  Large-scale crop type classification is a task at the core of remote sensing
efforts with applications of both economic and ecological importance. Current
state-of-the-art deep learning methods are based on self-attention and use
satellite image time series (SITS) to discriminate crop types based on their
unique growth patterns. However, existing methods generalize poorly to regions
not seen during training mainly due to not being robust to temporal shifts of
the growing season caused by variations in climate. To this end, we propose
Thermal Positional Encoding (TPE) for attention-based crop classifiers. Unlike
previous positional encoding based on calendar time (e.g. day-of-year), TPE is
based on thermal time, which is obtained by accumulating daily average
temperatures over the growing season. Since crop growth is directly related to
thermal time, but not calendar time, TPE addresses the temporal shifts between
different regions to improve generalization. We propose multiple TPE
strategies, including learnable methods, to further improve results compared to
the common fixed positional encodings. We demonstrate our approach on a crop
classification task across four different European regions, where we obtain
state-of-the-art generalization results.
