## 1 Introduction  
Radiometric calibration (_Cal_) is the process of transforming remote sensing (RS) sensory data, consisting of non-negative dimensionless digital numbers (DNs, where DN \(\geq\) 0), provided with no physical meaning, i.e., featuring no radiometric unit of measure, into a physical variable provided with a community-agreed radiometric unit of measure, such as top-of-atmosphere reflectance (TOARF), surface reflectance (SURF) or surface albedo values belonging to the physical domain of change 0.0-1.0 [1; 2; 3]. To cope with the five Vs characterizing _big data_ analytics, specifically, volume, variety, veracity, velocity and value [4], radiometric _Cal_ of Earth observation (EO) _big data_ is considered mandatory by the intergovernmental Group on Earth Observations (GEO)-Committee on Earth Observation Satellites (CEOS) Quality Accuracy Framework for Earth Observation (QA4 EO) Calibration/Validation (_Cal/Val_) guidelines [3]. In agreement with the visionary goal of a GEO's implementation plan for years 2005-2015 of a Global Earth Observation System of Systems (GEOSS) [5], unaccomplished to date, the ambitious goal of the GEO-CEOS QA4 EO _Cal_/_Val_ guidelines is systematic transformation of EO _big data cubes_ into timely, comprehensive and operational EO value-adding information products and services (VAPS). Despite being considered a well-known \"prerequisite for physical model-based analysis of airborne and satellite sensor measurements in the optical domain\" [1], EO data radiometric _Cal_ is largely oversighted in the RS common practice and existing literature. For example, in a pair of recent surveys about EO image classification systems published in the RS literature in years 2014 and 2016, the word \"calibration\" is absent [6; 7], whereas radiometric calibration preprocessing issues are barely mentioned in a survey dating back to year 2007 [8]. A lack of EO input data _Cal_ requirements means that statistical model-based data analytics and inductive learning-from-data algorithms are dominant in the RS community, including (geographic) object-based image analysis (GEOBIA) applications [9; 10] in the domain of geographic information science (GIScience). On the one hand, statistical model-based and inductive learning-from-data algorithms require as input DNs provided with no physical meaning. On the other hand, inductive learning-from-data algorithms are inherently semi-automatic and site-specific [2]. In practice, they require no radiometric _Cal_ data pre-processing, but they typically gain in robustness when input with radiometrically calibrated data. In compliance with the GEO-CEOS QA4 EO _Cal/Val_ requirements and with the GEO's visionary goal of a GEOSS, aiming at harmonization between missions acquiring EO data across time and geographic space, the European Space Agency (ESA) has recently defined an ESA EO Level 2 information product as follows [11; 12]:  
1. a single-date multi-spectral (MS) image, radiometrically corrected for atmospheric, adjacency and topographic effects,
2. stacked with its data-derived scene classification map (SCM), whose general-purpose, user- and application-independent thematic map legend includes quality layers cloud and cloud-shadow,
3. to be systematically generated at the ground segment, automatically (without human-machine interaction) and in near real-time. Unlike the non-standard ESA EO Level 2 SCM legend adopted by the Sentinel 2 imaging sensor-specific (atmospheric, adjacency and topographic) Correction Prototype Processor (Sen2 Cor), developed by ESA and distributed free-of-cost to be run on the user side [11; 12], see Table 1, an alternative ESA EO Level 2 SCM legend, proposed in [13; 14; 15] and shown in Table 2, consists of an \"augmented\" fully-nested 3-level 9-class Dichotomous Phase (DP) taxonomy of land cover (LC) classes in the 4D geospatial-temporalscene-domain.