RPV Model Parameters Based on Hyperspectral Bidirectional Reflectance Measurementsof Fagus sylvatica L. Leaves
"> Figure 1
<p>Picture and mechanical system setup of the Compact Laboratory Spectro-Goniometer (CLabSpeG). In the horizontal plane an aluminium rail (a) supports the light source arm (b) and rotates anti-clock wise with an angular sampling step of 30°. A stationary arm (c) supports the hyperspectral sensor. Light source (f) and spectroradiometer (e) have an operational angular sampling step of 15°. In the centre there is the sample holder including a leaf (d), rotating clock-wise with an angular sampling step of 30° [<a href="#B16-remotesensing-01-00092" class="html-bibr">16</a>].</p> "> Figure 2
<p>Relative standard deviation in % of the mean BRF of all 12 azimuth sensor positions for sensor zenith at 0°, 15°, 30°, 45°, and 60°. The light source is at 15° zenith and 0° azimuth. The mean reflectance among the 12 azimuth sensor position is presented with a thin solid line with the reflectance values on the right scale.</p> "> Figure 3
<p>The RPV model parameters rho0, k, and Θ for all wavelengths, are presented for the eight BRF measurements with light source at 45°.</p> "> Figure 4
<p>Bidirectional Reflectance Factors of a <span class="html-italic">Fagus sylvatica</span> L. leaf at 850 nm, for light source position set at 45° zenith and 90° azimuth (top panel) and 45° zenith and 0°azimuth (bottom panel). On the right side of the Figure the simulated RPV Bidirectional Reflectance Factors are presented for the same angular and wavelength configurations. The sensor is azimuthally positioned all over the hemisphere and ranges in zenith between 0° and 60°, with 15° increments. Sensors positions are marked by dots, while incident direction is presented by a star. The bar scale indicate reflectance values.</p> "> Figure 5
<p>Principal plane of the Bidirectional Reflectance Factor for measured reflectance and RPV estimations for the wavelengths of 550 nm, 850 nm, and 1,650 nm.</p> "> Figure 6
<p>Correlation Coefficient R (left graph) and Root Mean Square Error (right graph) between measured and RPV reflectance for different light source zenith positions.</p> "> Figure 7
<p>Correlation Coefficient R (left graph) and Root Mean Square Error (right graph) between measured data not participating in the inversion process and RPV reflectance for different light source zenith positions.</p> ">
Abstract
:1. Introduction
- The inversion of the widely applied Rahman-Pinty-Verstraete (RPV) model based on eight Bidirectional Reflectance Factor (BRF) data sets of leaves in the 400 to 2,500 nm spectral range.
- The evaluation of the retrieved RPV parameters based on four measured BRF data sets by comparison among modelled and measured leaf BRF values along the full hyperspectral dynamic range of the spectroradiometer.
2. Theoretical background: RPV model description
3. Data description
4. Methodology
4.1. Leaf variance
4.2. RPV inversion
5. Results and Discussion
5.1. BRF variance
5.2. RPV model
Viewing Conditions Principal plane | rho0 | k | Θ | |||
---|---|---|---|---|---|---|
Mean | St. dev | Mean | St. dev | Mean | St. dev | |
550 nm | 0.161 | 0.83 × 10-2 | 0.903 | 2.43 × 10-2 | 0.159 | 0.43 × 10-2 |
850 nm | 0.718 | 1.09 × 10-2 | 0.954 | 2.93 × 10-2 | 0.101 | 0.27 × 10-2 |
1,650 nm | 0.599 | 0.99 × 10-2 | 0.996 | 1.38 × 10-2 | 0.103 | 0.36 × 10-2 |
Viewing Conditions Principal plane | Light source zenith | Chi2 | p-value |
---|---|---|---|
550 nm | 15° | 7.805 | 15.51 |
850 nm | 15° | 6.576 | 15.51 |
1,650 nm | 15° | 6.781 | 15.51 |
550 nm | 60° | 7.845 | 15.51 |
850 nm | 60° | 6.543 | 15.51 |
1,650 nm | 60° | 6.762 | 15.51 |
6. Conclusions and Recommendations
Acknowledgements
References and Notes
- Asner, G.P. Biophysical and biochemical sources of variability in canopy reflectance. Remote Sens. Environ. 1998, 64, 234–253. [Google Scholar] [CrossRef]
- Boucher, Y.; Cosnefroy, H.; Petit, D.; Serrot, G.; Briottet, X. Comparison of measured and modeled BRDF of natural targets. In N° 3699-02 SPIE AeroSense; Orlando, FL, USA, April 1999; pp. 16–26. [Google Scholar]
- Maignan, F.; Bréon, F.M.; Lacaze, R. Bidirectional reflectance of earth targets, evaluation of analytical models using a large set of spaceborne measurements with emphasis on the hot spot. Remote Sens. Environ. 2004, 90, 210–220. [Google Scholar] [CrossRef]
- Schönermark, M.; Geiger, B.; Röser, H.P. Reflection Properties of Vegetation and Soil with a BRDF Data base; Wissenschaft und Technik Verlag: Berlin, Germany, 2004; p. 352. [Google Scholar]
- Roujean, J.-L.; Leroy, M.; Deschamps, P.Y. A bi-directional reflectance model of the Earth’s surface for the correction of remote sensing data. J. Geophys. Res. 1992, 97, 20455–20468. [Google Scholar] [CrossRef]
- Rahman, H.; Verstraete, M.M.; Pinty, B. Coupled Surface-Atmosphere Reflectance (CSAR) model 1. Mmodel description and inversion on synthetic data. J. Geophys. Res. 1993, 98, 20779–20789. [Google Scholar] [CrossRef]
- Rahman, H.; Pinty, B.; Verstraete, M.M. Coupled Surface-Atmosphere Reflectance (CSAR) model 2. Semiempirical surface model usable with noaa advanced very high resolution radiometer data. J. Geophys. Res. 1993, 98, 20791–20781. [Google Scholar] [CrossRef]
- Pinty, B.; Widlowski, J.-L.; Taberner, M.; Gobron, N.; Verstraete, M.M.; Disney, M.; Gascon, F.; Gastellu, J.-P.; Jiang, L.; Kuusk, A.; Lewis, P.; Li, X.; Ni-Meister, W.; Nilson, T.; North, P.; Qin, W.; Su, L.; Tang, S.; Thompson, R.; Verhoef, W.; Wang, H.; Wang, J.; Yan, G.; Zang, H. Radiation Transfer Model Intercomparison (RAMI) exercise, results from the second phase. J. Geophys. Res. 2004, 109, D06210. [Google Scholar] [CrossRef]
- Widlowski, J.-L.; Taberner, M.; Pinty, B.; Bruniquel-Pinel, V.; Disney, M.; Fernandes, R.; Gastellu-Etchegorry, J.-P.; Gobron, N.; Kuusk, A.; Lavergne, T.; Leblanc, S.; Lewis, P.E.; Martin, E.; Mõttus, M.; North, P.R.J.; Qin, W.; Robustelli, M.; Rochdi, N.; Ruiloba, R.; Soler, C.; Thompson, R.; Verhoef, W.; Verstraete, M.M.; Xie, D. Third Radiation Transfer Model Intercomparison (RAMI) exercise: Documenting progress in canopy reflectance modeling. J. Geophys. Res. 2007, 112, D09111. [Google Scholar] [CrossRef]
- Zhang, Z.; Kalluri, S.; JaJa, J.; Liang, S.; Townshend, J.R.G. High performance algorithms for global BRDF retrieval. IEEE Comput. Sci. Eng. 1998, 4, 16–29. [Google Scholar] [CrossRef]
- Gobron, N.; Lajas, D. A new inversion scheme for the RPV model. Can. J. Remote Sens. 2002, 28, 156–167. [Google Scholar] [CrossRef]
- Lavergne, T.; Kaminski, T.; Pinty, B.; Taberner, M.; Gobron, N.; Verstraete, M.M.; Vossbeck, V.; Widlowski, J.-L.; Giering, R. Application to MISR land products of an RPV model inversion package using adjoint and Hessian codes. Remote Sens. Environ. 2007, 107, 362–375. [Google Scholar] [CrossRef]
- Li, X.; Strahler, A.H. Geometrical - optical modelling of a conifer forest canopy. IEEE Trans. Geosci. Remote Sens. 1985, 23, 705–721. [Google Scholar] [CrossRef]
- Walter-Shea, E.A.; Norman, J.M.; Blad, B.L. Leaf bidirectional reflectance and transmittance in corn and soybean. Remote Sens. Environ. 1989, 29, 161–174. [Google Scholar] [CrossRef]
- Pinty, B.; Verstraete, M.M.; Gobron, N. The effect of soil anisotropy on the radiance field emerging from vegetation canopies. Geophys. Res. Lett. 1998, 25, 797–800. [Google Scholar] [CrossRef]
- Biliouris, D.; Verstraeten, W.W.; Dutré, P.; Aardt, J.A.N.; Muys, B.; Coppin, P. A Compact Laboratory Spectro-Goniometer (CLabSpeG) to assess the BRDF of materials. Presentation, calibration and implementation on Fagus sylvatica L. leaves. Sensors 2007, 7, 1846–1870. [Google Scholar] [CrossRef]
- Verstraete, M.M.; Pinty, B.; Myneni, R.B. Potential and limitations of information extraction on the terrestrial biosphere from satellite remote sensing. Remote Sens. Environ. 1996, 58, 201–215. [Google Scholar] [CrossRef]
- Martonchik, J.V.; Diner, D.J.; Kahn, R.A.; Ackerman, T.P.; Verstraete, M.M.; Pinty, B.; Gordon, H.R. Techniques for retrieval of aerosol properties over land and ocean using multiangle imaging. IEEE Trans. Geosci. Remote Sens. 1998, 36, 1212–1227. [Google Scholar] [CrossRef]
- Lattanzio, A.; Govaerts, Y.M.; Pinty, B. Consistency of surface anisotropy characterization with meteosat observations. Adv. Space Res. 2007, 39, 131–135. [Google Scholar] [CrossRef]
- Schaaf, C.B.; Gao, F.; Strahler, A.H.; Lucht, W.; Li, X.; Tsang, T. First operational BRDF, albedo and nadir reflectance products from MODIS. Remote Sens. Environ. 2002, 83, 135–148. [Google Scholar] [CrossRef]
- Widlowski, J-L.; Pinty, B.; Gobron, N.; Verstraete, M.M.; Davies, A.B. Characterization of surface heterogeneity detected at the MISR/TERRA subpixel scale. Geophys. Res. Lett. 2001, 28, 4639–4642. [Google Scholar]
- Gao, F.; Schaaf, C.B.; Strahler, A.H.; Jin, Y.; Li, X. Detecting vegetation structure using a kernel-based BRDF model. Remote Sens. Environ. 2003, 86, 198–205. [Google Scholar] [CrossRef]
- Engelsen, O.; Pinty, B.; Verstraete, M.M.; Martonchik, J.V. Parametric bidirectional reflectance factor models: evaluation, improvements and applications. In Catalogue CL-NA-16426-EN-C, ECSC-EC-EAEC.; Brussels, Luxembourg, 1996; p. 114. [Google Scholar]
- Minnaert, M. The reciprocity principle in lunar photometry. Astrophys. J. 1941, 93, 403–410. [Google Scholar] [CrossRef]
- Henyey, L.G.; Greenstein, T.L. Diffuse radiation in the galaxy. Astrophys. J. 1941, 93, 70–83. [Google Scholar] [CrossRef]
- Sandmeier, St. Acquisition of bidirectional reflectance factor data with field goniometers. Remote Sens. Environ. 2000, 73, 257–269. [Google Scholar] [CrossRef]
- Nicodemus, F.E.; Richmond, J.C.; Hsia, J.J.; Ginsberg, I.W.; Limperis, T. Geometrical considerations and nomenclature for reflectance. In National Bureau Standards Monograph; Inst. for Basic Standards: Washington, DC. USA, 1977; p. 160. [Google Scholar]
- Martonchik, J.V.; Bruegge, C.J.; Strahler, A.H. A review of reflectance nomenclature used in remote sensing. Remote Sens. Rev. 2000, 19, 9–20. [Google Scholar] [CrossRef]
- Schaepman-Strub, G.; Schaepman, M.E.; Painter, T.H.; Dangel, S.; Martonchik, J.V. Reflectance quantities in optical remote sensing -definitions and case studies. Remote Sens. Environ. 2006, 103, 27–42. [Google Scholar] [CrossRef]
- Dangel, S.; Verstraete, M.M.; Schopfer, J.; Kneubuhler, M.; Schaepman, M.; Itten, K.I. Toward a direct comparison of field and laboratory goniometer measurements. IEEE Trans. Geosci. Remote Sens. 2005, 43, 2666–2675. [Google Scholar] [CrossRef]
- Bousquet, L.; Lachérade, S.; Jacquemoud, S.; Moya, I. Leaf BRDF measurements and model for specular and diffuse components differentiation. Remote Sens. Environ. 2005, 98, 201–211. [Google Scholar] [CrossRef]
- Boucher, Y. Analyzing some models for a given type of surface. In Reflection Properties of Vegetation and Soil with a BRDF Data base; Schönermark, M., Geiger, B., Röser, H.P., Eds.; Wissenschaft und Technik Verlag: Berlin, Germany, 2004; pp. 121–129. [Google Scholar]
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Biliouris, D.; Van der Zande, D.; Verstraeten, W.W.; Stuckens, J.; Muys, B.; Dutré, P.; Coppin, P. RPV Model Parameters Based on Hyperspectral Bidirectional Reflectance Measurementsof Fagus sylvatica L. Leaves. Remote Sens. 2009, 1, 92-106. https://doi.org/10.3390/rs1020092
Biliouris D, Van der Zande D, Verstraeten WW, Stuckens J, Muys B, Dutré P, Coppin P. RPV Model Parameters Based on Hyperspectral Bidirectional Reflectance Measurementsof Fagus sylvatica L. Leaves. Remote Sensing. 2009; 1(2):92-106. https://doi.org/10.3390/rs1020092
Chicago/Turabian StyleBiliouris, Dimitrios, Dimitry Van der Zande, Willem W. Verstraeten, Jan Stuckens, Bart Muys, Philip Dutré, and Pol Coppin. 2009. "RPV Model Parameters Based on Hyperspectral Bidirectional Reflectance Measurementsof Fagus sylvatica L. Leaves" Remote Sensing 1, no. 2: 92-106. https://doi.org/10.3390/rs1020092