## 3 Results
### Classification Accuracy  
The OA (for all cultivations) of classification using different machine and deep learning methods for PRISMA and Sentinel-2 images are shown as a bar graph in Figure 9. Figure 9 depicts that 1D-CNN and 3D-CNN methods were the most performing classifiers for all the seasons and sensors. For same-farm TR/ACC of PRISMA, 1D-CNN and 3D-CNN provided overall accuracy higher than 95%, while for cross-farm TR/ACC, the OA was higher than 88%. 1D-CNN provided a 1-4% better result compared with 3D-CNN. Following these, CNN, RF (76% < OA < 79%), and SVM (73% < OA < 77%) algorithms showed the highest accuracy for cross-field validation. The accuracy of KNN (67% < OA< 73%) was slightly lower than RF and SVM, while the result of the MNB (OA < 53%) method was poor. For Sentinel-2 image same-farm TR/ACC, 1D-CNN and 3D-CNN derived an overall accuracy higher than 88%, while for cross-farm TR/ACC, the OA was higher than 79%. 1D-CNN provided better results in winter and summer crops, while 3D-CNN provided better results for perennial crops. The RF (62% < OA< 66%) and SVM (60% < OA < 64%) algorithms had the highest accuracy for cross-farm TR/ACC after CNN methods. The accuracy of KNN (58% < OA < 64%) was a bit lower than RF and SVM, while the result of MNB (43% < OA < 47%) was poor. The per-species classification PA and UA derived from PRISMA and Sentinel-2 images by the MNB, KNN, SVM, RF, and 1D-CNN and 3D-CNN classification algorithms are reported in Figure 10. Winter and durum wheat are merged in order to be counted as the same species to have a higher accuracy, especially in PRISMA images. For PRISMA image classification, among the other species, alfalfa showed the lowest UA using all the methods. The rest of the species did not show a stable trend in all methods. For PRISMA, alfalfa had the lowest UA, and maize showed the highest UA when in the cross-farm TR/ACC scenario. For Sentinel-2, alfalfa showed (similarly to PRISMA) the lowest UA, while winter wheat was characterized by the highest UA when TR/ACC in cross-farm (Figure 10). Figure 8: Per-species OA curves of early-season crop mapping for the Mediterranean crop calendar of (**a**) winter, (**b**) summer, and (**c**) perennial cultivations using 1D-CNN algorithm and PRISMA images. The vertical dashed red line represents the DOY when the OA first reached 80% or higher. Figure 11e,g show the classification maps for the year 2022 obtained using the 3D-CNN for the Jolanda di Savoia and MKK farms, respectively. Figure 11f shows the map of the Macacrese farm produced by the 1D-CNN method for the year 2021. Figure 11h shows the 1D-CNN method of the Grosseto farm for 2020. To avoid assigning two different labels to the same pixel, the fields used for both summer and winter cultivation (a few fields) were eliminated. The winter, summer, and perennial crop maps were merged to produce a comprehensive crop map of each field for one year. On all sites, it can be observed that most of the errors of omission and commission occurred in the pixels located at the margins of the fields. Classification errors in field edge pixels were higher than in central pixels, which was related to spectral mixing with neighboring fields with no similar cultivation. It was also related to the presence of trees/brushes surrounding the cultivated fields and water channels. A negative 30 m buffer (1 pixel) at the edges of fields resulted in 1% and 3% improvement in the OA of classification by 3D-CNN and SVM, respectively. This could have an impact on the usability of PRISMA in fragmented agricultural areas in the case that pansharpening techniques are not applied [62; 63; 64]. Figure 9: Overall accuracy (\(\%\)) of the MNB, KNN, SVM, RF, 1D-CNN, and 3D-CNN classification methods for (**a**) same-farm TR/ACC- PRISMA, (**b**) cross-farm TR/ACC- PRISMA, (**c**) same-farm TR/ACC-Sentinel-2, and (**d**) cross-farm TR/ACC-Sentinel-2 accuracy assessment for all the selected sites. Figure 11: Ground truth of (**a**) Jolanda di Savoia, (**b**) Macacrese, (**c**) MKK, and (**d**) Grosseto.