Scene normalisation is a process which attempts to remove some of the variance seen in EO data to create values that are independent of view angle or atmospheric state or observing time etc. to give a uniform measure of a given variable across an image. It can be considered as a method to give what would have been observed by the same instrument under viewing identical conditions.
Sustained Coordinated Processing of Environmental Satellite Data for Climate Monitoring
Some Level 1 correction schemes involve determining corrections to already calibrated radiances based on some defined reference. These sensor bias corrections can then be applied to correct for gross errors in the original calibration. One example of this are the corrections provided by the GSICS (Global Space-based Inter-Calibration System) consortium for a number of sensors. Note that the terms “Harmonisation” and “Homogenisation” can be applied to this form of correction.
Spinning Enhanced Visible Infra-Red Imager
International sytem of units
SI-Traceability is traceability where the “stated metrological reference” is formally calibrated within the International System of Units (SI) through a National Metrology Institute that participates in the Mutual Recognition Arrangement and whose measurement for this parameter is thus audited through formal international comparison and peer review.
Sea and Land Surface Temperature Radiometer
Simultaneous Nadir Overpass – a location on the planet where the nadir tracks of two satellites intersect within a given spatial and time distance. Both distance measures can vary, depending on the context. This is a special case of a match up as defined above.
Spectral Response Function
SEVIRI Solar Channel Calibration
Special Sensor Microwave/Temperature-2
Sea Surface Temperature
Standard uncertainty describes the standard deviation of the probability distribution describing the spread of possible values.
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.
This term refers to the data that a satellite collects by scanning the area below its current location, i.e., the swath or the width of this area perpendicular to the satellite’s flight direction.
Systematic errors are those that could in principle be corrected for if we had sufficient information to do so: that is, they arise from unknowns that could in principle be estimated; they are epistemic rather than aleatoric. Systematic errors influence many measured values, including, but are not limited to, effects that give rise to constant error for a significant proportion of a satellite mission—i.e., biases, for which the structure is a simple error in common. In terms of correlation properties across an image, therefore, effects that are systematic in origin give rise to either structured or common errors.
Systematic errors therefore “average out” slowly or not at all across many measured values; systematic effects may be operating at the same time as other types of effect, in which case only a component of the total error is systematic; an example of a systematic effect is a mis-characterised calibration target.
Effects for a particular measurement process that do not vary (or vary coherently) from (one set of) measurement(s) to (another set of) measurement(s) and therefore produce systematic errors that cannot be reduced by averaging.