\begin{abstract}
Self-supervised learning (SSL) has emerged as a powerful approach in computer vision and language modeling, yet its application to Earth observation (EO) remains challenging due to heterogeneous sensors, irregular sampling, and weak physical priors.
We present SPEAR, a modular, pixel-level foundation framework that learns sensor-adaptive representations across optical, radar, and climate modalities. Each modality is pretrained through a dedicated SSL learner \textit{Spectral-MAE} for multispectral imagery, \textit{BYOL-Denoise} for radar backscatter, and \textit{Climate-MAE} for environmental variables producing compact 32-D per-pixel embeddings conditioned on wavelength, spatial scale, and geolocation. A sensor- and scale-aware fusion head integrates these embeddings via concatenation or cross-attention, while learned zero-slot tokens enable seamless operation under missing-modality conditions. Leveraging pretraining on a heterogeneous multimodal dataset Sentinel-1, Sentinel-2, PlanetScope, and climate fields the model is evaluated on four representative benchmarks: Dynamic World land-cover, Sen1Floods11 flood detection, VIIRS Active Fire detection, and Sickle crop-type classification Despite using only approximately 1M parameters, achieves competitive accuracy, demonstrating strong performance and scalability in multi-sensor settings. Overall, SPEAR establishes a lightweight, sensor-awared foundation architecture for scalable, multi-sensor Earth analytics.
\end{abstract}
