# Copernicus Global Land Cover Layers--Collection 2
## 2 Methodological Overview
### Classification/Regression Pre-Processing  
A next step is the calculation of the metrics, which are used as general descriptors to enable distinguishing the different LC classes. The PROBA-V UTM ARD++ archive is used as input for this metrics generation step. Instead of using the original reflectance values, more descriptive Vegetation Indices (VI's) are used (Figure 1). Overall ten VI's are generated using the four reflectance bands of PROBA-V, in detail: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Structure Intensive Pigment Index (SIPI), Normalized Difference Moister Index (NDMI), Near-Infrared reflectance of vegetation (NIRv), Angle at NIR, HUE, and VALUE of the Hue Saturation Value (HSV) color system transformation, Area Under the Curve (AUC), and Normalized Area Under the Curve (NAUC) (for detailed VI's definition see ATBD [25]). Since the whole time series stack of the VI's for an epoch (three years period giving reference year +/- 1 year) would be too many proxies to be used in supervised learning, the time dimension in the data stack was condensed. Temporal descriptors were derived through a harmonic model, fitted through the time steps of each of the VI's based on a Fourier transformation [30; 32]. In addition to the seven parameters of the harmonic model using three frequencies above the zero-frequency, used as metrics for the overall level and seasonality of the time series, 11 descriptive statistics (mean, standard deviation, minimum, maximum, sum, median, 10 th percentile, 90 th percentile, 10 th - 90 th percentile range, time step of the first minimum appearance, and time step of the first maximum appearance) and one textural metrics (median variation of center pixel to median of the neighbors) were generated for each VI. Additionally, four external metrics including the height, slope, aspect, and purity derived at 100 m were derived from a Digital Elevation Model (DEM). Overall, 270 metrics were extracted from the PROBA-V UTM ARD++ archive for the 2015 epoch which includes spectral, temporal, and spatial features as suggested by [PERSON] et al. [18] and [PERSON] et al. [19]. In addition to the input EO data, other data sources were needed as well. Prior to using these external ancillary data, they were checked for consistency and if needed, re-warped to the UTM coordinate system, resampled to 100 m, retiled into the Sentinel-2 tiling grid, and post-processed to usable ancillary data products (Figure 1). Since water surfaces and built-up surfaces are not classified in the CGLS-LC100 algorithm but fused in from these ancillary data sets, the most important ancillary dataset in the CGLS-LC100 processing line is JRC's Global Surface Water dataset [35] and DLR's World Settlement Footprint (WSF) [36]. Training data has been collected through the Geo-Wiki engagement platform by a group of 20 experts trained by IIASA staff [37]. Note that the training data collection is not through the use of volunteer efforts but instead by a group of highly trained experts involved in the data collection. Therefore, a specific branch of Geo-Wiki, which is only accessible to the experts, has been consolidated for collecting training data at the required resolution and grid. The training data was collected via a 10x10 grid at 10 m spatial resolution through manual classification using Google Maps and Microsoft Bing images. Consequently, the training data not only includes the land cover type, but also the cover fractions of the main LC classes per 100 m resolution cell. Such fractions can be easily translated into different legends. Here, we used LC definitions as suggested by the United Nations (UN) Land Cover Classification System (LCCS) [38]. Overall, 141,000 unique 100x100 m training locations were used in the CGLS-LC100 Collection 2 workflow (see Figure 2 for the geographical distribution of the training data).