  Geographic variance in satellite imagery impacts the ability of machine
learning models to generalise to new regions. In this paper, we model
geographic generalisation in medium resolution Landsat-8 satellite imagery as a
continuous domain adaptation problem, demonstrating how models generalise
better with appropriate domain knowledge. We develop a dataset spatially
distributed across the entire continental United States, providing macroscopic
insight into the effects of geography on crop classification in multi-spectral
and temporally distributed satellite imagery. Our method demonstrates improved
generalisability from 1) passing geographically correlated climate variables
along with the satellite data to a Transformer model and 2) regressing on the
model features to reconstruct these domain variables. Combined, we provide a
novel perspective on geographic generalisation in satellite imagery and a
simple-yet-effective approach to leverage domain knowledge. Code is available
at: \url{https://github.com/samar-khanna/cropmap}
