# Remote Crop Mapping at Scale: Using Satellite Imagery and UAV-Acquired Data as Ground Truth
## 3 Results
### Cropped Land Model Results  
An RF model was trained and tested on the GEE platform using a composite image that consisted of four S2 features (B2, B3, B4, and B8) from a single point in time and three S1 features that spanned the peak of the growing season (median VV values for the months of November 2018, December 2018, and January 2019). For the optical bands, pixels from the January 23 image were used to fill in clouded pixels in the January 28 image. The ground-truth dataset was split into training (80% of labeled points) and testing (20% of labeled points) datasets such that none of the points used to train the model were used to test the model. The confusion matrix for the testing dataset is provided in Table 4, where each row corresponds to a predicted class and each column to an actual class. The cells show the counts of correct and incorrect classifications for each class. The producer's accuracy, defined as the ratio of the number of sites classified correctly by the model to the total number of sites for the class, is shown in  
Figure 4: Examples of maize intercropped with cassava (**a**) and maize-only (**b**) fields in Rwanda. The 10 × 10-m Sentinel grid cells are shown for reference and labeled to reflect at least 75% of the same landcover type. Figure 5: 2D S2-MSI feature space for representative examples of seasonal crops and vegetation (iMaize = intercropped maize). the last column. The overall accuracy of the model was 83%, with producer's accuracies of 91% and 84% for maize and beans, respectively. Examining the confusion matrix more closely, we can see that in cases where other vegetation was not classified correctly, it was most often confused with beans and cassava. These classes are spectrally very similar (Figure 3d), so this confusion is not surprising. The non-vegetative class was also confused with beans, and there is some spectral overlap with this class (Figure 3d). Trees were occasionally confused with maize, which again agrees with the short distance between data points corresponding to these categories (Figure 3d). The comparatively large confidence intervals for some of the categories (e.g., beans, cassava, and bananas) result from a relatively small number of data points used in the accuracy assessment based on the 80/20 split of the original ground-truth dataset. A cropped land area map (Figure 6) was created using the composite S1/S2 image for the entire country of Rwanda. National parks [41] and water bodies [42] were masked using available products in GEE. The estimated land area for each crop included in the model was compared with the 2019 Season A SAS on a country scale (Table 5) [30]. The estimated areas for maize and beans agreed with the crop/cultivated areas (defined as the area occupied by a given crop in a plot considering its density or occupation) reported in the SAS to within 3% and 7%, respectively. The estimated area for cassava was in good agreement with the total area reported by the SAS for tubers and roots but significantly overestimated the total area for cassava alone. The spectral feature space for cassava was overlapped with other vegetation, which was undercounted by the model. A large discrepancy was also observed for the predicted versus reported cropped area for bananas. The total harvested area (defined as the total number of hectares that was harvested in a given agricultural season) for bananas was lower than the cropped area (i.e., 82,523 ha versus 253,996 ha), indicating that the plants are at different stages of growth as would be expected for an annual crop. Other sources contributing to these discrepancies are further considered in the Discussion. The total area under cultivation (i.e., the sum of classes 1 through 5) in the model (1,454,713 ha) agreed with the survey results (1,319,256 ha) to within 10%.