1. 14 ARM Science Team Meeting, Albuquerque, NM, March 21-26, 2004 Canada Centre for Remote Sensing - Centre canadien de télédétection Geomatics Canada Natural Resources Canada - Géomatique Canada Ressources Naturelles Canada Analysis of BRDF and Albedo Properties of Pure and Mixed Surface Types From Terra MISR Using Landsat High-Resolution Land Cover and Angular Unmixing Technique A. P. Trishchenko a, K.Khlopenkov a, Y. Luo b (a) Canada Centre for Remote Sensing (b) Noetix Research Inc., Ottawa, Canada; Email: kkhlopen@ccrs.nrcan.gc.ca; trichtch@ccrs.nrcan.gc.ca Landcover type (%) Water0.83 Water/land mix0.81 Trees/shrubs4.09 Wheat (quite mature)24.4 Wheat (green)39.3 Pasture/grassland (green) 14.3 Pasture/grassland (dry)10.0 Baresoil (w/ short milo/corn) 5.78 Baresoil0.51 Fitting MISR data with modified Rahman model Landcover types around ARM-SGP CF, May 2003 LandSat ETM+ April 13, 2003 ARM SGP site MISR ETM+ May, 2003 ARM SGP site MISR ETM+ Sep, 2002 ARM SGP site LandSat ETM+ September, 2002 ARM SGP site Landcover types around ARM-SGP CF, Sep 2002 Comparison of Rahman BRDF parameters from MISR with ground observations over wheat field Introduction Surface bi-directional reflectance distribution function (BRDF) is critical for characterization of surface albedo, which plays important role in solar radiation budget and atmospheric radiative transfer. For application purposes, the knowledge of BRDF properties of pure land cover types is frequently required. However, the typical spatial resolution provided by MODIS and MISR instruments is not always sufficient to determine the bidirectional reflectance distribution function (BRDF) of pure surface types, since in most cases, the observed pixels correspond to mixed scenes. Method We selected a subset of a MISR image corresponding to the area of detailed landcover map (about 21  16 km). The selected subset contains approximately 340 MISR pixels. Pixels were then processed in groups, each having the same amount of pixels (12 typically). For each MISR pixel, we can define a set of weights of each landcover class based on the corresponding portion of the occupied area: w c (c=1…5),  w c = 1 We assume that each landcover class has characteristic BRDF: R c ( , ,  0,r0 c,k c,b c ) where r0, k, b are the BRDF shape parameters of the Rahman model, which is used in MISR dataset. The reflectance for each MISR pixel can be expressed as a linear combination of the basic BRDFs: Thus, for each 12-pixel group, we obtain a set of 12 equation with 5 unknowns being the reflectances of the pure landcover classes (water, trees, wheat, grass, baresoil). Solving this linear system yields a least-squares solution vector R model c. Having the values of the reflectance for the 9 directions, we can apply a non-linear fitting of the BRDF in order to find the BRDF shape parameters r0 c, k c, b c, for each basic landcover class. Since we have in about 30 groups of pixels for the selected MISR data subset, we can obtain 30 samples of the shape parameters in each wavelength band. From this statistics, it is possible to calculate the weighted average and the standard deviation for each shape parameter. Conclusions For better understanding of the properties of pure and mixed surface types, we developed an approach for retrieval of the basic surface BRDFs from the observations of MODIS/Terra and MISR instruments. Our research was focused on the Atmospheric Radiation Measurement Program (ARM) Southern Great Planes (SGP) region, which is a predominantly agricultural area. Pure surface classes were identified using high-resolution (30m) Landsat imagery and results of the ground survey. We analyzed data for two seasons (spring and fall). The dominating surface types in the area in spring were wheat (65%) followed by grassland/pasture (25%) and baresoil (6%). During late summer/fall the distribution was dominated by baresoil. Assuming that reflectance for each MISR/MODIS pixel is a weighted combination of reflectances of the pure surface types, we proposed a method to retrieve the original BRDF parameters for each landcover type. From this analysis, the confidence ranges for BRDF shape factors were determined for each spectral band. In general, the proposed method could be used to identify angular signatures of distinct landcover types. At the same time, we observed quite significant variability of BRDF parameters within similar types, which can be explained by non-orthogonality of the BRDF kernel functions, limited angular sampling of satellite observations and variability of natural scenes. The correlation between the BRDF shape factors and normalized difference vegetation index (NDVI) has also been analyzed. We concluded that the NDVI index could be used as a proxy for the BRDF shape of pure landcover types and mixed pixels. Comparison of reflectance predicted with the Pure-Class Model with the one measured by MISR MISR retrievals for low reflectance channels (1, 2, 3) show relatively large variability Mixing various crops and grassland does not create major problems providing that NDVI is taken into account in constructing the BRDF shape. Parameter b responsible for the hot-spot profile in the BRDF shape has the largest variability and requires better angular sampling for more accurate estimation. Results of the fitting of BRDF parameters to the angular reflectance of the pure landcover types MISR spectral and shape factors distribution MISR channel 4 (866nm) Objectives  To analyze the data from Multi-angular Imaging Spectral Radiometer (MISR) sensors onboard Terra satellites over the US Department of Energy Atmospheric Radiation Measurement (ARM) Program Southern Great Plains (SGP) area.  To reconstruct the original BRDFs corresponding to pure landcover classes such as wheat, grassland and baresoil based on the 30m resolution landcover map built from Landsat images and ground survey, and the 1km resolution MISR reflectance data.  To compare the reflectance predicted by the Pure-class Model with the one produced by MISR and test the correlation between the surface anisotropic properties and its green biomass level (e.g. NDVI).