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TitleOn the Performance of Hybrid BRDF Models over Boreal Forest Stands
AuthorLi, Z; Moreau, L; Cihlar, J
SourceInternational Symposium, Geomatics in the Era of RADARSAT (GER'97), Ottawa, Canada, May 25-30; 1997 p. 12
Alt SeriesEarth Sciences Sector, Contribution Series 20042199
AbstractSeveral kernel-driven bidirectional reflectance distribution functions (KDB) are tested using multi-angle reflectance measurements made with a Portable Apparatus for Rapid Acquisition of Bidirectional Observations of the Land and Atmosphere (PARABOLA) over boreal forest stands during the BOREAS field campaigns. Eleven linear combinations of five basic kernels, two for volume scattering and three for surface scattering, were evaluated. Overall, volume scattering kernels play a leading role in accounting for the angular variation of reflectance measurements, especially over dense canopies. While fitness in different viewing planes is investigated, emphasis is placed on the accuracy of an albedo estimated by integrating a KDB whose coefficients are tuned by observational data. In view of the limitations of remote sensing data, two basic tests were conducted assuming that observations are available in the principal plane and in all viewing angles. In the first case, performance varies considerably among different KDBs, two-kernel KDBs being much more accurate than single-kernel ones, whereas the accuracy of albedo estimates from different KDBs are not so much different if observations are available throughout the entire viewing domain. Relative uncertainties in the estimated albedo are usually less than 0.3 and 0.1, for the first and second tests respectively. In addition to ground-based measurements, reflectance at the top of the atmosphere (TOA) simulated with 6S were also employed for assessing KDBs. Surprisingly, the KDBs perform even better at the TOA than at the surface. Best results were achieved by the combination of Roujean et al.'s surface scattering kernel and Ross' volume scattering kernel with a relative uncertainty in albedo estimate merely 2%.

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