Function approximation, integration, and inverse problems are just few examples of numerical fields that rely on efficient strategies for function sampling. As particular sampling rules, the concepts of cubatures in Euclidean space and the sphere have been widely investigated to integrate polynomials by a finite sum of sampling values, cf. [22,30,39,41,47]. To some extent, cubatures are universal sampling strategies in the sense that they are highly efficient in many fields, and in the context of function approximation, covering, and integration they have proved superior to the widely used random sampling [12,52].Recently, cubatures on compact manifolds have attracted attention, cf. [11,32,49]. Integration, covering, and polynomial approximation from cubatures on manifolds and homogeneous spaces have been extensively studied from a theoretical point of view, cf. [20, 24, 34, 43, 52] and references therein. Orthogonality is a leading concept in many mathematical fields, and dimension reduction is intrinsically tied together with low dimensional projections. The Grassmannian manifold is the space of orthogonal projectors of fixed rank,