## 3 Underlying Terminology and Concepts
### Traceability and Uncertainty  
In the ideal scenario, the space-borne data and the reference validation measurements are both linked to a metrological standard, ideally up to SI units, through an unbroken chain of calibrations or comparisons, each contributing to the stated measurement uncertainty. This is the so-called \"traceability\" of the measurements, as defined in the International Vocabulary of Metrology (VIM) [38]. The traceability requirement equally applies to all satellite-based EO data and should be verified along the full processing chain from L1 to L2 bio-geophysical products. Examples of visualised traceability chains for various land and atmosphere ECVs [39] were generated as part of the Quality Assurance for Essential Climate Variables (QA4 ECV) project [40]. In practice, both the satellite and the reference data are rarely fully traceable, since rigorous calibrations performed in well-controlled laboratory conditions cannot be replicated in the field or in space. In addition, there is a lack of a consistent and metrologically-sound vocabulary within the EO community to describe the data and their quality [41]. The most obvious example are the terms \"error\" and \"uncertainty\", which are often misused or used interchangeably. Within this paper, we follow VIM recommended definition, namely [38]:  
* Measurement error is the measured quantity value minus a reference quantity value. The measurement error can contain both a random and a systematic component. While the random component averages out over multiple measurements, the systematic component does not. On the other hand, the systematic error, also called bias, if significant in size compared to required measurement accuracy and should be carefully estimated and ideally corrected through the use of an appropriate correction factor. * Measurement uncertainty is a non-negative parameter describing the dispersion of the quantity values attributed to a measurand, where the measurand is the quantity being measured. The methods for evaluating measurement uncertainty are classified in two main categories: Type A and Type B. Type A uncertainty evaluation is based on the statistical analysis of a series of independent observations of the same quantity. Type B includes all other methods, which do not rely on the analysis of a series of observations but are based on a prior and reliable knowledge about the measurement, e.g., previous data, knowledge about the instrument's properties and behaviour and calibration certificates. Both error and uncertainty are intrinsic properties of any measurement, which is, by definition, an imperfect estimation of the true value of the measurand. Any satellite-based EO data should therefore be provided as a bias-free measurement, i.e., corrected for known systematic errors (e.g., radiometric or geometric), and with an associated estimate of its uncertainty. For optical land imaging sensors, this requirement results in the need of providing uncertainty per-measurand, namely per-pixel. This information should be provided already at Level-1 and propagated along the full processing chain up to the derived Level-2 bio-geophysical products, following the Guide to the expression of Uncertainty in Measurement (GUM) [42]. The rigorous application of metrological practices to EO fails at this initial stage since none of the currently provided ESA operational Level-1 products of Table 1 have an associated per-pixel uncertainty. This is due to the complexity in rigorously estimating the uncertainties at pixel level and the lack of uncertainty estimates in the used ancillary data, such as pre-flight calibration data or meteorological data. Another key challenge consists in the quantitative assessment of the pixel-to-pixel correlation. Furthermore, the provision of per-pixel uncertainty has an obvious non-negligible impact on the product's size, which cannot be easily accommodated in the frame of an operational Ground Segment infrastructure.