Anisotropy, a structural property of dispersal, is observed in dispersal patterns occurring for a wide range of biological systems. While dispersal models more and more often incorporate anisotropy, the sampling schemes required to collect data for validation usually do not account for the anisotropy of dispersal data. Using a parametric model already published to describe the spatial spread of a plant disease, the wheat yellow rust, we carry out a study aimed at recommending an appropriate sampling scheme for anisotropic data. In a first step, we show with a simulation study that prior knowledge of dispersal anisotropy can be used to improve the sampling scheme. One of the main guidelines to be proposed is the orientation of the sampling grid around the main dispersal directions. In a second step, we propose a sequential sampling procedure (SSP) used to automatically build anisotropic sampling schemes adapted to the actual anisotropy of dispersal. The SSP is applied to simulated and real data. The proposed methodology is expected to be adapted easily to any kind of organisms with wind-borne propagule dispersal because it does not require the inclusion of biological features specific of the considered organism.