  The European Space Agency (ESA) defines an Earth Observation (EO) Level 2
product 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. No
ESA EO Level 2 product has ever been systematically generated at the ground
segment. To contribute toward filling an information gap from EO big sensory
data to the ESA EO Level 2 product, a Stage 4 validation (Val) of an off the
shelf Satellite Image Automatic Mapper (SIAM) lightweight computer program for
prior knowledge based MS color naming was conducted by independent means. A
time-series of annual Web Enabled Landsat Data (WELD) image composites of the
conterminous U.S. (CONUS) was selected as input dataset. The annual SIAM WELD
maps of the CONUS were validated in comparison with the U.S. National Land
Cover Data (NLCD) 2006 map. These test and reference maps share the same
spatial resolution and spatial extent, but their map legends are not the same
and must be harmonized. For the sake of readability this paper is split into
two. The previous Part 1 Theory provided the multidisciplinary background of a
priori color naming. The present Part 2 Validation presents and discusses Stage
4 Val results collected from the test SIAM WELD map time series and the
reference NLCD map by an original protocol for wall to wall thematic map
quality assessment without sampling, where the test and reference map legends
can differ in agreement with the Part 1. Conclusions are that the SIAM-WELD
maps instantiate a Level 2 SCM product whose legend is the FAO Land Cover
Classification System (LCCS) taxonomy at the Dichotomous Phase (DP) Level 1
vegetation/nonvegetation, Level 2 terrestrial/aquatic or superior LCCS level.
