Joint optical and Synthetic Aperture Radar (SAR) pretraining seeks cross-modal gains over representations learned from either modality alone. This is challenging because the modalities share only partial information, and their correspondence varies across pairs with scene context, acquisition timing, sensor configuration, and spatial resolution. In particular, finer resolution exposes more detailed modality-specific optical appearance and SAR backscatter, further complicating cross-modal supervision. Existing methods encourage cross-modal agreement through representation alignment or cross-modal prediction. Under simplified models of partial, pair-dependent correspondence, we show why a fixed alignment relation can be insufficient and characterize prediction ambiguity arising from target variation unresolved by the source representation. These analyses motivate CoDe-MAE, a masked-autoencoding framework for high-resolution optical-SAR joint pretraining. Its Conditioned Contrastive Learning (CCL) aligns cross-attention-conditioned representations, while Cross-modal Degraded-Target Prediction (CDP) predicts transformed targets to balance conditional predictability and retained supervision. We curate OSPretrain-1M, a 1M-sample optical-SAR corpus, and use CoDe-MAE to pretrain MMATR-X, which achieves leading transfer performance across optical-only, SAR-only, and dual-modal tasks. Controlled experiments on remote-sensing optical-SAR, optical-DEM, and indoor RGB-depth pairs further support these design principles across heterogeneous modality pairs. Code: \url{https://github.com/scenarri/MMATR-X}.

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