Future satellite missions such as NASA's CLARREO Pathfinder will, for the first time, allow radiometric calibration that is traceable to SI metrological standards. This raises a fundamental question for any remote‑sensing activity: if the measurements reach metrological quality, can the processing algorithms keep up—or do they throw away that precision because uncertainty is not properly propagated?
Expressing EO algorithm logic in a differentiation‑enabled framework automatically tracks how uncertainties in inputs, calibration, and model parameters affect the final products, operating directly on images and data cubes with their spatial and temporal correlations intact. Jacobians and covariance tensors become operational data inside the algorithms, not external reports.
Such a framework makes it possible to deliver products whose uncertainty is consistent with SI‑traceable measurements, supporting regulatory‑grade use, high‑value decision making, and sensor‑to‑sensor consistency. Strategically, it positions providers to offer truly uncertainty‑aware services that fully exploit upcoming metrological missions, instead of being limited by legacy ideas that ignore metrology.
Everyone wants explainable AI, but most “ML” in Earth Observation still remains a decoupled black box that ignores the physics we already know.
Our idea is to flip that around: start from existing, physics‑based EO algorithms and express their logic in a differentiation‑enabled framework. Algorithmic differentiation then provides exact sensitivities of every output to every input and parameter, directly from the real code. Jacobians and covariance tensors become operational data inside the algorithms, so you can see and quantify how the physics drives the predictions and how uncertainty propagates through each step.
Bringing physics‑based equations into a differentiation‑enabled framework creates a natural bridge between physics and machine learning: physical models stay in charge of structure and constraints, while learned components fill in what the physics does not capture, all within one differentiable, uncertainty‑aware program. The result is a class of physics‑informed ML systems for EO that are both high‑performance and inherently explainable, because their behaviour is rooted in—and analysable through—the underlying physics.