Stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for Earth observation Level 2 product generation, Part 2 Validation
Andrea Baraldi, Michael Laurence Humber, Dirk Tiede, Stefan Lang

TL;DR
This study validates the effectiveness of the lightweight Satellite Image Automatic Mapper (SIAM) in generating Earth Observation Level 2 products by comparing its outputs with the U.S. NLCD 2006 map using a novel wall-to-wall quality assessment protocol.
Contribution
It introduces a new validation protocol for satellite image classification maps and demonstrates SIAM's capability to produce Level 2 Land Cover products aligned with FAO LCCS taxonomy.
Findings
SIAM maps successfully represent Level 2 Land Cover classes.
The validation protocol enables comprehensive wall-to-wall quality assessment.
SIAM's outputs are consistent with the NLCD 2006 reference map.
Abstract
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…
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Taxonomy
TopicsRemote Sensing in Agriculture · Geochemistry and Geologic Mapping · Remote Sensing and Land Use
