When Earth Foundation Models Meet Diffusion: An Application to Land Surface Temperature Super-Resolution

• URL: http://arxiv.org/abs/2604.16841v1
• Date: Sat, 18 Apr 2026 05:21:05 GMT
• Title: When Earth Foundation Models Meet Diffusion: An Application to Land Surface Temperature Super-Resolution
• Abstract summary: We propose EFDiff, a novel framework for super-resolution under extreme spatial degradation.<n>EFDiff uses the Prithvi-EO-2.0 Earth foundation model to encode high-resolution multispectral reflectance into geospatial embeddings.<n>We evaluate EFDiff under an extreme $32times$ scale gap using a globally diverse benchmark comprising 242,416 co-registered Landsat thermal-reflectance patches.
• Score: 7.772443261581984
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Land surface temperature (LST) super-resolution is important for environmental monitoring. However, it remains challenging as coarse thermal observations severely underdetermine fine-scale structure. In this paper, we propose Earth Foundation Model-guided Diffusion (EFDiff), a novel framework for super-resolution under extreme spatial degradation. EFDiff uses the Prithvi-EO-2.0 Earth foundation model to encode high-resolution multispectral reflectance into geospatial embeddings, which are injected into the denoising network via cross-attention to guide fine-scale reconstruction from highly degraded observations. We study two variants, EFDiff-$ε$ and EFDiff-$x_0$, which offer complementary trade-offs between perceptual realism and pixel-level fidelity. We evaluate EFDiff under an extreme $32\times$ scale gap using a globally diverse benchmark comprising 242,416 co-registered Landsat thermal-reflectance patches. Results show that EFDiff consistently outperforms baseline methods and that cross-attention conditioning by EFM is more effective than HLS channel concatenation. Although we present EFDiff in the context of LST super-resolution, the framework is broadly applicable to remote sensing problems in which pretrained geospatial representations can guide generative reconstruction.

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