Structured errors are a concept introduced to characterise certain errors in satellite imagery. Structured errors arise from effects that influence more than one measured value in the image, but are not in common across the whole image. The originating effect may be random (aleatoric) or systematic (but acting on a subset or locality of pixels), but in either case the resulting errors are not independent, and may even be perfectly correlated across the affected pixels. Since the sensitivity of different pixels/channels to the originating effect may differ, even if there is perfect error correlation, the error (and associated uncertainty) in the measured value can differ in magnitude. Structured errors are therefore complex, and, at the same time, important to understand, because their error correlation properties affect how uncertainty propagates to higher-level data. The uncertainty from structured effects (or, loosely, structured uncertainty) is the part of the uncertainty contributed by structured errors. A structured random effect would refer to an effect that is unpredictable in terms of origin while leading to a predictable pattern of correlated errors across measured values in an image.
An example of a structured random effect is the impact of a random error in the measurement of signal while viewing a calibration target, which causes unpredictable but inter-related errors in all measured values which use that calibration cycle.