Mapping accurately vegetation type is one of the main challenges for monitoring arid and semi-arid grasslands with remote sensing. The multi-angle approach has been demonstrated to be useful for mapping vegetation types in deserts. The current paper presents a study on the use of directional reflectance derived from two sensor systems, using two different models to analyse the data and two different classifiers as a means of mapping vegetation types. The multiangle imaging spectroradiometer (MISR) and the moderate resolution imaging spectroradiometer (MODIS) provide multi-spectral and angular, off-nadir observations. In this study, we demonstrate that reflectance from MISR observations and reflectance anisotropy patterns derived from MODIS observations are capable of working together to increase classification accuracy. The patterns are described by parameters of the modified Rahman-Pinty-Verstraete and the RossThin-LiSparseMODIS bidirectional reflectance distribution function (BRDF) models. The anisotropy patterns derived from MODIS observations are highly complementary to reflectance derived from radiances observed by MISR. Support vector machine algorithms exploit the information carried by the same data sets more effectively than the maximum likelihood classifier.