  Existing works mainly rely on the far-field planar-wave-based channel model
to assess the performance of reconfigurable intelligent surface (RIS)-enabled
wireless communication systems. However, when the transmitter and receiver are
in near-field ranges, this will result in relatively low computing accuracy. To
tackle this challenge, we initially develop an analytical framework for
sub-array partitioning. This framework divides the large-scale RIS array into
multiple sub-arrays, effectively reducing modeling complexity while maintaining
acceptable accuracy. Then, we develop a beam domain channel model based on the
proposed sub-array partition framework for large-scale RIS-enabled
UAV-to-vehicle communication systems, which can be used to efficiently capture
the sparse features in RIS-enabled UAV-to-vehicle channels in both near-field
and far-field ranges. Furthermore, some important propagation characteristics
of the proposed channel model, including the spatial cross-correlation
functions (CCFs), temporal auto-correlation functions (ACFs), frequency
correlation functions (CFs), and channel capacities with respect to the
different physical features of the RIS and non-stationary properties of the
channel model are derived and analyzed. Finally, simulation results are
provided to demonstrate that the proposed framework is helpful to achieve a
good tradeoff between model complexity and accuracy for investigating the
channel propagation characteristics, and therefore providing highly-efficient
communications in RIS-enabled UAV-to-vehicle wireless networks.
