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Landsat Thermal

Landsat two-band thermal-infrared at 25 m Fidelity Reconstruction (FR) — surface temperature and thermal anomalies.

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Landsat's two thermal-infrared bands, reconstructed to 25 m — surface temperature and thermal anomalies at a scale the ~100 m native source can't resolve.

Landsat's thermal-infrared instrument (TIRS) senses emitted heat in two bands, but at ~100 m it is too coarse for field- or facility-scale work. ETD applies Fidelity Reconstruction to both thermal bands, delivering 25 m surface-temperature and anomaly imagery that stays bounded to the real thermal evidence. It supports urban-heat mapping, irrigation and evapotranspiration, water-temperature and industrial-thermal monitoring.

What is Fidelity Reconstruction (FR)? Fidelity Reconstruction (FR) is a non-generative super-resolver: it recovers only the detail the input actually implies — resolving and sharpening real structure without inventing any — so its output downscales back to the source almost exactly. Where a GAN hallucinates plausible-but-fabricated texture, FR stays evidence-bounded.

Fidelity Reconstruction is a reconstructive, non-hallucinating approach to super-resolution. Rather than generating a plausible high-resolution image the way adversarial (GAN) upscalers do, an FR model performs a learned, information-bounded reconstruction: it recovers high-frequency structure that is genuinely implied by — aliased or blurred within — the input, and refines edges and fine geometry, but it does not synthesize novel content (textures, objects, or detail) that the source cannot support.

The defining, measurable property is downscale-consistency: when an FR output is reduced back to the input resolution, it returns the input almost exactly (in our tests ~56 dB PSNR), because every pixel is derived from real evidence. Generative upscalers fail this test (~30 dB) precisely because they add content that isn't in the source — content that looks sharp but is fabricated. That distinction is what makes FR appropriate for measurement and monitoring, where an invented building, road, or texture would be an analytical liability, not a cosmetic bonus.

The deliberate trade-off is that FR will not manufacture the “photographic crispness” a GAN conjures; its ceiling is the true information content of the input (and the resolution its training targets taught it to resolve). In exchange, its output is trustworthy and reproducible — sharper and higher-resolution than the source, but faithful to it — which is the correct guarantee for a satellite-analytics product.

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