Environmental variables never seen in pretraining are linearly decodable from frozen geospatial SSL encoders, with architecture-dependent strength

Mocharla & Patel (2026) fit ridge-regression (linear) and MLP (nonlinear) probes for five ERA5 environmental variables (temperature, precipitation, solar radiation, pressure, soil water) -- never used during pretraining -- on real DINO/MAE/MoCo ViT-S/16 encoders trained identically on SSL4EO Sentinel-1/2 imagery, plus several public geospatial foundation models [mocharla-patel-2026-probing-geospatial-ssl-representations] DINO shows the strongest ERA5 decodability (linear R^2=0.50, MLP R^2=0.68) versus MAE/MoCo near-random despite similar downstream segmentation mIoU; linear-probe R^2 correlates more strongly with downstream agricultural-task utility than MLP R^2 (rho=0.75 vs 0.63) [mocharla-patel-2026-probing-geospatial-ssl-representations] Intrinsic geometry metrics (effective rank, uniformity) correlate with probe performance (rho~=0.40-0.45); purely associative, the authors explicitly state the analysis is not causal [mocharla-patel-2026-probing-geospatial-ssl-representations]