## 4 Discussion
### Early-Stage Crop Mapping Lising ML and DL Algorithms  
Regarding the performances of the different algorithms tested, for all cultivations, (Figure 9) both CNN (1D and 3D) methods showed to be the best performing classifiers, achieving OAs higher than 88% and 79% for PRISMA and Sentinel-2, respectively. In all the cases we considered, 1D-CNN produced higher OA than 3D-CNN when applied to the PRISMA imagery. Similarly, for the Sentinel-2 time series, with the exception of the cross-farm TR/ACC scenarios applied to the perennial cultivation, the 1D-CNN provided higher OA than the 3D-CNN (Figure 9). Following 1D-CNN and 3D-CNN, the RF (OA higher than 76% and 62% for PRISMA and Sentinel-2, respectively) and SVM (OA higher than 73% and 60% for PRISMA and Sentinel-2, respectively) algorithms gained the next level of accuracy. The accuracy of KNN (OA higher than 67% and 58% for PRISMA and Sentinel-2, respectively) was lower than RF and SVM, while the result of the MNB (OA higher than 46% and 43% for PRISMA and Sentinel-2, respectively) method performed not well, showing lower accuracy (Figure 9). If we analyze the classification performance along the available image time series, assuming an OA of 80% as an acceptable threshold for a suitable crop mapping, then, at DOY 81, all winter species can be recognized by PRISMA images (Figure 8a). The triticale showed the maximum OA earlier than wheat and barley in accordance with the different winter development among crops (Figure 8a). Wheat and triticale had lower OA until the end of the season (Figure 8a). The spectral similarity between barley triticale and winter wheat was high (Figure 6e), which led to confusion for the classifiers. The fava beans gained the best OA after DOY 102, as it was related to later growth start with respect to the other species (Figure 7a). For summer cultivation, DOY 172 was the first time for which all species could be separated by PRISMA images (Figure 8b). Rice OA reached the maximum earlier and then started to reduce after DOY 190. Sorghum and soybeans could be identified 20 days earlier  
Figure 12: (**a**) Ground truth, (**b**) classification result of a non-homogeneous field at Jolanda di Savoia farm with high misclassification in a field cultivated with durum wheat, (**c**) PRISMA RGB (bands 32-21-10) acquired on 30 April 2022, (**d**) spectral reflectance of sparse and dense wheat. than other species, while maize and sorghum showed the better identification after DOY 185 (Figure 5b). For perennial cultivation, DOY 91 was the first time for which they could be separated by PRISMA images (Figure 6c) and, among them, almonds could be identified 20 days earlier than other species. Cardoon showed the highest user accuracy until DOY 91 (OA up to 92%). This was also related to the fact that cardoons started to grow before other species (Figure 5b), which led to having less similarity in reflectance during the first growing stages. We were able to estimate the cultivated area for maize, tomato, and cardoon two months ahead of harvest. For soybeans, wheat, barley, triticale, and peas, the estimation could be carried out about three months before the harvest, while, for rice, sunflower, and sorghum, we could estimate their cultivated areas about four months before the harvest. This information is very useful for a range of applications, including monitoring for compliance with agricultural policies such as the EU Common Agricultural Policy (CAP), in terms of surfaces declared by farmers for crops. Additionally, since the proper identification of cultivated areas is a fundamental requirement for yield forecast analysis at the regional scale, this information is extremely valuable for entities dealing with both food security and food commodity trading, as well as insurance. This supporting information allows to detect at the earliest the critical regions that could suffer food crises, allowing local governments and international humanitarian organizations to plan remediation activities [75].