## 1 Introduction
To overcome structural limitations in system design of existing open source or commercial software products for EO image radiometric _Cal_ (correction, in general), such as Sen2 Cor, ATCOR, LEDAPS and LaSRC, a viable alternative is an inherently ill-posed atmospheric, topographic, adjacency and BRDF effect correction system architecture sketched as in Figure 12[13; 82; 83], to be considered as the third original contribution of this review section. The estimation from an input numeric variable, starting from EO sensory data equivalent to DNs provided with no physical meaning, of an output numeric variable provided with a physical meaning of increasing quality, such as EO data-derived TOARF, SURF and spectral albedo values featuring increasing levels of radiometric _Cal_ quality, requires as additional input, at each stage of the EO image enhancement flow chart, a categorical (nominal) variable belonging to a preliminary SCM automatically generated from the EO image radiometrically corrected at the previous stage in the workflow. In other words, equivalent to two sides of the same coin, categorical variables (e.g., SCMs at increasing levels of mapping accuracy and semantics) and continuous variables (e.g., TOARF, SURF and spectral albedo values) should be estimated from raw EO imagery (coded as dimensionless DNs, provided with no physical meaning) _alternately and hierarchically_[13; 107; 147; 148; 149]. It means that, in an EO image pre-processing workflow conceived of as a hierarchical hybrid inference system, such as the stratified topographic correction (STRATCOR) algorithm proposed in [13; 107], categorical variables of hierarchically increasing quality in semantics and accuracy are estimated from (hierarchically enhanced) continuous variables, where _a priori_ knowledge is required in addition to data to make an inherently ill-posed data classification problem better posed for numerical solution [13; 118; 82; 83; 83]. This hierarchical classification approach alternates with a numeric variable estimation stage of hierarchically increasing radiometric quality, which is conducted on a categorical (stratified, masked, class-conditional) basis, where data stratification is required to make the inherently ill-posed radiometric correction stage, either atmospheric, topographic, adjacency or BRDF effect correction, better posed for numeric solution [13; 107; 82; 83; 83], see Figure 12.  
Figure 12: Ideal ESA EO Level 2 product generation design as a hierarchical alternating sequence of: (A) hybrid (combined deductive and inductive) radiometric enhancement of multi-spectral (MS) dimensionless digital numbers (DNs) into top-of-atmosphere reflectance (TOARF), surface reflectance (SURF) values and spectral albedo values corrected in sequence for (1) atmospheric, (2) adjacency, (3) topographic and (4) BRDF effects, and (B) hybrid (combined deductive and inductive) classification of TOARF, SURF and spectral albedo values into a sequence of ESA EO Level 2 scene classification maps (SCMs), whose legend (taxonomy) of community-agreed land cover (LC) class names, in addition to quality layers cloud and cloud–shadow, increases hierarchically in semantics and mapping accuracy. An implementation in operating mode of this EO image pre-processing system design for stratified topographic correction (STRATCOR) is presented and discussed in [13; 82; 83; 107]. In comparison with  
this desirable system design, let us consider that, for example, the existing Sen2 Cor software toolbox, developed by ESA to support a Sentinel-2 sensor-specific Level 2 product generation on the user side [11; 12; 44], adopts no hierarchical alternating approach between MS image classification and MS image radiometric enhancement. Rather, ESA Sen2 Cor accomplishes, first, one SCM generation from TOARF values based on a per-pixel (spatial context-insensitive) prior spectral knowledge-based decision tree.