Polarized reflectances of natural surfaces: Spaceborne measurements and analytical modeling

The PARASOL instrument provides polarization measurements of the Earth's reflectance. Data processing of these measurements leads to a large and representative dataset of Bidirectional Polarization Distribution Functions (BPDF) for a wide range of surface cover. All surfaces show a similar pattern of the polarized reflectances, with very little polarization at backscattering and a general increase with the phase angle. The largest polarized reflectances are observed facing the sun, with large sun and view angles and amount to 0.02-0.04 depending on the surface type. Simple BPDF models available in the literature predict the correct order of magnitude as well as the main features of its directional signatures. However, these models also show significant biases for some viewing geometries. We propose a new model for the BPDF of natural surfaces, based on theoretical development as well as empirical fit to the PARASOL measurements. This linear model with only one free parameter allows a similar fit to the measurements as a previously published one (Nadal and Bréon, 1999), non-linear with two parameters. Because the BPDF of natural surfaces appears to vary very little, a fixed model (i.e. with a priori biome-specific values for the parameter) is defined and may be sufficient for most applications.     

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.     

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.     

Effect of surface roughness,wavelength, illumination,and viewing zenith angles on soil surface BRDF using an imaging BRDF approach

Compared to non-imaging instruments, imaging spectrometers (ISs) can provide detailed information to investigate the influence of scene components on the bidirectional reflectance distribution function (BRDF) of a mixed target. The research reported in this article investigated soil surface reflectance changes as a function of scene components (i.e. illuminated pixels and shaded pixels), illumination and viewing zenith angles, and wavelength. Image-based BRDF data of both rough and smooth soil surfaces were acquired in a laboratory setting at three different illumination zenith angles and at four different viewing zenith angles over the full 360° azimuth range, at an interval of 20°, using a Specim V10E IS (Specim, Spectral Imaging Ltd., Oulu, Finland) mounted on the University of Lethbridge Goniometer System version 2.5 (ULGS-2.5). The BRDF of the smooth soil surface was dominated by illuminated pixels, whereas the shaded pixels were a larger component of the BRDF of the rough soil surface. As the illumination zenith angle was changed from 60° to 45° and then to 30°, the shadowing effect decreased, regardless of the soil surface. Soil surface reflectance was generally higher at the backscattering view zenith angles and decreased continuously to forward scattering view zenith angles in the light principal plane, regardless of the wavelength, due to the Specim V10E IS seeing more illuminated pixels in the backscattering angles than in the forward scattering angles. Higher soil surface reflectance was observed at higher illumination and viewing zenith angle combinations. For both soil surface roughness categories, the BRDF exhibited a greater range of values in the near-infrared than at the visible wavelengths. This research enhances our understanding of soil BRDF for various soil roughness and illumination conditions.     

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.     

Retrieval of surface directional reflectance properties using ground level multiangle measurements

Knowledge of the directional reflectance properties of natural surfaces such as soils and vegetation canopies is essential for classification studies and canopy model inversion. Atmospheric correction schemes, using various levels of approximation, are described to retrieve surface bidirectional reflectance factors (BRFs) and directionalhemispherical reflectances (albedos) from multiangle radiance measurements taken at ground level. The retrieval schemes are tested on simulated data incorporating realistic surface BRFs and atmospheric models containing aerosols. Sensitivity of the atmospherically corrected BRFs and associated directional-hemispherical reflectances to various aerosol properties and the sun-view geometry is illustrated. A measurement strategy for obtaining highly accurate surface reflectance properties also is examined in the context of instrument radiometric calibration, knowledge of the atmospheric properties, and sun-view angular coverage.     

Coupled soil-leaf-canopy and atmosphere radiative transfer modeling to simulate hyperspectral multi-angular surface reflectance and TOA radiance data

Coupling radiative transfer models for the soil background and vegetation canopy layers is facilitated by means of the four-stream flux interaction concept and use of the adding method. Also the coupling to a state-of-the-art atmospheric radiative transfer model like MODTRAN4 can be established in this way, thus enabling the realistic simulation of top-of-atmosphere radiances detected by space-borne remote sensing instruments. Possible applications of coupled modeling vary from mission design to parameter retrieval and data assimilation. This paper introduces a modified Hapke soil BRDF model, a robust version of the PROSPECT leaf model, and a modernized canopy radiative transfer model called 4SAIL2. The latter is a hybrid two-layer version of SAIL accommodating horizontal and vertical heterogeneities, featuring improved modeling of the hot spot effect and output of canopy absorptances. The integrated model is simply called SLC (soil-leaf-canopy) and has been implemented as a speed-optimized Windows DLL which allows efficient use of computer resources even when simulating massive amounts of hyperspectral multi-angular observations. In this paper various examples of possible model output are shown, including simulated satellite image products. First validation results have been obtained from atmospherically corrected hyperspectral multi-angular CHRIS-PROBA data of the Upper Rhine Valley in Germany.     

Perceptually Motivated BRDF Comparison using Single Image

Surface reflectance of real‐world materials is now widely represented by the bidirectional reflectance distribution function (BRDF) and also by spatially varying representations such as SVBRDF and the bidirectional texture function (BTF). The raw surface reflectance measurements are typically compressed or fitted by analytical models, that always introduce a certain loss of accuracy. For its evaluation we need a distance function between a reference surface reflectance and its approximate version. Although some of the past techniques tried to reflect the perceptual sensitivity of human vision, they have neither optimized illumination and viewing conditions nor surface shape. In this paper, we suggest a new image‐based methodology for comparing different anisotropic BRDFs. We use optimization techniques to generate a novel surface which has extensive coverage of incoming and outgoing light directions, while preserving its features and frequencies that are important for material appearance judgments. A single rendered image of such a surface along with simultaneously optimized lighting and viewing directions leads to the computation of a meaningful BRDF difference, by means of standard image difference predictors. A psychophysical experiments revealed that our surface provides richer information on material properties than the standard surfaces often used in computer graphics, e.g., sphere or blob.     

Investigating the accuracy of vegetation index-based models for estimating the fractional vegetation cover and the effects of varying soil backgrounds using in situ measurements and the PROSAIL model

Fractional vegetation cover (FVC) is an important variable for describing the quality and changes of vegetation in terrestrial ecosystems. The simplest and most widely used model for the estimation of FVC is the dimidiate pixel model. The normalized difference vegetation index (NDVI) is commonly used as a vegetation index (VI) in this model. A range of VIs is possible alternative to the use of NDVI in the dimidiate pixel model. In this article, six VI-based dimidiate pixel models were compared using in situ measurements and canopy reflectances simulated by the PROSAIL model over nine different soil backgrounds. A comparison with in situ measurements showed that the Gutman–Ignatov method overestimated FVC, with a mean root mean square error (RMSE) of 0.14. The mean RMSE had an intermediate value of 0.08 in the Carlson–Ripley method and was further reduced to 0.05 in the method proposed by Baret et al. The use of both modified soil-adjusted vegetation index (MSAVI) and a mixture of NDVI and the ratio vegetation index (RVI) to replace NDVI in the Gutman–Ignatov model reduced the RMSE to 0.06. The mean RMSE in the difference vegetation index (DVI)-based model was 0.08. The simulated results indicated that soil backgrounds have significant effects on these VI-based models. The sensitivity of the first three models and the NDVI plus RVI-based model to soil backgrounds decreased with an increase in soil reflectance. In contrast, the DVI-based model is sensitive to soil backgrounds with high reflectances. MSAVI, which is less sensitive to soil backgrounds, represents a feasible alternative to the use of NDVI in the Gutman–Ignatov model.     

Assessing viewing and illumination geometry effects on the MODIS vegetation index (MOD13Q1) time series: implications for monitoring phenology and disturbances in forest communities in Queensland,Australia

Time series analysis of satellite data can be used to monitor temporal dynamics of forested environments, thus providing spatial data for a range of forest science, monitoring and management issues. The moderate resolution imaging spectroradiometer (MODIS) vegetation index (MOD13Q1) product has potential for monitoring forest dynamics and disturbances. However, the suitability of the product to accurately measure temporal changes due to phenology and disturbances is questionable as the effects of variable solar and viewing geometry have not been removed from these data. This study aimed to examine the impact that viewing and illumination geometry differences had on MOD13Q1 vegetation index values, and their subsequent ability to map changes arising from phenology and disturbances in a number of forest communities in Queensland, Australia. MOD13Q1 normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were compared to normalized NDVI and EVI (NDVI_normalized and EVI_normalized), which were derived from the reflectance modelled from a bidirectional reflectance distribution function (BRDF)/albedo parameters product (MCD43A1) using fixed viewing and illumination geometry. Time series plots of the vegetation index values from a number of pixels representing different forest types and known disturbances showed that the NDVI_normalized time series was more effective at capturing the changes in vegetation than the NDVI. MOD13Q1 NDVI showed higher seasonal amplitude, but was less accurate at capturing phenology and disturbances compared to the NDVI_normalized. The EVI was less affected by variable viewing and illumination geometry in terms of amplitude, but was affected in terms of time shift in periodicities providing erroneous information on phenology. More studies in different environments with appropriate vegetation phenology reference data will be needed to confirm these observations.