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# Framing: metrology and uncertainty
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Future satellite missions such as TRUTHS and CLARREO will, for the
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first time, allow radiometric calibration that is traceable to SI
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metrological standards. This raises a fundamental question for any
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remote‑sensing activity: if the measurements reach metrological
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quality, can the processing algorithms keep up—or do they throw away
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that precision because uncertainty is not properly propagated?
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Future satellite missions such as ESA [TRUTHS](https://www.esa.int/Applications/Observing_the_Earth/TRUTHS)
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and NASA [CLARREO Pathfinder](https://science.nasa.gov/mission/clarreo-pathfinder/)
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will, for the first time, allow radiometric calibration that is
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traceable to SI metrological standards. This raises a fundamental
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question for any remote‑sensing activity: if the measurements reach
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metrological quality, can the processing algorithms keep up—or do
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they throw away that precision because uncertainty is not properly
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propagated?
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Expressing EO algorithm logic in a differentiation‑enabled framework
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automatically tracks how uncertainties in inputs, calibration, and

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