Abstract
ESA defines as Earth Observation (EO) Level 2 information product a multi-spectral (MS) image corrected for atmospheric, adjacency, and topographic effects, stacked with its data-derived scene classification map (SCM), whose legend includes quality layers cloud and cloud-shadow. No ESA EO Level 2 product has ever been systematically generated at the ground segment. To fill the information gap from EO big data to ESA EO Level 2 product in compliance with the GEO-CEOS stage 4 validation (Val) guidelines, an off-the-shelf Satellite Image Automatic Mapper (SIAM) lightweight computer program was selected to be validated by independent means on an annual 30 m resolution Web-Enabled Landsat Data (WELD) image composite time-series of the conterminous U.S. (CONUS) for the years 2006 to 2009. The SIAM core is a prior knowledge-based decision tree for MS reflectance space hyperpolyhedralization into static (non-adaptive to data) color names. For the sake of readability, this paper was split into two. The present Part 2—Validation—accomplishes a GEO-CEOS stage 4 Val of the test SIAM-WELD annual map time-series in comparison with a reference 30 m resolution 16-class USGS National Land Cover Data (NLCD) 2006 map. These test and reference map pairs feature the same spatial resolution and spatial extent, but their legends differ and must be harmonized, in agreement with the previous Part 1 - Theory. Conclusions are that SIAM systematically delivers an ESA EO Level 2 SCM product instantiation whose legend complies with the standard 2-level 4-class FAO Land Cover Classification System (LCCS) Dichotomous Phase (DP) taxonomy.
Highlights
Proposed by the intergovernmental Group on Earth Observations (GEO) and the Committee on Earth Observation Satellites (CEOS), the implementation plan for years 2005–2015 of the Global Earth Observation System of Systems (GEOSS) aimed at systematic transformation of multi-source Earth observation (EO) big data (IBM, 2016; Yang, Huang, Li, Liu, & Hu, 2017) into timely, comprehensive, and operational EO value-adding products and services (GEO, 2005), submitted to the GEO-CEOS Quality Assurance Framework for Earth Observation (QA4EO) calibration/validation (Cal/ Val) requirements (Group on Earth Observation/Committee on Earth Observation Satellites (GEO/ CEOS), 2010)
Provided with a relevant survey value, the Part 1 of this paper reviewed a long history of prior knowledge-based MS reflectance space partitioners for static color naming developed by the remote sensing (RS) community for use in hybrid EO image understanding systems (EO-IUSs) for EO image enhancement and classification tasks in operating mode, but never validated in compliance with the GEO-CEOS QA4EO Cal/Val requirements
In agreement with the definition of an information processing system in operating mode proposed in this work, the off-the-shelf Satellite Image Automatic Mapper (SIAM) software executable submitted to a GEO-CEOS stage 4 Val can be considered in operating mode because its whole set of outcome and process (OP)-Q2I estimates scored “high.” Second, the off-the-shelf SIAM lightweight computer program in operating mode can be considered suitable for systematic generation of an European Space Agency (ESA) EO Level 2 Scene Classification Map (SCM) product instantiation whose legend agrees with the standard 2-level 4class Food and Agriculture Organization (FAO) Land Cover Classification System (LCCS)-Dichotomous Phase (DP) taxonomy, preliminary to an “augmented” 3-level 9-class FAO LCCS-DP taxonomy, defined as a standard 3-level 8-class FAO LCCD-DP legend, see Figure 1, augmented with class “Others,” which includes quality layers cloud and cloud-shadow
Summary
Proposed by the intergovernmental Group on Earth Observations (GEO) and the Committee on Earth Observation Satellites (CEOS), the implementation plan for years 2005–2015 of the Global Earth Observation System of Systems (GEOSS) aimed at systematic transformation of multi-source Earth observation (EO) big data (IBM, 2016; Yang, Huang, Li, Liu, & Hu, 2017) into timely, comprehensive, and operational EO value-adding products and services (GEO, 2005), submitted to the GEO-CEOS Quality Assurance Framework for Earth Observation (QA4EO) calibration/validation (Cal/ Val) requirements (Group on Earth Observation/Committee on Earth Observation Satellites (GEO/ CEOS), 2010). To contribute toward the visionary goal of GEOSS, this interdisciplinary work aimed at filling an analytic and pragmatic information gap from EO big sensory data to systematic European Space Agency (ESA) EO Level 2 information product generation ESA defines as EO Level 2 information product: (i) a single-date multi-spectral (MS) image, radiometrically calibrated into surface reflectance (SURF) values corrected for atmospheric, adjacency, and topographic effects, in compliance with the GEO-CEOS QA4EO Cal requirements (GEO-CEOS, 2010), stacked with (ii) its data-derived Scene Classification Map (SCM), whose legend includes quality layers cloud and cloud-shadow (ESA, 2015; DLR & VEGA, 2011; CNES, 2015). The principle of statistic stratification guarantees that “stratification will always achieve greater precision provided that the strata have been chosen so that members of the same stratum are as similar as possible in respect of the characteristic of interest” (Hunt & Tyrrell, 2012)
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