## 1 Introduction  
Recent years have seen an unprecedented growth of space-borne Earth Observation (EO) optical land imaging systems at ever increasing spatial, temporal and spectral resolutions. The advent of the New Space era, with the launch of a multitude of small satellite constellations, further contributes to expanding our ability of monitoring Earth's system processes at enhanced spatiotemporal scales. Similarly, a fast-growing number of automated cost-effective sensors provides a unique opportunity for gathering valuable in situ data for the validation of satellite-derived bio-geophysical products. All in all, our current capability in generating EO data, both from space and from ground-based or drone-based platforms, far outpace our ability to synergistically exploit their measurements. To get the most out of such a deluge of EO data in the years to come, we are faced with two major challenges: diversity and quality. Diversity may result, if properly handled, in enhanced observation capabilities by combining complementary observations acquired by a variety of heterogeneous sensors. Quality translates in this context to the need of ensuring that the relevant EO data is provided to the users with exhaustive and fully traceable information about its quality, allowing them to readily evaluate the fitness for purpose for their applications [1]. As long as EO data quality is assessed with metrological rigour and using community-endorsed best practices, the measurements acquired by heterogeneous sensors can be reliably combined, meaning that the corresponding observing systems are interoperable. Interoperability can be tackled at different levels of complexity, ranging from format definition, products content and metadata, data processing algorithms and quality assurance approaches. Yet, in general terms, two systems can be considered interoperable when their derived EO data come with all the necessary quality information, including a detailed uncertainty estimate, allowing to fully characterise, and eventually correct for, the systematic errors inherent in each system. The interoperability concept is tackled within this paper from a Space Agency's perspective, meaning in terms of end-to-end system performances. The overall quality assurance process is therefore reviewed and discussed, starting in the early phase of the mission, with the pre-launch characterisation activities, and continuing while in-orbit, with the calibration and validation activities (Cal/Val). Cal/Val and metrology are the two essential elements in order to assess inconsistencies between sensors and consequently address the potential for interoperability. Cal/Val activities provide the reference measurements and the relevant methods for quantitatively estimating the quality (uncertainty) of satellite EO products. The metrology component ensures the generic framework and the internationally agreed standards, ideally traceable to SI (International System of units), to tie the EO data acquired by a variety of sensors to a common reference. The advances and standardisation of Cal/Val methodologies and the implementation of metrological practices are therefore crucial for enabling an informative exploitation of the various EO remote sensing systems, eventually reaching the objective of a global System-of-Systems [2]. This System-of-Systems vision equally applies to in situ reference data, which are used to validate the satellite-based products. This ideal condition is far from being attained for the currently available land validation systems. As a matter of fact, the existing in situ validation data from field campaigns and networks still lack consistency and proper traceability to metrological standards, which limits their integrated use for validation purposes. The harmonisation of best practices across existing ground-based networks would significantly enhance our ability to validate satellite-based EO data in terms of spatiotemporal and geographical coverage.