## 3 Underlying Terminology and Concepts
### Spatiotemporal Sampling  
The representativeness, i.e., the extent to which a set of measurements taken in a given space-time domain reflect the actual conditions in the same or different space-time domain [50], is a fundamental issue in land product validation, owing to the inherent spatial and geographical heterogeneity and temporal variability of terrestrial surfaces. In order to ensure a good representativeness and high accuracy of the validation dataset, the following aspects should be carefully considered. * Reference measurement--Since the truth is, strictly speaking, unknown, the general validation process consists in assessing the accuracy of the satellite data against a chosen reference measurement. The basic assumption is that the uncertainty associated to the reference is better constrained than the one of the satellite data. For instance, ground-based sensors can be regularly calibrated (pre- and post-deployment) and closely characterised in laboratory conditions, which is not the case in satellite-borne sensors. In the ideal case, the reference measurement should be traceable to SI unit. In reality, reference data are rarely traceable to a standard and can be affected by various sources of uncertainties, which are very often neglected. * Spatial sampling--The ideal reference data should provide a meaningful estimate of the investigated geophysical quantity to a level, which is comparable to the satellite measurement scale that is subject of the validation process. This generic requirement drives the choice of the validation sites and the location of in situ observations within each site (spatial sampling). The adoption of community-agreed best practices for in situ data collection allows to address this generic issue in satellite validation. In the land community, and specifically for the validation of bio-geophysical products, the most widely used approach consists in repeating the in situ measurements over the representative sampling unit, the so-called Elementary Sampling Unit (ESU), and upscaling the data averaged over this ESU to a larger scale using empirical transfer functions, based on a combination of high and coarse resolution sensors [51]. * Temporal sampling--In the ideal case, Cal/Val data should be acquired continuously, such as by using automated devices, to increase the number of collocation opportunities (match-ups) with satellite overpasses, as well as to enhance the monitoring of dynamic bio-geophysical processes over the considered ecosystem. The increase of match-up opportunities is essential for improving the reliability of the derived validation results by providing more robust statistical metrics. In reality, a large fraction of available Cal/Val data for land are gathered during ad-hoc field campaigns with limited temporal sampling. * Spatiotemporal mismatch--Despite the effort in defining an optimised sampling in the spatiotemporal domain, the intrinsic difference between the measurement scale for the satellite and the reference in situ data directly translates into a collocation mis-match error (\(\Sigma\) in Equation (2)), which can be the most significant source of errors for highly dynamic geophysical parameters [48]. For the specific case of terrestrial variables, the temporal mismatch is a second order issue with respect to the spatial sampling, since surface properties change slowly with time. On the other hand, the short-term variability in aerosol and cloud contamination can induce a mismatch temporal collocation error in the derived land bio-geophysical parameters. * Geographical coverage--The uncertainty budget of satellite EO data should ideally be assessed at global scale in order to verify the accuracy of the used retrieval algorithms over a wide range of biomes and eco-climate conditions. This translates into the need of a network of globally distributed sites, providing continuous, representative and consistent measurements of the same geophysical quantity.