# Remote Crop Mapping at Scale: Using Satellite Imagery and UAV-Acquired Data as Ground Truth
## 4 Discussion  
This study investigated a scalable approach for using remote sensing technologies to create cropped land area maps for smallholder agricultural systems. UAVs were used to collect high-fidelity ground-truth information at six sites in Rwanda during 2019 Season A. Models were implemented using open-source, freely available resources to offer an accessible solution for the developing world [17]. Using UAVs to develop labeled datasets has advantages over traditional survey methods involving observers on the ground. In addition to accurate georeferencing, labelled imagery can be reviewed with respect to vegetation type, growth stage, and ground coverage. Labeling efforts can be extended at any point after the images are obtained and points added to the datasets based on the purpose of a specific model. The labeling can also be automated as indicated by another recently published study from our group [34]. Limitations to collecting ground-truth examples in this manner include restrictions on site selection in terms of the elevation and slope at which UAVs can fly. Although other studies that specifically address crop mapping in smallholder agricultural systems have focused on a single crop, most commonly maize [17], this study aimed to develop a cropped area map that included several strategic crops. Determining the separability of classes using the selected features is an important first step to ensure a robust model and aid in interpretability [16]. We analyzed the data in selected optical and SAR bands from the peak of the growing season based on the availability of relatively cloud-free S2 imagery. This qualitative analysis indicated that maize, bananas, trees, and the non-vegetative class were well separated, but beans, cassava, and other vegetation significantly overlapped. We also examined the spectral signatures of plots of maize that were intercropped with beans or cassava. It is generally assumed that intercropped maize will appear spectrally similar to pure maize fields [17]. From the UAV imagery, we determined that there was a broad range of intercropping patterns. Not surprisingly, when the density of maize was low, the plot signature more closely matched that of beans or cassava compared with pure maize. To better capture intercropped conditions,  
Figure 6: 2019 Season A crop area map. researchers should include additional categories and ground-truth examples in the model to describe specific intercropping patterns. Ultimately, as the development of publicly supported satellite networks continues and more high-resolution imagery is made freely available, the accuracy of labeled datasets and models will improve. For example, [PERSON] et al. were able to differentiate between pure maize and intercropped maize conditions using imagery acquired from the commercial RapidEye satellites that provide images with 5-m ground resolution [38]. The ground-truth dataset generated from the labeled UAV imagery was used to create a seven-class cropped land model including three seasonal and one annual crop using a S1/S2 composite image. This model extends the seminal work of [PERSON] et al. and [PERSON] et al. [8; 9; 17] that focused on classifying maize only in smallholder agricultural systems. The model developed in this work was developed using the open-access GEE platform and achieved an overall accuracy of 83%, and maize and beans (including climbing beans and bush beans) were classified with overall accuracies of 91% and 84%, respectively. Model accuracies for these categories compare well to the models reported in the other studies of smallholder agricultural systems, in which single crops were classified with accuracies ranging from 67% to 79% [8; 9; 17]. Overall accuracy was also similar to the pixel-based multi-class modeling approaches used by [PERSON] et al. in their study combining S1 and S2 data to classify fields in Ukraine [13].