## 3 Related Works in Cloud and Cloud-Shadow Quality Layers Detection
The same considerations driven from the Sen2 Cor's spectral knowledge-based decision tree implementation for SCM generation hold for the LEDAPS, LaSRC and ATCOR spectral knowledge-based decision trees, implemented for pixel-based pre-classification as necessary-but-not-sufficient pre-condition to make inherently ill-posed atmospheric and/or topographic correction algorithms better posed for numerical solution [32; 33], see Section 2. According to [PERSON] and [PERSON] [71; 72], \"pre-classification as part of the atmospheric correction has a long history, e.g., in the NASA's processing chain for MODIS\" [57]. First, in the interdisciplinary domain of cognitive science [18; 77; 78; 79; 80; 81], it was highlighted that static (non-adaptive to data, prior knowledge-based) spectral rule-based decision trees aim at a mutually exclusive and totally exhaustive (hyper)polyhedralization of a MS reflectance (hyper)space, equivalent to (numeric) color space discretization into color names belonging to a categorical variable, known as color vocabulary [13; 14; 15]. Within the domain of cognitive science (see Figure 2), color naming was deeply investigated by linguistics. Central to this consideration is [PERSON] and [PERSON]'s landmark study of a \"universal\" inventory of eleven basic color (BC) words in twenty human languages: black, white, gray, red, orange, yellow, green, blue, purple, pink and brown [171]. Suitable for color naming of MS imagery, MS reflectance (hyper)space (hyper)polyhedralization is difficult to think of and impossible to visualize when the MS data space dimensionality is superior to three, see Figure 18. This is not the case of basic color (BC) names adopted in human languages [171], whose mutually exclusive and totally exhaustive perceptual polyhedra, neither necessarily convex nor connected, are intuitive to think of and easy to visualize in a 3D monitor-typical red-green-blue (RGB) data cube [172], see Figure 19.  
Figure 13:0 EO for Geographical Sciences (EO4 GEO, EO4 GIScience) framework, meaning EO big data analytics in operating mode for GIScience applications, constrained by 2D (retinotopic, spatial topology-preserving) image analysis in cognitive science [129]. EO4 GEO is more restrictive than the traditional GEOBIA paradigm, formalized in 2006 and 2014 as a viable alternative to 1D spatial context-insensitive (pixel-based) image analysis [9; 10; 130]. Figure 13:1 Examples of land cover (LC) class-specific families of spectral signatures [17] in top-of-atmosphere reflectance (TOARF) values, which include surface reflectance (SURF) values as a special case in clear sky and flat terrain conditions [173], i.e., in general, TOARF \(\supseteq\) SURF, where TOARF \(\approx\) SURF (depicted as an ideal “noiseless” spectral signature in red) + atmospheric and topographic noise. A within-class family of spectral signatures (e.g., dark-toned soil) in TOARF or SURF values forms a buffer zone (hyperpolyhedron, envelope, manifold, joint distribution), depicted in light green. Like a vector quantity has two characteristics, a magnitude and a direction, any LC class-specific MS manifold is characterized by a multivariate shape and a multivariate intensity information component. In the RS literature, typical prior knowledge-based spectral decision trees for MS reflectance space hyperpolyhedralization into a finite and discrete vocabulary of MS color names, such as Sen2 Cor’s [12], MAJA’s [46; 48], ATCOR’s [71; 72], LEDAPS’ [139; 140; 146] and LaSRC’s [139; 140; 160], typically adopt either a multivariate analysis of spectral indexes or a logical (AND, OR) combination of univariate variables, such as scalar spectral indexes or spectral channels, considered mutually independent. A typical spectral index is a scalar band ratio or band-pair difference equivalent to an angular coefficient of a tangent to the spectral signature in one point. It is well known that infinite functions can feature the same tangent value in one point.