## 1 Introduction  
Early-season crop mapping is valuable for estimating the area under cultivation and for yield prediction [1], which is vital information for marketing and decision making involved in food security. Moreover, accurate and detailed in terms of number of crop species, crop mapping is important for agricultural management, economic development planning, and agroecosystem conservation. Precise crop maps could be used for analyzing the implementation and effects of agri-environmental policies [2] and planning of optimal and sustainable agronomic management based on crop rotations [3]. National and international institutions require an early estimation of the planted area for food security issues [4]. Parallelly, there is increasing interest from crop insurance companies in precise early crop mapping and weather-based crop health indicators to be included in their area-yield crop insurance schemes, especially in the early season when the vegetation cover is low and fields do not show a dense homogeneous canopy. Time series-based analysis of multispectral images, e.g., Landsat and Sentinel, are the most common data sources that are used for crop mapping [5, 6, 7]. The time series method is more accurate when data covering the whole growing season are used [8]. Interference from clouds and aerosols is the main drawback of optical remote sensing data, making time series analysis more challenging [9]. Concerning hyperspectral remote sensing, UAV hyperspectral cameras [10; 11; 12; 13] and airborne hyperspectral data [14; 15] have been extensively used for crop mapping. Spaceborne hyperspectral images have been used for crop mapping [16; 17; 18]. However, moving from proximal/airborne to spaceborne sensors, new challenging issues, related to low signal-to-noise ratio (SNR), spectral mixing issues related to the 30 m/pixel supported by the present missions, and atmosphere attenuations, must be considered. The two hyperspectral technology demonstration missions, i.e., the NASA Hyperion onboard the Earth Observing-1 (EO-1) spacecraft [19] and the Compact High Resolution Imaging Spectrometer (CHRIS) on ESA's Proba-1 microsatellite [20] did not show suitable temporal resolution and did not receive much attention from geoscientists and agronomists for crop-type mapping. Conversely, significant improvements in this context can be obtained nowadays, following the operation of the Italian Space Agency (ASI) PRISMA (Hyperspectral Precursor of the Application Mission) [21] hyperspectral satellite mission since 2019, the German EnMAP (Environmental Mapping and Analysis Program) since 2022 [22], the JPL-NASA EMIT (Earth Surface Mineral Dust Source Investigation) [23] since 2022, and the German DLR Earth Sensing Imaging Spectrometer (DESIS) [24], which provide hyperspectral images to the community worldwide. Furthermore, potential added value would be explored by forthcoming hyperspectral missions such as the ESA CHIME [25], ASI-PRISMA Second Generation ([[https://space.leonardo.com/en/news-and-stories-detail/-/detail/prismacordo-progetto-seconda-generazione](https://space.leonardo.com/en/news-and-stories-detail/-/detail/prismacordo-progetto-seconda-generazione)]([https://space.leonardo.com/en/news-and-stories-detail/-/detail/prismacordo-progetto-seconda-generazione](https://space.leonardo.com/en/news-and-stories-detail/-/detail/prismacordo-progetto-seconda-generazione)), accessed on 5 May 2024), and NASA SBG [26]. Despite improvements in the suitable swath width and revisit time, the ongoing hyperspectral satellites are still not fully suitable for early crop mapping and monitoring [27] unless applied in a tandem configuration on specific selected test sites. However, it is foreseen that the higher temporal resolution of the hyperspectral imagery will be able to provide, in the following years, denser time series suitable to the development of new high value products covering the crop growing season [28].