\section{Related Work}
\label{sec:related}

Self-supervised learning (SSL) progresses primarily through contrastive, masked prediction, and bootstrapping strategies. Contrastive models such as MoCo~\cite{he2020momentum} and SimCLR~\cite{chen2020simple} maximize agreement between augmented views while contrasting others, often requiring large batches and carefully defined positives. Bootstrapping methods like BYOL~\cite{grill2020bootstrap} and DINO~\cite{caron2021emerging} remove explicit negatives by training student and teacher encoders via momentum updates, yielding stable, semantically rich embeddings; DINO particularly leverages ViTs~\cite{han2022survey} with multi-crop self-distillation. Masked image modeling (MIM)~\cite{hondru2025masked} takes a generative route where MAE-style models reconstruct masked patches using an asymmetric encoder–decoder, learning global structure from partial inputs. These three SSL families underpin modern visual foundation models such as CLIP~\cite{radford2021learning}, BEiT~\cite{bao2021beit}, and MAE variants, enabling strong cross-domain transfer. In remote sensing, SatMAE~\cite{cong2022satmae} introduced spectral-temporal masking for multispectral imagery, ScaleMAE~\cite{reed2023scale} added scale-awareness via GSD encoding, Cross-Scale MAE~\cite{tang2023cross} enforced multi-resolution consistency, and S2MAE~\cite{li2024s2mae} extended masked reconstruction to 3D spectral–spatial cubes. SpectralGPT~\cite{hong2023spectralgpt} further scaled MIM for hyperspectral data using 3D tokens and multi-target decoding. Beyond purely spectral models, multimodal transformers such as ContextFormer~\cite{koyuncu2022contextformer} and FusAtNet~\cite{mohla2020fusatnet} fuse optical, meteorological, or LiDAR features via cross-attention, highlighting the value of multi-sensor integration. More recent works like AlphaEarth~\cite{brown2025alphaearth} with physics-aware implicit decoders and TESSERA~\cite{feng2025tessera} with dual Sentinel-1/2 encoders, push toward unified global-scale embeddings robust to missing or cloudy observations. MMST-ViT~\cite{lin2023mmst} advances this direction by jointly modeling multi-modal spatial-temporal signals for climate-aware crop yield prediction, emphasizing long-range temporal reasoning and modality-conditioned attention. Despite these advances, most prior efforts specialize in isolated aspects - fusion, pixel-level encoding, or missing-data robustness. SPEAR instead unifies optical, radar, and climate modalities through physics-informed self-supervised pretraining, producing compact wavelength- and resolution-conditioned 32-D pixel embeddings that remain consistent across single-modality or multimodal inputs. The next section introduces our method in detail.