  Optical and radar satellite time series are synergetic: optical images
contain rich spectral information, while C-band radar captures useful
geometrical information and is immune to cloud cover. Motivated by the recent
success of temporal attention-based methods across multiple crop mapping tasks,
we propose to investigate how these models can be adapted to operate on several
modalities. We implement and evaluate multiple fusion schemes, including a
novel approach and simple adjustments to the training procedure, significantly
improving performance and efficiency with little added complexity. We show that
most fusion schemes have advantages and drawbacks, making them relevant for
specific settings. We then evaluate the benefit of multimodality across several
tasks: parcel classification, pixel-based segmentation, and panoptic parcel
segmentation. We show that by leveraging both optical and radar time series,
multimodal temporal attention-based models can outmatch single-modality models
in terms of performance and resilience to cloud cover. To conduct these
experiments, we augment the PASTIS dataset with spatially aligned radar image
time series. The resulting dataset, PASTIS-R, constitutes the first
large-scale, multimodal, and open-access satellite time series dataset with
semantic and instance annotations.
