## 3 Results  
### Overall Accuracy  
The classification approaches used in this work were selected based on the confusion matrix and the Kappa index [51; 52; 53], whose the overall accuracy is the proportion of the area mapped correctly. It provides the user of the map with the probability that a randomly selected location on the map is correctly classified. The Kappa coefficient measures the agreement between the resulting classes of the classifier and the true values [52; 54], with values ranging from 0 to 1, where 0 represents no agreement and 1 represents perfect agreement. The Table 2 shows the confusion matrix calculated from the reference data  
Figure 8: _NDVI_ time-series spectral profile curves at different crop developmental stages. Figure 7: Different types of crops in the study area. (**a**) Olive trees, (**b**) Cereals, (**c**) Fruit trees, (**d**) Vegetables (Source: Esri, Maxar, GeoEye, Earthquake Geographics, CNES/Airbus DS, USDA, USGS, AeroGRID, IGN, and the GIS User Community). and the map classes; the reference data is presented in the row, and the map classes in the columns. The results showed that the two approaches showed high classification accuracies. The overall accuracy for the SVM classifier exceeded 89.76%, and a significant agreement by the Kappa index of 0.79 was obtained. The overall accuracy for RF was 84.03%, with a Kappa index of 0.68. For the validation, 57,554 pixels were used. In most cases, it showed a high accuracy of this classification for most crop species. Few confusions between classes were recorded using the RF approach (e.g., crops followed by cereals, cereals, vegetables, and olive trees). In addition to the overall accuracy and the Kappa index, other types of accuracy and errors were calculated for both classifiers, including the producer's accuracy, the user's accuracy, the commission error, and the omission error (Table 3) [53]. 1. Producer's accuracy is defined as the probability that a value in a reference dataset was correctly classified. Producer's accuracy is the complement to the probability of omission error. 2. User's accuracy represents the probability that a resulting value in a certain class is really that class. User's accuracy is the complement to the probability of commission error. 3. Commission errors represent the fraction of the resulting values in a class that does not belong to that class. 4. Omission errors represent the fraction of values that belongs to one class but was predicted in a different class. In terms of accuracy, the producer's accuracy and user's accuracy confirm the results found for the overall accuracy. The producer's accuracy showed very high values, with more than 80% for all classes in the both classifiers SVM and RF, except the class other crops followed by cereals, which presented a value of 41.86% for the RF classifier. The user's accuracy showed very high values, with more than 79% for all classes in the SVM classifier, except for other crops followed by cereals, which presented a value 17.07%. For the RF classifier, this accuracy shows high values for the three classes of cereals, vegetables, and olive trees: 98.09%, 80.62%, and 66.05%, respectively. The other two classes, fruit trees