# Remote Crop Mapping at Scale: Using Satellite Imagery and UAV-Acquired Data as Ground Truth
## 1 Introduction
tested several pixel- and object-based approaches for identifying a variety of landcover and crop types using Sentinel-1 and Landsat-8 data [13]. They achieved the highest accuracy when assigning parcels based on the dominate pixel class and demonstrated that accuracies exceeding 90% should be achievable for large parcels. Identifying individual parcels is difficult and time-consuming in smallholder agricultural systems, which may limit the utility of these approaches. However, techniques for combining multispectral and SAR data into a single model are still relevant for identifying cropland, and specifically maize, in Kenya and Tanzania where smallholder agriculture is predominantly practiced and cloud cover interferes with the regular availability of Sentinel-2 imagery [17]. A key aspect in developing well-performing ML models is the quality of ground-truth data [2]. Continental- or regional-scale cropland data maps can be used to generate a large amount of ground-truth data quickly, but these types of maps cannot be rendered with a high degree of accuracy for the developing world because of sparse and/or missing data [18]. Sites managed by the Joint Experiment for Crop Assessment and Monitoring (JECAM) [19] can be valuable sources of data for studies conducted in nearby areas [10; 12; 13] as these sites are rigorously monitored for the purposes of generating time series datasets. For smallholder agricultural systems, information is traditionally obtained by observers on the ground [6; 7; 8; 9; 14; 16; 17; 18]. Aside from practical limitations such as the challenge of surveying hilly or remote areas with limited road systems, this practice is time consuming, is labor intensive, and does not lend itself to easy standardization and automation. In recent years, unmanned aerial vehicles (UAVs) have become widely available and inexpensive, and companies providing UAV services are now present in many developing countries. UAVs can acquire high-resolution georeferenced images of large areas quickly and at low cost [20; 21]. For example, in this study, images of an area of 80 ha with a minimum ground resolution of 10 cm were acquired in 2 to 3 h. UAV imagery can be georeferenced with high accuracy, reliably registering locations of even the smallest fields in the satellite grid. The aerial view gives the analyst the ability to accurately label intercropped fields, a task that is especially difficult for surveyors on the ground [22]. Although UAV-acquired images have previously been employed to gain information about agricultural production on a local scale [23; 24; 25; 26; 27; 28], their use as a source of ground-truth data for satellite-based crop analytics has not yet been widely investigated [29]. Building on the strengths of UAVs for capturing on-demand, accurate agricultural information, the objective of this study was to determine the best practices for using UAV-based ground-truth data to create regional-scale cropland data maps for smallholder agricultural systems. To be cost-effective for use in the developing world, cropland data maps must be easy to maintain and update as new information becomes available. To this end, a second objective of this work was to implement all necessary models in open source platforms using freely available satellite data. In order to test the feasibility of this approach, we conducted a pilot study in Rwanda during 2019 Season A (September 2018-February 2019). Given the significant cloud cover during the growing season, we chose to focus the training, testing, and implementation of the model on relatively cloud-free Sentinel-2 (S2) images recorded in January 2019 when many crops reached their maturity [30] and tended to exhibit the most pronounced differences in their spectral signatures. We tested the model on a subset of the ground-truth dataset that was not used for training. We compared the areas under specific crops with the areas reported by the agricultural survey for 2019 Season A, obtaining agreement for maize and beans to within 10% on a country scale.