## 1 Introduction
Systematic ESA EO Level 2 Information Product Generation as a Broad Context of Cloud/Cloud-Shadow Quality Layers Detection in a Cognitive Science Domain  
Systematic ESA EO Level 2 product generation at the ground segment in multi-source EO _big data cubes_[11; 12] is an inherently ill-posed CV problem [13; 23; 75; 76]; the necessary-but-not-sufficient pre-condition of this CV problem is the inherently ill-posed CV sub-problem of cloud and cloud-shadow quality layers detection. The former is regarded as a broad context where the importance and degree of complexity of the latter are highlighted. Featuring a relevant survey value in the multidisciplinary domain of cognitive science [18; 77; 78; 79; 80; 81], encompassing AI \(\supset\) CV (see Figure 2), this section provides a critical review of ESA EO Level 2 product generation strategies. Expert readers non-interested in the broad context of cloud and cloud shadow quality layers detection can skip this review section and move directly to either Section 3 in the present Part 1 or the Part 2 (proposed as Supplementary Materials) of this paper. In this review section, the Marr two lower levels of abstraction of an information processing system, identified as algorithm and implementation (see Section 1), are ignored. Rather, it focuses on the Marr (three more abstract) levels of understanding known as outcome and process requirements specification, information/knowledge representation and system design (see Section 1), because they are typically considered the cornerstone of success of an information processing system [13; 19; 76; 82; 83]. Hence, this critical review is not alternative, but complementary to surveys on EO image understanding systems typically proposed in the RS literature, such as [6; 7; 8], focused exclusively on the two lower levels of abstraction, specifically, algorithm and implementation. In more detail, among the three aforementioned surveys, EO image preprocessing requirements, such as radiometric _Cal_, atmospheric correction and topographic correction, although considered mandatory by the GEO-CEOS QA4 EO _Cal_/_Val_ guidelines [3], are totally ignored in surveys [6; 7], published in year 2014 and 2016 respectively. In contrast, EO image pre-processing issues are briefly taken into account by the third survey [8], dating back to year 2007. This observation supports the thesis that, in more recent years, when computational power has been exponentially increasing according to the Moore law of productivity [106], statistical model-based (inductive) image analysis algorithms have been dominating the RS literature, whereas physical model-based or hybrid (combined statistical and physical model-based) inference algorithms, which require as input sensory data provided with a physical meaning, specifically, EO data provided with a physical unit of radiometric measure in agreement with the GEO-CEOS QA4 EO _Cal_/_Val_ requirements [3], have been increasingly oversighted. Figure 3: Solar illumination geometries and viewpoint geometries in spaceborne and airborne EO image acquisition. To successfully cope with the five Vs of EO _big data_ analytics, specifically, volume, variety, veracity, velocity and value [4], multi-sensor analysis of multi-temporal multi-angular EO sensory data cubes depends upon the ability to distinguish between relevant changes and no-changes occurring at the Earth surface through time [68; 69]. A necessary-but-not-sufficient pre-condition for EO _big data_ transformation into timely, comprehensive and operational EO data-derived VAPS, expected by GEO to be pursued by a GEOSS never accomplished to date [5], is radiometric _Cal_, considered mandatory by the GEO-CEOS QA4 EO _Cal_/_Val_ requirements [3], but largely oversighted in the RS common practice and literature, e.g., see [6; 7]. In short, radiometric _Cal_ guarantees EO sensory data interoperability (consistency, harmonization, reconciliation, \"normalization\") across time, geographic space and sensors.