## 1 Introduction
LC data, for example, are used to assess the progress toward the UN Sustainable Development Goals (SDGs) targets [13], such as target 15.3 which relates to achieving land degradation neutrality (LDN) by 2030 [14]. Despite the importance of LC data in environmental monitoring and planning, the number of accessible national products is limited and their qualities are not always appropriate. The EAGLE idea was built on a clear difference between land cover and land use, and it is described in a matrix consisting of three descriptors: land cover components (LCC), land use attributes (LUA) and additional characteristics (LCH). The descriptors can be merged to develop unique categorization systems for various needs or to detect correspondences with existing classes while maintaining the independence of the three descriptors [15]. There are numerous algorithms for examining land cover, starting with the classification of satellite images. The most appropriate approach is determined by factors such as the type of data, class distribution, research interests and classifier interpretability, as well as the balance between the objectives and the available resources. In general, the automatic classification systems are more time consuming as the data dimension and volume grow and data interpretation might become problematic at times [16]. Supervised classifications may be used to analyze large amounts of data as they are based on selecting a sufficient number of training samples with known values [17], which are then used to predict unknown values in the testing data [18]. As a result, it's critical to choose examples that fully depict the diversity of the studied territory's characteristics. Nowadays, the CORINE Land Cover (CLC) ensures a high level of thematic detail and a lengthy historical series, but it is limited in terms of geographical detail and updating frequency ([[https://land.copernicus.eu/pan-european/corine-land-cover](https://land.copernicus.eu/pan-european/corine-land-cover)]([https://land.copernicus.eu/pan-european/corine-land-cover](https://land.copernicus.eu/pan-european/corine-land-cover)), viewed on 6 November 2022). In recent years, the high-resolution layers (HRLs) made it possible to describe the principal land cover classes in high spatial detail while maintaining a multi-year update frequency. However, the low accuracies in the classification of alpine areas still persist [19]. At the same time, Copernicus national-scale products are still available with high thematic and spatial depth, including Urban Atlas, Riparian Zones, Coastal Zones and Natura 2000, but these are only for specific locations. The most recent official product is the CORINE LAND COVER v.2018 (CLC). However, this product does not permit a detailed mapping of the territory especially in alpine areas [20]. Nevertheless, in recent years, some institutions, academic centers and private enterprises have tried to overcome the spatial resolution issue by creating prototypal products at the national level by adopting Copernicus EO Data [21; 22]. New evidence is represented by the 10 m land cover produced by ESRI on a global scale with 10 classes even if there are strong limitations and errors in the alpine area due to the absence of a local confusion matrix [23]. Another example linked to the Italian context is the prototypal LC of the whole Italian territory performed by the ISPRA for the year 2018. This LC proposes a methodology with the joint use of the optical, multispectral and radar data of Sentinel 1 and Sentinel 2 [12]. However, following the choice of the adopted input data and the need to map an entire territory, it has strong limits in mountainous areas. Compared to the validation set, these areas are low and are not adequate for mapping mountainous territories in detail as suggested by [19]. As previously mentioned, a robust local EO service only based on satellite remote sensing data can only map land cover (hereinafter called LC) with a higher accuracy.