RPV Model Parameters Based on Hyperspectral Bidirectional Reflectance Measurementsof Fagus sylvatica L. Leaves

Biliouris, Dimitrios; Zande, Dimitry Van der; Verstraeten, Willem; Stuckens, Jan; Muys, Bart; Dutré, Philip; Coppin, Pol

doi:10.3390/rs1020092

Cited by 37 publications

(9 citation statements)

References 30 publications

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“…After reviewing the values presented by other authors (who have also worked with the parameters of the MRPV model), we verified that the values reported in our study coincided with those examined in the literature [25,37,38].…”

Section: Rahman-pinty-verstraete Modified (Mrpv) Model Parameters Behsupporting

confidence: 89%

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Relationship between MRPV Model Parameters from MISRL2 Land Surface Product and Land Covers: A Case Study within Mainland Spain

Arrogante-Funes

Novillo

Calcerrada

et al. 2017

IJGI

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Abstract:In this study, we showed that the multi-angle satellite remote sensing product, MISR L2 Land Surface (MIL2ASLS), which has a scale of 1.1 km, could be suitable for improving land-cover studies. Using seven images from this product, captured by the multi-angle imaging spectroradiometer sensor (MISR), we explored the values reached by the three parameters (ρ0, Θ, and k) of the Rahman-Pinty-Verstraete model, which was modified by Martonchick (MRPV). Thereafter, we compared the values and behaviors shown in seven Co-ordination of Information on the Environment (CORINE) land cover categories, in the red and near infrared (NIR) bands, over the seven MISR orbits captured in 2006 for Mainland Spain. Furthermore, we used Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), and Fraction of Photosynthetically Active Radiation (FPAR) ancillary data and the illumination angles from the same pixels, which made up the images. These ancillary data were also provided by the MISR products. An inferential statistic test was performed to evaluate the relationship between each parameter-band combination, and the land cover in every MISR orbit used. The results suggested that the ρ0 parameters of this product seemed to be the most related to photosynthetic activity, and it should be comparable with the widely-used NDVI. On the other hand, the k and Θ parameter values were not related, or at least not entirely related, to the phenology of land coverage. These seemed to be more influenced by the anisotropy behavior of the studied land cover pixels. Additionally, we observed, by constructing analysis of variance, how the mean of each MRPV parameter-band differed statistically (p < 0.01) by land covers and orbits. This study suggested that the MISR MRPV model parameter data product has great potential to be used to improve land cover applications.

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Section: Rahman-pinty-verstraete Modified (Mrpv) Model Parameters Behsupporting

confidence: 89%

“…The latter indicated a predominance of backscattering [26,37,38]. Pinty et al [17] were the first to report how the anisotropic character of terrestrial targets is influenced by the structure and density of vegetation on a sub-pixel scale.…”

Section: Rahman-pinty-verstraete Modified (Mrpv) Model Parameters Behmentioning

confidence: 99%

Relationship between MRPV Model Parameters from MISRL2 Land Surface Product and Land Covers: A Case Study within Mainland Spain

Arrogante-Funes

Novillo

Calcerrada

et al. 2017

IJGI

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“…The RPV model is a semi-empirical model that uses four parameters to simulate the BRDF of any arbitrary surface. It has been used in anisotropy studies of a wide variety of targets like forests [44], grasslands and cultivated fields [45], snow [31], and soil [10,46] at different scales ranging from the centimeter scale in laboratory studies [10,47], to the decimeter scale in UAV-and airborne-based studies [11,31], up to a scale of several hundreds of meters to a kilometer scale in space-borne studies. The model splits the reflectance anisotropy in an amplitude component (ρ 0 ) and a directional component:…”

Section: Discussionmentioning

confidence: 99%

Hyperspectral Reflectance Anisotropy Measurements Using a Pushbroom Spectrometer on an Unmanned Aerial Vehicle—Results for Barley, Winter Wheat, and Potato

et al. 2016

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Abstract:Reflectance anisotropy is a signal that contains information on the optical and structural properties of a surface and can be studied by performing multi-angular reflectance measurements that are often done using cumbersome goniometric measurements. In this paper we describe an innovative and fast method where we use a hyperspectral pushbroom spectrometer mounted on a multirotor unmanned aerial vehicle (UAV) to perform such multi-angular measurements. By hovering the UAV above a surface while rotating it around its vertical axis, we were able to sample the reflectance anisotropy within the field of view of the spectrometer, covering all view azimuth directions up to a 30 • view zenith angle. We used this method to study the reflectance anisotropy of barley, potato, and winter wheat at different growth stages. The reflectance anisotropy patterns of the crops were interpreted by analysis of the parameters obtained by fitting of the Rahman-Pinty-Verstraete (RPV) model at a 5-nm interval in the 450-915 nm range. To demonstrate the results of our method, we firstly present measurements of barley and winter wheat at two different growth stages. On the first measuring day, barley and winter wheat had structurally comparable canopies and displayed similar anisotropic reflectance patterns. On the second measuring day the anisotropy of crops differed significantly due to the crop-specific development of grain heads in the top layer of their canopies. Secondly, we show how the anisotropy is reduced for a potato canopy when it grows from an open row structure to a closed canopy. In this case, especially the backward scattering intensity was strongly diminished due to the decrease in shadowing effects that were caused by the potato rows that were still present on the first measuring day. The results of this study indicate that the presented method is capable of retrieving anisotropic reflectance characteristics of vegetation canopies and that it is a feasible alternative for field goniometer measurements.

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“…Biliouris et al [9] also made leaf directional reflectance measurements on European beech leaves. As described in [10], they used the compact laboratory spectrogoniometric device (CLab-SpeG) to measure leaf BCRF as an estimate of BRF.…”

Section: Introductionmentioning

confidence: 99%

On Leaf BRDF Estimates and Their Fit to Microfacet Models

Roth

Saunders

Bachmann

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Remote sensing provides high accuracy/precision for quantifying forest biophysical parameters needed for ecological management. Although the significant impact of bidirectional scattering distribution functions (BSDFs) on remote sensing of vegetation is well known, current forest metrics derived from sensor data seldom take leaf BSDF into account, and despite the importance of BSDF effects, leaf directional scattering measurements are almost nonexistent. Previous studies have been limited in the spectral coverage and resolution of observed electromagnetic radiation and lacked models to interpolate all source-sensor angles beyond measurements. This study captured deciduous broadleaf bidirectional reflectance distribution functions (BRDFs) from the visible through shortwave infrared spectral regions (350-2500 nm) and accurately modeled the BRDF for extension to any illumination angle, viewing zenith, or azimuthal angle. We measured biconical directional reflectance factor of leaves from three species of large trees, Norway maple (Acer platanoides), American sweetgum (Liquidambar styraciflua), and northern red oak (Quercus rubra). We then fit the data through nonlinear regression to physical, microfacet BRDF models, resulting in normalized root-mean-square errors of less than 8%, averaged across all wavelengths (excluding low signalto-noise spectral regions). We extracted leaf physical parameters, including the index of refraction and a relative physical roughness from the microfacet models delineating the three species. The implications for forestry remote sensing are important, as rigorous models to represent leaves allow for the creation of more accurate forest scenes for radiative transfer modeling. Such accuracy enables higher fidelity sensor evaluations and data processing algorithms.

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RPV Model Parameters Based on Hyperspectral Bidirectional Reflectance Measurementsof Fagus sylvatica L. Leaves

Cited by 37 publications

References 30 publications

Relationship between MRPV Model Parameters from MISRL2 Land Surface Product and Land Covers: A Case Study within Mainland Spain

Relationship between MRPV Model Parameters from MISRL2 Land Surface Product and Land Covers: A Case Study within Mainland Spain

Hyperspectral Reflectance Anisotropy Measurements Using a Pushbroom Spectrometer on an Unmanned Aerial Vehicle—Results for Barley, Winter Wheat, and Potato

On Leaf BRDF Estimates and Their Fit to Microfacet Models

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