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The European Space Agency (ESA) defines an Earth Observation (EO) Level 2product as a multispectral (MS) image corrected for geometric, atmospheric,adjacency and topographic effects, stacked with its scene classification map(SCM), whose legend includes quality layers such as cloud and cloud-shadow. NoESA EO Level 2 product has ever been systematically generated at the groundsegment. To contribute toward filling an information gap from EO big data tothe ESA EO Level 2 product, an original Stage 4 validation (Val) of theSatellite Image Automatic Mapper (SIAM) lightweight computer program wasconducted by independent means on an annual Web-Enabled Landsat Data (WELD)image composite time-series of the conterminous U.S. The core of SIAM is a onepass prior knowledge based decision tree for MS reflectance spacehyperpolyhedralization into static color names presented in literature inrecent years. For the sake of readability this paper is split into two. Thepresent Part 1 Theory provides the multidisciplinary background of a prioricolor naming in cognitive science, from linguistics to computer vision. To copewith dictionaries of MS color names and land cover class names that do notcoincide and must be harmonized, an original hybrid guideline is proposed toidentify a categorical variable pair relationship. An original quantitativemeasure of categorical variable pair association is also proposed. Thesubsequent Part 2 Validation discusses Stage 4 Val results collected by anoriginal protocol for wall-to-wall thematic map quality assessment withoutsampling where the test and reference map legends can differ. Conclusions arethat the SIAM-WELD maps instantiate a Level 2 SCM product whose legend is the 4class taxonomy of the FAO Land Cover Classification System at the DichotomousPhase Level 1 vegetation/nonvegetation and Level 2 terrestrial/aquatic.

Stage 4 validation of the Satellite Image Automatic Mapper lightweight  computer program for Earth observation Level 2 product generation, Part 1  Theory