radiant Energy System (CERES) top‐of‐the‐atmosphere albedos as well as historical global albedos from advanced very high resolution radiometer (AVHRR) and Earth Radiation Budget Experiment (ERBE) observations. A comparison with the field measurements shows that the MODIS surface albedo generally meets an absolute accuracy requirement of 0.02 for our study sites during April–September 2001, with the root mean square errors less than 0.018. Larger differences appear in the winter season probably due to the increased heterogeneity of surface reflectivity in the presence of snow. To examine the effect of spatial heterogeneity on the validation of the MODIS albedos using fine resolution field measurements, we derive an intermediate albedo product from four Landsat Enhanced Thematic Mapper Plus (ETM+) images at 30‐m spatial resolution as a surrogate for the distributed field measurements. The surface albedo is relatively homogeneous over the study stations in growing seasons, and therefore the validation during April–September is supported. A case study over three SURFRAD stations reveals that the MODIS bidirectional reflectance distribution function model is able to capture the solar zenith angle dependence of surface albedo as shown by the field measurements. We also find that the MODIS surface shortwave albedo is consistent with the contemporary and collocated CERES top‐of atmosphere albedos derived directly from broadband observations. The MODIS albedo is also well correlated with historical surface albedos derived from AVHRR and ERBE observations, and a high bias of 0.016 and a low bias of 0.034 compared to those of the latter albedos are reasonable considering the differences in instruments and retrieval algorithms as well as environmental changes.

The quality of Earth observation (EO) based vegetation monitoring has improved during recent years, which can be attributed to the enhanced sensor design of new satellites such as MODIS (Moderate Resolution Imaging Spectroradiometer) on Terra and Aqua. It is however expected that sun-sensor geometry variations will have a more visible impact on the Normalized Difference Vegetation Index (NDVI) from MODIS compared to earlier data sources, since noise related to atmosphere and sensor calibration is substantially reduced in the MODIS data stream. For this reason, the effect of varying MODIS viewing geometry on red, near-infrared (NIR) and NDVI needs to be quantified. Data from the geostationary MSG (Meteosat Second Generation) SEVIRI (Spinning Enhanced Visible and Infrared Imager) sensor is well suited for this purpose due to the fixed position of the sensor, the spectral resolution, including a red and NIR band, and the high temporal resolution (15 min) of data, enabling MSG data to be used as a reference for estimating MODIS surface reflectance and NDVI variations caused by varying sun-sensor geometry. The study was performed on data covering West Africa for periods of lowest possible cloud cover for three consecutive years (2004–2006). An analysis covering the entire range of NDVI revealed day-to-day variations in observed MODIS NDVI of 50–60% for medium dense vegetation (NDVI ≈ 0.5) caused by variations in MODIS view zenith angles (VZAs) between nadir and the high forward-scatter view direction. Statistical analysis on red, NIR and NDVI from MODIS and MSG SEVIRI for three transects (characterized by different vegetation densities) showed that both MODIS red and NIR reflectances are highly dependant on MODIS VZA and relative azimuth angle (RAA), due to the anisotropic behaviour of red and NIR reflectances. The anisotropic reflectance in the red and NIR band was to some degree minimized by the ratioing properties of NDVI. The minimization by the NDVI normalization is very dependent on the vegetation density however, since the degree of anisotropy in red and NIR reflectances depends on the amount of vegetation present. MODIS VZA and RAA effects on NDVI were highest for medium dense vegetation (NDVI ≈ 0.5–0.6). The VZA and RAA effects were less for sparsely vegetated areas (NDVI ≈ 0.3–0.35) and the smallest effect on NDVI was found for dense vegetation (NDVI ≈ 0.7). These results have implications for the end users' interpretation of NDVI, and challenge the expediency of the MODIS NDVI compositing technique, which should be refined to distinguish between forward- and backward-scatter viewing direction by taking RAA into account.

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

Mapping accurately vegetation type is one of the main challenges for monitoring arid and semi‐arid grasslands with remote sensing. The multi‐angle approach has been demonstrated to be useful for mapping vegetation types in deserts. The current paper presents a study on the use of directional reflectance derived from two sensor systems, using two different models to analyse the data and two different classifiers as a means of mapping vegetation types. The multiangle imaging spectroradiometer (MISR) and the moderate resolution imaging spectroradiometer (MODIS) provide multi‐spectral and angular, off‐nadir observations. In this study, we demonstrate that reflectance from MISR observations and reflectance anisotropy patterns derived from MODIS observations are capable of working together to increase classification accuracy. The patterns are described by parameters of the modified Rahman–Pinty–Verstraete and the RossThin‐LiSparseMODIS bidirectional reflectance distribution function (BRDF) models. The anisotropy patterns derived from MODIS observations are highly complementary to reflectance derived from radiances observed by MISR. Support vector machine algorithms exploit the information carried by the same data sets more effectively than the maximum likelihood classifier.

Observing system simulations for small satellite formations estimating bidirectional reflectance