A growing body of literature leverages multitemporal data fusion from multiple platforms to identify various crops [21; 22], including sugarcane [23; 24], rice [25], maize, soybeans, and wheat [26; 27]. Additionally, research extends to mapping different horticultural crops in heterogeneous contexts [28] and perennial crops, such as olive, grapes, and fruit trees [29; 30; 31; 32]. Moreover, the application of remotely sensed data extends beyond spatial identification, encompassing diverse domains such as crop type characterization [33; 34], yield prediction [35; 36], estimation of biophysical parameters [37; 38], nutritional requirements [39], and disease detection [40]. As pointed by [41], the task of crop classification through remote sensing encompasses a plethora of approaches that span several objectives, crops, data sources, and features selection. Notably, there is a wide array of Machine Learning and Deep Learning [42; 43; 44; 45] algorithms available. This rich variety of methods is made possible by the extensive utilization of big data, which is now readily available, thanks to the advancements in Earth Observation (EO) technologies. Among these technologies, the Google Earth Engine (GEE) cloud platform [47] facilitates efficient work on diverse data catalogs, enabling simultaneous tasks on different collections and unlocking new possibilities for crop recognition and analysis in geospatial data [48]. Regarding hazelnut crop detection, several studies have been conducted in Turkey, while the literature in Italy is limited and outdated [49]. Mapping hazelnut groves using satellite imagery can be challenging due to their arboreal nature, resulting in spectral signatures similar to other arboreal crops or spontaneous vegetation; this is a common issue for orchards detection [50]. Furthermore, hazelnut occurs spontaneously as a wild plant in the woods; based on only the spectral signature, this could lead to numerous false-false positives, which are hardly verifiable in a mixed forest context. These factors may lead to the misclassification of hazelnuts in wooded areas [51; 52]. Another significant challenge is the heterogeneity of hazelnut plantations. Variations in age and planting distances can influence the spectral content of pixels, impacting classification accuracy [51]. In line with these challenges, [31] found the lowest precision specifically in the \"hazelnut\" class during a multi-crop classification study conducted in Australia. Further investigations into hazelnut crop detection emphasize the importance of incorporating texture using Gabor filters in conjunction with vegetation indices [52]. This approach has shown good results in limited areas and high-resolution images [51], with notably superior performance during the summer season [52], compared to winter, leading to improved accuracy metrics. This study presents an innovative approach for accurately classifying a specific tree crop within a complex vegetated landscape. We exploited Sentinel data, which have provided during the last years a unique opportunity to investigate the territory and apply remote-sensing techniques at different territorial scales [53]. The innovative aggregation of multi-temporal optical data, spanning three years, with radar data from a single year, utilizing all bands as individual variables, represents a novel approach to data effectively exploited in tree crop detection. A significant aspect of the present work also involves a thorough analysis of the importance of variables in this context. This analysis contributes to defining physiological and phenological characteristics specific to crops, enabling a better understanding of the dynamics that characterize them. Moreover, it is crucial to underscore that, as case studies, there are no such detailed and accurate analyses for the specific areas considered in the present work.