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The versatility of digital imaging and remote sensing image generation (DIRSIG) lies in user control. Within a single simulation, each link of the image chain can be modeled with precision. The input of the chain is a uniquely defined scene and irradiance level. The output is a radiance image produced by a virtual sensor. Links of the chain include the light source, radiation propagation, target geometry, atmosphere, and the sensor. Parameters of each link are defined prior to simulation. Since the model is compartmentalized, scenarios can be changed with precision and with ease.
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Soil reflectance signatures were modeled using the digital imaging and remote sensing image generation model and Blender three-dimensional (3-D) graphic design software. Using these tools, the geometry, radiometry, and chemistry of quartz and magnetite were exploited to model the presence of particle size and porosity effects in the visible and the...
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... radiometric aspects of this simulation are solved using the DIRSIG model. DIRSIG is a first-principles ray- tracing model that outputs detected at-sensor radiance. Light sources, scene geometries, and sensor configurations are all defined by the user (Fig. 1). This model has been pre- dominantly used for the analysis and modeling of sensors. DIRSIG allows for direct system comparisons. A single scene can be observed under varying atmospheric condi- tions, with multimodal sensing techniques. Though it is easy to conceptualize passive remote sensing as light rays travel- ing from source to target to sensor, DIRSIG models radiation in inverse fashion. Rays are initially cast from individual pix- els of a user-defined focal plane array. These rays determine the area observed at each pixel, and where incident radiation ...
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... bimodal particle size distribution was used to evalu- ate the effects of density for soil samples that contained a mixture of quartz and magnetite particles. Within the Blender 3-D sample, small particles (106 and 150 μm) were given the spectral signature of magnetite. All particles larger than 150 μm were represented as pure quartz. This distribu- tion was used because it closely resembled the distribution observed by Bachmann et al. 11 In that paper, soil that was denser was observed to reflect less than sand with higher levels of porosity. It was assumed that this result was a con- sequence of small black magnetite particles that more com- pletely fill pore spaces when soil is dense. To test this, a DIRSIG simulation was created using the scene described above. The illumination source was positioned 20 deg from nadir in the zenith axis. BRDF values were calculated in the visible and shortwave infrared (SWIR). The reflectance characteristics of the mixed soil are compared with pure quartz in Fig. ...
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... decrease in the reflectance of soil containing small magnetite grains. A much smaller reflectance gradient exists in Figs. 10(d) to 10(f), which describe a low-density sample. Bachmann et al. also observed there to be greater variance in measurements collected in the SWIR. The spectral reflectance of quartz contrasts more with the reflectance of magnetite in this regime. 11 This trend is observed in the simulation results displayed in Fig. 10. All plots in this figure include standard deviation error bars, which were calculated using the reflec- tance simulation of five separate geometric representations of the respective high-and low-density scenes. Standard deviation is larger in the low-density scene. Not only is the reflectance gradient in the 450, 1000, and 1915 nm bands less distinct for the less-dense scene, there is less certainty in the results. This is a product of transient porosity features in the five different simulations of the low-density scene. There was less change in porosity between each representation of the high-density scene. BRDF change with respect to wavelength can be seen in Fig. 11, where the 450, 868, 1000, and 1915 nm bands are plotted together for low-density [ Fig. 11(a)] and high- density [ Fig. 11(b)] scenarios. For a bimodal distribution, spectral contrast due to density has been observed to increase as phase angle increases. 11 The DIRSIG model presented in this work also predicts this ten- dency. Figure 12 shows that the increase in contrast of a quartz and magnetite mixture occurs at visible and SWIR wavelengths. As expected, contrast is greater in the SWIR and increases with larger phase angles. This simulation did not consider coherent scatter beyond that which was cap- tured in the reflectance measurements of the USGS. There- fore, the trend of increasing contrast that was observed in Ref. 11 can be at least partially attributed to the intricacies of soil geometry that are associated with pore spacing, par- ticle size, and surface ...
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... decrease in the reflectance of soil containing small magnetite grains. A much smaller reflectance gradient exists in Figs. 10(d) to 10(f), which describe a low-density sample. Bachmann et al. also observed there to be greater variance in measurements collected in the SWIR. The spectral reflectance of quartz contrasts more with the reflectance of magnetite in this regime. 11 This trend is observed in the simulation results displayed in Fig. 10. All plots in this figure include standard deviation error bars, which were calculated using the reflec- tance simulation of five separate geometric representations of the respective high-and low-density scenes. Standard deviation is larger in the low-density scene. Not only is the reflectance gradient in the 450, 1000, and 1915 nm bands less distinct for the less-dense scene, there is less certainty in the results. This is a product of transient porosity features in the five different simulations of the low-density scene. There was less change in porosity between each representation of the high-density scene. BRDF change with respect to wavelength can be seen in Fig. 11, where the 450, 868, 1000, and 1915 nm bands are plotted together for low-density [ Fig. 11(a)] and high- density [ Fig. 11(b)] scenarios. For a bimodal distribution, spectral contrast due to density has been observed to increase as phase angle increases. 11 The DIRSIG model presented in this work also predicts this ten- dency. Figure 12 shows that the increase in contrast of a quartz and magnetite mixture occurs at visible and SWIR wavelengths. As expected, contrast is greater in the SWIR and increases with larger phase angles. This simulation did not consider coherent scatter beyond that which was cap- tured in the reflectance measurements of the USGS. There- fore, the trend of increasing contrast that was observed in Ref. 11 can be at least partially attributed to the intricacies of soil geometry that are associated with pore spacing, par- ticle size, and surface ...
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... decrease in the reflectance of soil containing small magnetite grains. A much smaller reflectance gradient exists in Figs. 10(d) to 10(f), which describe a low-density sample. Bachmann et al. also observed there to be greater variance in measurements collected in the SWIR. The spectral reflectance of quartz contrasts more with the reflectance of magnetite in this regime. 11 This trend is observed in the simulation results displayed in Fig. 10. All plots in this figure include standard deviation error bars, which were calculated using the reflec- tance simulation of five separate geometric representations of the respective high-and low-density scenes. Standard deviation is larger in the low-density scene. Not only is the reflectance gradient in the 450, 1000, and 1915 nm bands less distinct for the less-dense scene, there is less certainty in the results. This is a product of transient porosity features in the five different simulations of the low-density scene. There was less change in porosity between each representation of the high-density scene. BRDF change with respect to wavelength can be seen in Fig. 11, where the 450, 868, 1000, and 1915 nm bands are plotted together for low-density [ Fig. 11(a)] and high- density [ Fig. 11(b)] scenarios. For a bimodal distribution, spectral contrast due to density has been observed to increase as phase angle increases. 11 The DIRSIG model presented in this work also predicts this ten- dency. Figure 12 shows that the increase in contrast of a quartz and magnetite mixture occurs at visible and SWIR wavelengths. As expected, contrast is greater in the SWIR and increases with larger phase angles. This simulation did not consider coherent scatter beyond that which was cap- tured in the reflectance measurements of the USGS. There- fore, the trend of increasing contrast that was observed in Ref. 11 can be at least partially attributed to the intricacies of soil geometry that are associated with pore spacing, par- ticle size, and surface ...
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... decrease in the reflectance of soil containing small magnetite grains. A much smaller reflectance gradient exists in Figs. 10(d) to 10(f), which describe a low-density sample. Bachmann et al. also observed there to be greater variance in measurements collected in the SWIR. The spectral reflectance of quartz contrasts more with the reflectance of magnetite in this regime. 11 This trend is observed in the simulation results displayed in Fig. 10. All plots in this figure include standard deviation error bars, which were calculated using the reflec- tance simulation of five separate geometric representations of the respective high-and low-density scenes. Standard deviation is larger in the low-density scene. Not only is the reflectance gradient in the 450, 1000, and 1915 nm bands less distinct for the less-dense scene, there is less certainty in the results. This is a product of transient porosity features in the five different simulations of the low-density scene. There was less change in porosity between each representation of the high-density scene. BRDF change with respect to wavelength can be seen in Fig. 11, where the 450, 868, 1000, and 1915 nm bands are plotted together for low-density [ Fig. 11(a)] and high- density [ Fig. 11(b)] scenarios. For a bimodal distribution, spectral contrast due to density has been observed to increase as phase angle increases. 11 The DIRSIG model presented in this work also predicts this ten- dency. Figure 12 shows that the increase in contrast of a quartz and magnetite mixture occurs at visible and SWIR wavelengths. As expected, contrast is greater in the SWIR and increases with larger phase angles. This simulation did not consider coherent scatter beyond that which was cap- tured in the reflectance measurements of the USGS. There- fore, the trend of increasing contrast that was observed in Ref. 11 can be at least partially attributed to the intricacies of soil geometry that are associated with pore spacing, par- ticle size, and surface ...
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... decrease in the reflectance of soil containing small magnetite grains. A much smaller reflectance gradient exists in Figs. 10(d) to 10(f), which describe a low-density sample. Bachmann et al. also observed there to be greater variance in measurements collected in the SWIR. The spectral reflectance of quartz contrasts more with the reflectance of magnetite in this regime. 11 This trend is observed in the simulation results displayed in Fig. 10. All plots in this figure include standard deviation error bars, which were calculated using the reflec- tance simulation of five separate geometric representations of the respective high-and low-density scenes. Standard deviation is larger in the low-density scene. Not only is the reflectance gradient in the 450, 1000, and 1915 nm bands less distinct for the less-dense scene, there is less certainty in the results. This is a product of transient porosity features in the five different simulations of the low-density scene. There was less change in porosity between each representation of the high-density scene. BRDF change with respect to wavelength can be seen in Fig. 11, where the 450, 868, 1000, and 1915 nm bands are plotted together for low-density [ Fig. 11(a)] and high- density [ Fig. 11(b)] scenarios. For a bimodal distribution, spectral contrast due to density has been observed to increase as phase angle increases. 11 The DIRSIG model presented in this work also predicts this ten- dency. Figure 12 shows that the increase in contrast of a quartz and magnetite mixture occurs at visible and SWIR wavelengths. As expected, contrast is greater in the SWIR and increases with larger phase angles. This simulation did not consider coherent scatter beyond that which was cap- tured in the reflectance measurements of the USGS. There- fore, the trend of increasing contrast that was observed in Ref. 11 can be at least partially attributed to the intricacies of soil geometry that are associated with pore spacing, par- ticle size, and surface ...
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... DIRSIG-based model shows that the results observed by Bachmann et al. may have been partially caused by the particle size distribution of the sample soil. The plots in Fig. 10 also confirm the notion that the effects of magnetite are more pronounced in high-density samples. Figures 10(a) to 10(c) correspond to a high-density soil sample and reveal a In the high-density plots (a-c), the impact of intimate mixing between magnetite and quartz was defined by a noticeable drop in reflectance at all viewing angles. There was very little variance between the BRDF of mixed soil and pure quartz in the low-density scenario ...
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... DIRSIG-based model shows that the results observed by Bachmann et al. may have been partially caused by the particle size distribution of the sample soil. The plots in Fig. 10 also confirm the notion that the effects of magnetite are more pronounced in high-density samples. Figures 10(a) to 10(c) correspond to a high-density soil sample and reveal a In the high-density plots (a-c), the impact of intimate mixing between magnetite and quartz was defined by a noticeable drop in reflectance at all viewing angles. There was very little variance between the BRDF of mixed soil and pure quartz in the low-density scenario ...
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... the degree of contrast in reflectance signature that increases with phase angle was linked to sample geom- etry. Models affirmed that the univariant signature of reflec- tance is built upon an interdependent trade space of several geometric variables. The influence of realistic particle geom- etry needs to be explored in greater depth if functional mod- els are to be accurately developed for soils and mixed solids. It is the ability to focus on geometric modeling that separates this technique from other models. Because the use of Blender 3-D and DIRSIG provides complete user control of sample geometry and the assignment of spectral properties, it serves as a convenient test-bed for target construction and target signature sensing. This technique can be easily modified for implementation with other material mixtures provided that pure spectral reflectance or emissivity data is available. Ultimately, this study demonstrated that by combining Fig. 11 Principal plane BRDF of bimodal quartz and a bimodal quartz/magnetite mixture is plotted at 450, 868, 1000, and 1915 nm for low-density (a) and higher density (b) scenarios. As observed by Ref. 11, the difference in BRDF between the pure and mixed targets increases when density is high. Fig. 12 Variance between the reflectance of high-density soil and low-density soil for a bimodal distribution of magnetite and quartz was shown to increase as phase angle increased. The effect was more dramatic in the shortwave infrared (SWIR). These effects have been observed in previous lab analysis. 11 realistic target geometry and spectral measurements of pure quartz and magnetite, effects of soil particle size, density, and texture could be modeled without functional data fitting or rigorous analysis of material ...
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... the degree of contrast in reflectance signature that increases with phase angle was linked to sample geom- etry. Models affirmed that the univariant signature of reflec- tance is built upon an interdependent trade space of several geometric variables. The influence of realistic particle geom- etry needs to be explored in greater depth if functional mod- els are to be accurately developed for soils and mixed solids. It is the ability to focus on geometric modeling that separates this technique from other models. Because the use of Blender 3-D and DIRSIG provides complete user control of sample geometry and the assignment of spectral properties, it serves as a convenient test-bed for target construction and target signature sensing. This technique can be easily modified for implementation with other material mixtures provided that pure spectral reflectance or emissivity data is available. Ultimately, this study demonstrated that by combining Fig. 11 Principal plane BRDF of bimodal quartz and a bimodal quartz/magnetite mixture is plotted at 450, 868, 1000, and 1915 nm for low-density (a) and higher density (b) scenarios. As observed by Ref. 11, the difference in BRDF between the pure and mixed targets increases when density is high. Fig. 12 Variance between the reflectance of high-density soil and low-density soil for a bimodal distribution of magnetite and quartz was shown to increase as phase angle increased. The effect was more dramatic in the shortwave infrared (SWIR). These effects have been observed in previous lab analysis. 11 realistic target geometry and spectral measurements of pure quartz and magnetite, effects of soil particle size, density, and texture could be modeled without functional data fitting or rigorous analysis of material ...
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Citations
... In Pilorget et al. (2016), the macroscopic roughness parameter, as defined by Hapke (1984), has been shown evolving with the wavelength and being to first order correlated with the absorptivity of the particles, thus mostly corresponding to a measurement of the particle shadowing. Additional support for this observation can be found in Carson et al. (2015), who find from a physically robust modelling study that fine granular soils composed of quartzite and magnetite have a bidirectional reflectance distribution function (BRDF) intensity which is inversely proportional to wavelengths and therefore results in a systematically higher reflectance at longer infrared wavelengths for all view angles. Furthermore, Robinson & Friedman (2005) observed that the dielectric constant of materials composed of spherical particles can be affected by the geometry of the sphere packing arrangements. ...
... Notably, the sediment bars selected on the Bonamico River, featuring a D 50 of 35 mm, have a lower spectral signature over the whole spectrum, in comparison to the other sites (sites B1 and B2, Figure 5a). This result is explained by accounting for the different factors that influence the spectral response of the soils (Swain & Davis, 1978 (Black et al., 2014;Carson et al., 2015;Pilorget et al., 2016) and later confirmed by our results (Table 4). The fact that, by mixing the dataset collected in the different sites and thus mixing different lithology and environmental conditions, particle sizes are distinguishable confirms that besides many parameters that influence reflectance response, surface roughness of unvegetated, homogeneous, exposed and dry sediment river bars affects Sentinel-2 data enough to discriminate different sediment classes. ...
... The spectral signature analysis results were encouraging and con- Black et al. (2014), Carson et al. (2015) and Pilorget et al. (2016), as well as the preliminary analysis made on the spectral signatures. Moreover, the negative coefficients appearing in singleband models confirm that the physical effect of an inverse correlation between grain size and reflectance values of Sentinel-2 (Table 4) is likely the most influential physical process to be captured (again, over the other factors that influence the spectral response of soils, in uncontrolled field conditions). ...
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