Classification and Verification of Surface Anisotropic Reflectance Characteristics

The surface reflectance anisotropy is usually described by bidirectional reflectance distribution function (BRDF), and the BRDF archetypes extracted based on the Anisotropic Flat Index (AFX) have been used as the prior reflectance anisotropy knowledge of quantitative inversion. In this study, we introduce a new index, i.e., the Perpendicular Anisotropic Flat Index (PAFX), based on the AFX in a prior study, to refine the BRDF classification of the land surface. Based on the MODIS BRDF parameter products sampled at global intervals over multiple land cover types in 2015, we used AFX and PAFX as classification indicators to divide MODIS BRDF parameter space into some orthogonal clusters through use of the ISODATA (Iterative Self organizing data Analysis technique) Clustering Algorithm. As a case study, the MODIS BRDF parameters are divided into three classes by using the AFX and PAFX, respectively, in an orthogonal way, and therefore a total of nine cluster classes of BRDF parameters are generated, correspondingly generating nine BRDF archetypes. We use the average BRDF fitting error, algorithm complexity and classification accuracy to evaluate this classification method for classifying BRDF parameters, showing that the difference within the BRDF archetypes class is significantly reduced after the introduction of PAFX (RMSE = 0.0083) compared with only using AFX (RMSE = 0.0139), and the error for fitting all BRDF shapes with the average BRDF shape, i.e., the BRDF archetype presents a V-shaped trend as functions of AFX and PAFX, indicating an optimized minimum. In general, jointing the AFX and PAFX has improved the classification accuracy of the MODIS BRDF parameter space and is expected a further application in near future.