# Copernicus Global Land Cover Layers--Collection 2
## 3 The CGLS-LC100 Product and Accuracy Assessment
### Accuracy Assessment  
A full validation report for the CGLS-LC100 Collection 2 product is available for download [39]. It includes a detailed description of the validation sampling design, sampling data collection, and methods regarding the accuracy assessment including detailed confusion matrixes on global and continental level for classification levels 1 and 2. We report here only a short summary of the results. Table 1 shows the confusion matrix for the discrete layer at classification level 1 (base classes) of the CGLS-LC100 Collection 2 product at a global scale. The overall accuracy is 80.2% +/-0.7% (confidence intervals at 95% confidence level). In terms of class-specific accuracies, forest, bare/sparse vegetation, snow/ice, and permanent water are mapped with very high accuracies (>85%). The class accuracies of herbaceous vegetation, croplands and built-up are moderate (70%-85%). Built-up class has a higher confusion error with croplands, forest, and herbaceous vegetation. While herbaceous wetlands, lichen/moss, and shrubs have lower-class accuracies (<65%). Specifically, herbaceous wetlands have high confusion errors with herbaceous vegetation and shrubs have high confusion errors with forest and herbaceous vegetation classes which are expected considering the spectral similarity of these classes. Lichen/moss class has higher confusion errors with herbaceous vegetation. All the class accuracies are estimated with high confidence, having confidence intervals less than 8%. The overall accuracies for the discrete layer at classification level 1 of the CGLS-LC100 Collection 2 product were also calculated for each continent (Table 2). All continents are mapped with overall accuracies around 80% with the lowest at 77.1% for North America and the highest at 83.3 % for Asia. See the full validation report [39] for in-depth confusion matrices per continent at classification level 1 as well as for detailed validation on classification level 2 (separating open and closed forests). Table 3 lists the mean absolute error (MAE) and root mean square error (RMSE) for the fractional cover layers of the CGLS-LC100 Collection 2 product at a global scale. Rare classes, such as snow/ ice, water, and built-up area, show the lowest errors with MAE of maximum 0.8% and RMSE lower than 6%. Followed by lichen/moss, crops and bare/sparse vegetation cover fraction layers with an MAE  
\(\sim\)5% and RMSE \(\sim\)15%. Higher MAE and RMSE (MAE \(\sim\)9%, RMSE \(\sim\)17%) are viewable in the tree and shrubland cover fraction layers. Herbaceous vegetation fraction product has the highest error with MAE of 17.3% and RMSE of 28.1%. This can be due to difficulty in separating herbaceous vegetation from other LC types. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & **Number of Samples** & **Overall Accuracy (\%)** & **Confidence Intervals \(\pm\)** \\ \hline
**Africa** & 3616 & 80.1 & 2.0 \\ \hline
**Asia** & 3071 & 83.3 & 1.5 \\ \hline
**Northern Eurasia** & 2976 & 79.8 & 1.6 \\ \hline
**Europe** & 3120 & 80.4 & 1.6 \\ \hline
**North America** & 2843 & 77.1 & 1.7 \\ \hline
**Oceania \& Australia** & 2951 & 81.9 & 1.9 \\ \hline
**South America** & 3017 & 79.6 & 1.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overall classification accuracy of the discrete layer at classification level 1 of the CGLS-LC100 collection 2 product at continental scale. Adapted from [39].