An empirical study on the utility of BRDF model parameters and topographic parameters for mapping vegetation in a semi‐arid region with MISR imagery

In this study we show that multiangle remote sensing is useful for increasing the accuracy of vegetation community type mapping in desert regions. Using images from the National Aeronautics and Space Administration (NASA) Multiangle Imaging Spectroradiometer (MISR), we compared roles played by Bidirectional Reflectance Distribution Function (BRDF) model parameters with those played by topographic parameters in improving vegetation community type classifications for the Jornada Experimental Range and the Sevilleta National Wildlife Refuge in New Mexico, USA. The BRDF models used were the Rahman–Pinty–Verstraete (RPV) model and the RossThin‐LiSparseReciprocal (RTnLS) model. MISR nadir multispectral reflectance was considered as baseline because nadir observation is the most basic remote sensing observation. The BRDF model parameters and the topographic parameters were considered as additional data. The BRDF model parameters were obtained by inversion of the RPV model and the RTnLS model against the MISR multiangle reflectance data. The results of 32 classification experiments show that the BRDF model parameters are useful for vegetation mapping; they can be used to raise classification accuracies by providing information that is not available in the spectral‐nadir domain, or from ancillary topographic parameters. This study suggests that the Moderate Resolution Imaging Spectroradiometer (MODIS) and MISR BRDF model parameter data products have great potential to be used as additional information for vegetation mapping.     

Seasonal and inter-annual variation in view angle effects on MODIS vegetation indices at three forest sites

The bi-directional reflectance distribution function (BRDF) has been widely studied across different vegetation types. However, these studies generally report values for only one point in time. We were interested in the potential for seasonal and inter-annual variation in BRDF parameters. NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on board the EOS satellites has now been collecting data for 10 years. Since BRDF parameters are reported for the individual spectral bands, these data can be used to examine intra-annual variation. However, MODIS BRDF parameters are not calculated for the various vegetation indices which are derived from the spectral bands. Our objective in this study was to use the 10 years of MODIS data now available to examine seasonal and inter-annual variation in the view angle sensitivity of three vegetation indices; the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), and the photochemical reflectance index (PRI) at 3 flux tower sites (Harvard Forest, Howland Forest and Morgan Monroe State Forest). For these 3 sites, only EVI was significantly affected by view angle. There was also a substantial variation in the view angle sensitivity of EVI across seasons and this variation was different for backscatter vs. forward scatter data. It is possible that differences in the scattering of radiation between the spring and the fall are responsible for the seasonal difference in view angle sensitivity. There were also complimentary variations in forward and backscatter view angle sensitivity of EVI across years. The greater view angle sensitivity of EVI, as opposed to NDVI, suggests that greater care must be taken to correct for BRDF effects when using this vegetation index.     

Integration and Case Analysis of BRDF Model based on Linear Kernel\|driven Model#br#

Anisotropic reflectance is the intrinsic characteristic of an object surface.over the past few decades,various BRDF models have been developed for investigating the relationship between the vegetation canopy and reflectance anisotropy.This helps to retrieve biophysical parameters from the anisotropic reflectance patterns of vegetation canopy.In this study,for the purpose of assisting potential users to use these models,and to improve the understanding of the BRDF modeling,several BRDF models that are widely used in the remote sensing community have been integrated with the current version of the MaKeMAT (Multi\|angular Kernel\|driven Model Analysis Tool),based on the Interactive Data Language (IDL).This work retains all functions of the current version of the MaKeMAT model,meanwhile,adds some new functions by integrating these physical BRDF models.Undoubtedly,this work facilitates the potential users to process BRDF data and make further analysis in their work by operating a simpler visual interface.This helps to build a rapid communication between the kernel\|driven BRDF models and the physical BRDF models.Our initial results show that this model\|integration practice is a valuable reference for potential users to devise a similar technique.Our case study in coupling these physical BRDF models with the kernel\|driven models present a high correlation between them,with the determination of coefficients (R²) reaching 0.899~0.989 in the red and NIR bands.     

A Light Scattering Model for Layered Dielectrics with Rough Surface Boundaries

A new model for the scattering of light from layered dielectrics with rough surface boundaries is introduced. The model contains a surface scattering component together with a subsurface scattering component. The former component corresponds to the roughness on the upper surface boundary and is modeled using the modified Beckmann model. The latter component accounts for both refraction due to Fresnel transmission through the layer and rough scattering at the lower layer boundary. One interesting consequence of the model is that the peak radiance is deflected away from the specular direction, a behavior that is also evident in BRDF data from human skin. By allowing independent roughness parameters for each surface boundary and controlling the contributions from the two scattering components in the outgoing radiance using a balance parameter, we can achieve excellent fits of the model to the measured BRDF data. We experiment with BRDF data from skin surface samples (human volunteers) and show that the new model outperforms alternative variants of the Beckmann model and the Lafortune et al. reflectance model. As an application in computer graphics, we also show that realistic images of 3D surfaces can be generated using the new model, by setting the values of its physical parameters.     

Variations in reflectance with seasonality and viewing geometry: implications for semi-arid vegetation mapping with MISR data

The purpose of this article is to understand the effect of multi-temporal multi-angle data on vegetation community type mapping in desert regions. Based on data from the multi-angle imaging spectroradiometer (MISR), a set of 46 multi-temporal classification experiments were carried out in the Jornada Experimental Range in New Mexico, USA. Besides multi-angle observations, bidirectional reflectance distribution function (BRDF) model parameters were also used as input data for the classifications. The experiments used two widely accepted BRDF models, the Rahman–Pinty–Verstraete (RPV) model and the Ross-thin Li-sparse reciprocal (RTnLS) model. The experiments show that multi-temporal multi-angle classifications can yield a more accurate mapping than multi-temporal nadir classifications, and multi-temporal BRDF model parameters combined with a single nadir image can provide an accuracy roughly the same as all multi-temporal multi-angle observations for the vegetation mapping. These findings opened not only a path of reducing data dimensionality for multi-temporal multi-angle classifications, but also a way of merging products of both MISR and moderate resolution imaging spectroradiometer (MODIS) to improve semi-arid vegetation mapping.     

基于SAILH模型的植被冠层NDVI二向性分析

NDVI是植被遥感中最为常用的一种植被指数,建立NDVI与其他冠层参数模型必须考虑其方向性问题.本文基于SAILH模型讨论了连续植被冠层NDVI的二向性特征,并分析了叶面积指数、叶倾角分布、热点参数以及太阳天顶角和相对方位角对NDVI的影响.研究表明冠层NDVI在主平面观测方向存在一个明显的负热点,前向散射方向的NDV...     

Forward and inverse modelling of canopy directional reflectance using a neural network

This article explores the use of artificial neural networks for both forward and inverse canopy modelling. The forward neural modelling paradigm involved training a network for predicting the bidirectional reflectance distribution function (BRDF) of a canopy given the density of the trees, their height, crown shape, viewing, and illumination geometry. The neural network model was able to predict the BRDF of unseen canopy sites with 90% accuracy. Analysis of the signal captured by the model indicates that the canopy structural parameters, and illumination and viewing geometry, are essential for predicting the BRDF of vegetated surfaces. The inverse neural network model involved learning the underlying relationship between canopy structural parameters and their corresponding bidirectional reflectance. The inversion results show that the R2 between the network predicted canopy parameters and the actual canopy parameters was 0.85 for density and 0.75 for both the crown shape and the height parameters. The results of both forward and inverse modelling suggest that neural networks can model accurately the BRDF of vegetated canopies.     

Analysis of POLDER-ADEOS data for the Australian continent: The relationship between BRDF and vegetation structure

We analysed the POLDER-ADEOS level 3 dataset (coefficients of a bi-directional reflectance distribution (BRDF) model fitted to reflectances) to interpret the spatial and temporal patterns of BRDF for the Australian continent in terms of landscape attributes. Significant noise was identified and systematically removed by transparent and mechanistically sound filtering and interpolation. We investigated the hypotheses that the spatial variation of the Roujean model coefficients over the continent is greater than the temporal variation over the eight month time series and that vegetation structure has more influence on the BRDF than does landform. We found systematic, interpretable relationships between the Roujean model coefficients and structural attributes for both woody and grassy vegetation. For woody vegetation, the model coefficients were closely related to the spacing to height ratio of vegetation elements. The Roujean model coefficients were generalized to ten types by an unsupervised classification which produced coherent spatial patterns. While these could be related to vegetation types, they indicated that there were other factors operating. Finally, the magnitude of the BRDF normalization of AVHRR to nadir viewing and fixed solar zenith angle was investigated for the continent under summer and winter illumination conditions. The magnitude of normalization was small during summer, but was as large as 100% at the winter solstice.     

Canopy attributes of desert grassland and transition communities derived from multiangular airborne imagery

The surface bidirectional reflectance distribution function (BRDF) contains valuable information on canopy physiognomy for desert grassland and grass-shrub transition communities. This information may be accessed by inverting a BRDF model against sets of observations, which encompass important variations in viewing and illumination angles. This paper shows that structural canopy attributes can be derived through inversion of the Simple Geometric Model (SGM) of the BRDF developed in this paper. It is difficult to sample BRDF features from the ground because of the discontinuous nature of the canopies and long intrinsic length scales in remotely sensed spectral measures (>10 m). A multispectral digital camera was therefore used to derive spatial multiangular reflectance data sets from the air and the SGM was validated against and inverted with these. It was also validated using 3-D radiosity simulations driven with maps of field-measured plant dimensions. The interpretation of the retrieved parameter maps (shrub density, shrub width and canopy height) reveals variations in canopy structure within desert grassland and grassland-shrubland transition communities, which are clearly related to structural and optical features in high resolution panchromatic and vegetation index images. To our knowledge, this paper reports on the first attempts to acquire structural canopy attributes of desert landscapes using multiple view angle data at scales less than 1 km. The results point to further opportunities to exploit multiangular data from spaceborne sensors such as the Multiangle Imaging SpectroRadiometer (MISR) and the Compact High Resolution Imaging Spectrometer (CHRIS) on the NASA Terra and European Space Agency's PROBA satellites, respectively.     

A method to convert AVHRR Normalized Difference Vegetation Index time series to a standard viewing and illumination geometry

The bi-directional reflectance distribution function (BRDF) alters the seasonal and inter-annual variations exhibited in Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) data and this hampers the detection and, consequently, the interpretation of temporal variations in land-surface vegetation. The magnitude and sign of bi-directional effects in commonly used AVHRR data sets depend on land-surface properties, atmospheric composition and the type of atmospheric correction that is applied to the data. We develop an approach to estimate BRDF effects in AVHRR NDVI time series using the Moderate Resolution Imaging Spectrometer (MODIS) BRDF kernels and subsequently adjust NDVI time series to a standard illumination and viewing geometry. The approach is tested on NDVI time series that are simulated for representative AVHRR viewing and illumination geometry. These time series are simulated with a canopy radiative transfer model coupled to an atmospheric radiative transfer model for four different land cover types—tropical forest, boreal forest, temperate forest and grassland - and five different atmospheric conditions - turbid and clear top-of-atmosphere, turbid and clear top-of-atmosphere with a correction for ozone absorption and Rayleigh scattering applied (Pathfinder AVHRR Land data) and ground-observations (fully corrected for atmospheric effects). The simulations indicate that the timing of key phenological stages, such as start and end of growing season and time of maximum greenness, is affected by BRDF effects. Moreover, BRDF effects vary with latitude and season and increase over the time of operation of subsequent NOAA satellites because of orbital drift. Application of the MODIS kernels on simulated NVDI data results in a 50% to 85% reduction of BRDF effects. When applied to the global 18-year global Normalized Difference Vegetation Index (NDVI) Pathfinder data we find BRDF effects similar in magnitude to those in the simulations. Our analysis of the global data shows that BRDF effects are especially large in high latitudes; here we find that in at least 20% of the data BRDF errors are too large for accurate detection of seasonal and interannual variability. These large BRDF errors tend to compensate, however, when averaged over latitude.     

Robust estimation of BRDF model parameters

The effect of the Bidirectional Reflectance Distribution Function (BRDF) is one of the most important factors in correcting the reflectance obtained from remotely sensed data. Estimation of BRDF model parameters can be deteriorated by various factors; contamination of the observations by undetected subresolution clouds or snow patches, inconsistent atmospheric correction in multiangular time series due to uncertainties in the atmospheric parameters, slight variations of the surface condition during a period of observation, for example due to soil moisture changes, diurnal effects on vegetation structure, and geolocation errors [Lucht and Roujean, 2000]. In the present paper, parameter estimation robustness is examined using Bidirectional Reflectance Factor (BRF) data measured for paddy fields in Japan. We compare both the M-estimator and the least median of squares (LMedS) methods for robust parameter estimation to the ordinary least squares method (LSM). In experiments, simulated data that were produced by adding noises to the data measured on the ground surface were used. Experimental results demonstrate that if a robust estimation is sought, the LMedS method can be adopted for the robust estimation of a BRDF model parameter.     

Models for surface reflection of radiance and polarized radiance: Comparison with airborne multi-angle photopolarimetric measurements and implications for modeling top-of-atmosphere measurements

In this paper, we investigate the surface-atmosphere radiative interaction in application to the problem of aerosol satellite remote sensing over land. First, we test different models of the Bidirectional Reflectance and Polarization Distribution Function (BRDF and BPDF) for bare soil and vegetation surfaces using multi-angle, multi-spectral photopolarimetric airborne measurements of the Research Scanning Polarimeter (RSP). Then, we investigate the performance of different models of BRDF and BPDF for modeling top-of-atmosphere measurements. We have found that different BRDF models can describe the RSP measurements equally well. However, for soil surfaces, the different BRDF models show a different dependence on illumination geometry (solar zenith and azimuth angles), as well as a different dependence on viewing angle outside the range of RSP measurements. This implies that different models describe the surface-atmosphere interaction differently, leading for soil surfaces to differences in the top-of-atmosphere reflectance up to 4-5%, whereas at surface level the models agree within 2% for RSP illumination and measurement geometry. For vegetation, the different BRDF models show more similar dependence on illumination geometry, meaning that, in general, the differences in top-of-atmosphere reflectances are smaller than the differences in surface total reflectances. For the BPDF, we compare the empirical model of Nadal and Breon (1999) and the model developed by Maignan et al. (2009) with a newly developed model. The latter model compares better with RSP measurements. It was shown that, though all models have essentially different angular profiles at different illumination and viewing geometries, the difference of the top-of-atmosphere degree of linear polarization is less or is of the same order as the degree of linear polarization difference at the surface level taken at RSP illumination and measurement geometry. For the considered models, it can be up to 0.015 but is mostly below 0.005.     

A Composite BRDF Model for Hazy Gloss

We introduce a bidirectional reflectance distribution function (BRDF) model for the rendering of materials that exhibit hazy reflections, whereby the specular reflections appear to be flanked by a surrounding halo. The focus of this work is on artistic control and ease of implementation for real‐time and off‐line rendering. We propose relying on a composite material based on a pair of arbitrary BRDF models; however, instead of controlling their physical parameters, we expose perceptual parameters inspired by visual experiments [ VBF17 ]. Our main contribution then consists in a mapping from perceptual to physical parameters that ensures the resulting composite BRDF is valid in terms of reciprocity, positivity and energy conservation. The immediate benefit of our approach is to provide direct artistic control over both the intensity and extent of the haze effect, which is not only necessary for editing purposes, but also essential to vary haziness spatially over an object surface. Our solution is also simple to implement as it requires no new importance sampling strategy and relies on existing BRDF models. Such a simplicity is key to approximating the method for the editing of hazy gloss in real‐time and for compositing.     

Theoretical noise sensitivity of BRDF and albedo retrieval from the EOS-MODIS and MISR sensors with respect to angular sampling

The sensitivity of the semiempirical RossThick-LiSparse Ambrals BRDF model to random noise in observed multiangular reflectances was investigated through a study of the impact of angular sampling. The mathematical properties of (linear, additive) kernel-driven BRDF models allow the analytical derivation of so-called weights of determination or noise amplification factors which quantify the uncertainty in retrieved parameters such as nadir-view reflectance or albedo at various solar zenith angles, or in the BRDF model parameters themselves. The study was carried out using simulated angular sampling for the MODIS and MISR instruments to be flown on NASA's Earth Observing System AM-1 platform, as a function of latitude, day of year and sampling period. A similar study was carried out for comparison using the modified RPV BRDF model, a multiplicative model. Results show that the retrieved parameters, reflectance and albedo can be expected to have noise amplification factors that are less than unity, indicating that the retrievals are stable with respect to random noise under the angular sampling schemes occurring. The BRDF model parameters themselves were found to be more susceptible to noise than many of the derived products, especially for the modified RPV model. The effect of different angular sampling regimes on the uncertainty of derived information was further explored. This study provides an indication of the reliability to be expected from the operational BRDF/albedo products from the MODIS and MISR instruments. The findings may qualitatively also apply to AVHRR, SPOT VEGETATION and similar satellite angular sampling regimes.     

Variability of biome reflectance directional signatures as seen by POLDER

Reflectance measurements acquired with the spaceborne POLDER instrument are used to analyze the variability of land surface directional signatures as a function of vegetation cover type. The reflectance directional signatures are quantified by the three parameters of a modified version of the Ross-Li reflectance model. The variability of the estimated parameters with respect to the seven MODIS biome classes was found to be higher within the classes than between classes, with the exception of the desert targets that show more isotropic reflectances. A limited number of standard BRDFs (typically 5 in the red and near infrared) capture most of the variability of the directional reflectance measurements, supporting the idea that different land surfaces have similar directional signatures. Over vegetation targets, they are characterized by a strong increase toward backscattering and much smaller variations in forward directions. The results express the diversity in structural situations within a given biome class and indicate that, at the resolution of the POLDER sensor, i.e. a few kilometers, the BRDF contains little information on the dominant vegetation type. We show that standard directional signatures may be used to correct the reflectance measurements for directional effects with an RMS error on the order of 0.011 in the red and 0.015 in the near infrared.     

Variability in surface BRDF at different spatial scales (30 m-500 m) over a mixed agricultural landscape as retrieved from airborne and satellite spectral measurements

Over the past decade, the role of multiangle remote sensing has been central to the development of algorithms for the retrieval of global land surface properties including models of the bidirectional reflectance distribution function (BRDF), albedo, land cover/dynamics, burned area extent, as well as other key surface biophysical quantities impacted by the anisotropic reflectance characteristics of vegetation. In this study, a new retrieval strategy for fine-to-moderate resolution multiangle observations was developed, based on the operational sequence used to retrieve the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 5 reflectance and BRDF/albedo products. The algorithm makes use of a semiempirical kernel-driven bidirectional reflectance model to provide estimates of intrinsic albedo (i.e., directional-hemispherical reflectance and bihemispherical reflectance), model parameters describing the BRDF, and extensive quality assurance information. The new retrieval strategy was applied to NASA's Cloud Absorption Radiometer (CAR) data acquired during the 2007 Cloud and Land Surface Interaction Campaign (CLASIC) over the well-instrumented Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) Cloud and Radiation Testbed (CART) site in Oklahoma, USA. For the case analyzed, we obtained ~ 1.6 million individual surface bidirectional reflectance factor (BRF) retrievals, from nadir to 75° off-nadir, and at spatial resolutions ranging from 3 m to 500 m. This unique dataset was used to examine the interaction of the spatial and angular characteristics of a mixed agricultural landscape; and provided the basis for detailed assessments of: (1) the use of a land cover type-specific a priori knowledge in kernel-driven BRDF model inversions; (2) the interaction between surface reflectance anisotropy and instrument spatial resolution; and (3) the uncertainties that arise when sub-pixel differences in the BRDF are aggregated to a moderate resolution satellite pixel. Results offer empirical evidence concerning the influence of scale and spatial heterogeneity in kernel-driven BRDF models; providing potential new insights into the behavior and characteristics of different surface radiative properties related to land/use cover change and vegetation structure.     

Barycentric parameterizations for isotropic BRDFs

A bidirectional reflectance distribution function (BRDF) is often expressed as a function of four real variables: two spherical coordinates in each of the "incoming" and "outgoing" directions. However, many BRDFs reduce to functions of fewer variables. For example, isotropic reflection can be represented by a function of three variables. Some BRDF models can be reduced further. In This work, we introduce new sets of coordinates which we use to reduce the dimensionality of several well-known analytic BRDFs as well as empirically measured BRDF data. The proposed coordinate systems are barycentric with respect to a triangular support with a direct physical interpretation. One coordinate set is based on the BRDF mode) proposed by Lafortune. Another set, based on a model of Ward, is associated with the "halfway" vector common in analytical BRDF formulas. Through these coordinate sets we establish lower bounds on the approximation error inherent in the models on which they are based. We present a third set of coordinates, not based on any analytical model, that performs well in approximating measured data. Finally, our proposed variables suggest novel ways of constructing and visualizing BRDFs.     

Estimates of surface soil moisture under grass covers using L-band radiometry

The scope of this study is to establish the parameters of the L-band (1.4 GHz) Microwave Emission of the Biosphere model (L-MEB) for grass covers, and to assess surface soil moisture retrievals in areas covered by grass. L-MEB parameters are key ancillary information for the Soil Moisture and Ocean Salinity mission (SMOS) retrieval algorithm that produces estimates of the surface soil moisture from measurements of the surface brightness temperature at L-band.L-band data sets from three ground-based experiments over grass are analysed in this paper: BARC (orchard grass and alfalfa), ELBARA-ETH (clover grass), and SMOSREX (grass and litter from a field left fallow). Modelling of the brightness temperature using the zero-th order radiative transfer model in L-MEB indicates that the vegetation appears isotropic to microwaves propagating with horizontal polarisation, and that the single scattering albedo can be neglected. At vertical polarisation, non-zero scattering is observed for all the grass data sets. Surface soil moisture is retrieved with enough accuracy for all data sets as long as the soil and litter emission are calibrated beforehand. Then surface soil moisture and vegetation optical depth can be left as free parameters in the retrieval process. Finally, the study highlights the importance of detecting strong emission and attenuation by wet vegetation and litter due to rainfall interception in order to obtain accurate estimates of the surface soil moisture. The study illustrates how strong rainfall interception can be flagged straightforwardly using a microwave polarisation index.     

Bessel函数测距模型的RSSI测距方法

针对现有RSSI测距方法中,影响测距精度的RSSI测量值难以准确估计和RSSI值与距离对应衰减关系不明确的问题,给出一种基于Bessel函数测距模型的RSSI测距方法。首先对RSSI原始测量数据进行异常值剔除,滤波和凸优化提取趋势项的预处理,然后建立基于Bessel函数的测距模型,基于预处理所得光滑数据,利用最小二乘法辨识测距模型中未知参数,从而得到具体测距模型表达式。基于实测数据对所提方法进行实验验证,与Shadowing模型、分段函数测距模型对比,结果表明,Bessel函数测距模型的RSSI误差均值在1.8dBm范围以内,能更有效反映RSSI值衰减关系,提高了测距精度且计算开销不大。     

基于ZigBee室内定位算法

鉴于ZigBee技术适用于数据采集系统的特点,提出了一种基于ZigBee的室内无线定位系统解决方法.传统的基于ZigBee定位采用无线信号损耗模型,受周围环境的影响比较大,现实模型根据经验值设置参数,定位精度不高;在此基础上采用曲线拟合方法拟合RSSI与距离之间的关系,采用RSSI三角形质心算法计算待测节点坐标.实验结果表明该算法提高了定位精度.