## 2 Materials and Methods
### Pre-Processing and Processing EO Data
#### 2.4.2 Machine Learning Classification Algorithms  
Different machine and deep learning algorithms for crop type mapping using PRISMA and Sentinel-2 data were considered. To avoid errors that could happen between species with different cultivation seasons, we decided to develop separate classifiers for winter, summer, and perennial crop seasons. Each method is described in the following paragraphs. Multiclass Naive Bayes (MNB): The MNB classifier is based on Bayes theorem and belongs to the family of generative learning algorithms, which means that it models the distribution of inputs for a given class or category. This approach assumes that the features of the input data are conditionally independent given the class, allowing the algorithm to make predictions quickly and accurately [51]. K-Nearest Neighbor (KNN): The KNN algorithm is an instance-based learning method that classifies elements based on the closest k training samples in the resource space. KNN is a non-parametric MLA that makes no assumptions regarding data distribution. This is important when classifying processes of land use change, for which there is little or no prior knowledge of data distribution. In KNN, the pixel whose class is unknown is assigned to the same class as its spectrally closest neighbors [52]. Random Forest (RF): The RF technique [53] is an ensemble classifier that employs many decision trees and combines their results to forecast an output. There are two main steps in building each decision tree in an RF: (1) creation of a new training set by random \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Site** & **Year** & **N. of Fields** & **Area (ha)** & **Sentinel-2** & **PRISMA Image Date (Month/Day)** \\ \hline \multirow{4}{*}{Maccarese} & 2021 & 92 & 1600 & 6 & 04/01, 05/17, 06/27, 09/04 \\ \cline{2-6}  & 2022 & 165 & 1600 & 6 & 01/16, 04/12, 05/29, 06/15, 07/14 \\ \cline{2-6}  & 2023 & 70 & 700 & 2 & 02/02, 03/21 \\ \hline \multirow{4}{*}{Jolanda} & 2021 & 176 & 3700 & 6 & 04/24, 06/04, 06/21 \\ \cline{2-6}  & 2022 & 210 & 4100 & 8 & 04/30, 05/12, 07/03, 08/01 \\ \cline{2-6}  & 2023 & 105 & 2863 & 4 & 03/04, 04/07, 05/24, 07/03, 08/07 \\ \hline \multirow{2}{*}{Grosseto} & 2020 & 6 & 20 & 1 & 07/31 \\ \cline{2-6}  & 2021 & 24 & 120 & 5 & 01/23, 03/11, 04/09, 05/14, 05/19, 06/23, 07/22 \\ \cline{2-6}  & 2022 & 30 & 310 & 7 & 01/29, 02/27, 03/28, 05/08, 05/25, 07/05, 07/22 \\ \cline{2-6}  & 2023 & 26 & 130 & 3 & 02/28, 03/17, 04/09 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Field data and PRISMA and Sentinel-2 images used for crop type mapping of MKK, Grosseto, Maccarese, and Jolanda di Savoia sites for 2020–2023. The PRISMA images used for Group A and Group B were shown in green and red color, respectively. The images highlighted in bold were assumed as if they were acquired in the other Group. selection of subsamples through bootstrapping sampling from a specific number of samples in the original dataset, and (2) random selection of features. These two random processes help to avoid overfitting [54], because RF averages all these multiple decision trees, trained on different parts of the same training set, and assigns each pixel to the class label that garnered the most \"votes\". The number of trees is obtained using out-of-bag data analysis, and the number of features is obtained using the square root of the features. The RF algorithm, in particular, can process high-dimensional data, works well with mislabeled data, and has high accuracy for large-scale studies [55]. Support Vector Machine (SVM): SVM was originally proposed as a linear binary classifier. Subsequently, by introducing suitable kernels, the algorithm can perform nonlinear classification. Previous studies show that the radial basis function (RBF) kernel works properly for classification [56]. The RBF kernel was used in this research, where \(C\) (cost) and gamma parameters were optimized.