Are Earth Observation Foundation Models Really Foundation Models? Investigation Based on Spectral Analysis

Recent progress in Earth Observation (EO) foundation models has raised expectations that large-scale pretraining will yield general-purpose representations comparable to those in natural language processing and computer vision. In this work, we show that this promise has not yet been realized. We introduce spectral stability as a principled criterion for foundation models, measuring the extent to which the principal singular subspaces of pretrained weights are preserved during fine-tuning. Through this lens, we conduct a comparative analysis of several EO foundation models, including AnySat and Presto, alongside established models from vision and language, namely DINOv2 and BERT. Our analysis reveals a stark contrast between domains. BERT and DINOv2 exhibit strong spectral stability, with fine-tuning primarily inducing rotations within a small low-rank subspace. In contrast, EO models display severe spectral instability, where fine-tuning substantially rewrites their dominant singular directions. We show that this instability explains two key limitations of current EO foundation models. First, pretraining does not consistently accelerate downstream learning. Second, low-rank adaptation methods such as LoRA can fail or collapse, as the pretrained subspaces are only partially useful. Using extensive experiments on the TimeMatch benchmark for cross-regional crop classification, we demonstrate that despite strong performance claims, pretrained EO models yield inconsistent or marginal improvements over random initialization and do not achieve state-of-the-art performance. These findings indicate that current EO models lack the representational universality characteristic of true foundation models. We conclude that spectral stability is a critical property for robust transfer learning in Earth Observation, and we argue that future EO foundation models should prioritize spectral coherence through improved pretraining objectives and architectural designs that better capture the underlying structure of geospatial data.