# Remote Crop Mapping at Scale: Using Satellite Imagery and UAV-Acquired Data as Ground Truth
## 2 Materials and Methods
### Ground-Truth Data  
For the ground-truth dataset, we collected high-resolution imagery (minimum ground resolution of 10 cm) in a series of UAV flights that were conducted at six locations in different agro-ecological zones (AEZs) [32; 33] (Figure 1). AEZs in Rwanda are mainly divided along the lines of elevation (higher elevation in the west and north and lower elevation toward the east), rainfall (higher in the west and lower in the east), and temperature (higher in the east and lower in the west) [33]. Sites within these AEZs were selected based on accessibility and the presence of (1) consolidated land use areas where field sizes are larger and generally only a single crop is grown; (2) smaller, intercropped fields that represent the dominant practice of smallholder agriculture; and (3) natural areas. Flights covered approximately 80 ha in each location and were conducted on three dates during the peak of the 2019 Season A growing season. This information is summarized in Table 1.  
The UAV flights were conducted by Charis Unmanned Aerial Solutions (Kigali, Rwanda). The RGB (red, green, blue) camera and UAV selections were based on the site requirements on the day of the flight. The DJI Zenmuse XSS (DJI, Shenzhen, China) camera was used with the DJI Inspire (DJI, Shenzhen, China) drone in all of the round #2 and round #3 flights and one round #1 flight. The senseFly S.O.D.A. (senseFly SA, Cheseaux-sur-Lausanne, Switzerland) camera was used with the Ebee plus (senseFly SA, Cheseaux-sur-Lausanne, Switzerland) drone in three round #1 flights. The SONY ILCE-6000 (SONY, New York, NY, USA) camera was used with the Parrot Disco (Parrot, Paris, France) \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Location** & **Agro-Ecological Zone** & **Flight \#1** & **Flight \#2** & **Flight \#3** \\ \hline Kaberege & Birunga & 27 December 2018 & 29 January 2019 & 19 February 2019 \\ \hline Kinyaga & \begin{tabular}{c} Central Plate/Eastern \\ Plateau \\ \end{tabular} & 12 December 2018 & 24 January 2019 & 21 February 2019 \\ \hline Kabarama & \begin{tabular}{c} Mayaga and \\ Peripheral Buggesera \\ \end{tabular} & 10 December 2018 & 21 January 2019 & 16 February 2019 \\ \hline Cyampirita & \begin{tabular}{c} Eastern Savanna and \\ Central Buggesera \\ \end{tabular} & 17 December 2018 & 25 January 2019 & 18 February 2019 \\ \hline Ngarama & \
\begin{tabular}{c} Buberuka Highlands \\ \end{tabular} & – & 30 January 2019 & 20 February 2019 \\ \hline Rwakigarati &
\begin{tabular}{c} Congo-Nile Watershed \\ Divide/Kivu Lake Border \\ \end{tabular} & 27 December 2018 & 31 January 2019 & 22 February 2019 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: UAV flight site information (note: imagery from 17 of the 18 flights was used in future analysis; the round #1 flight conducted at the Ngarama site was excluded). Figure 1: Agro-ecological zone map of Rwanda with unmanned aerial vehicles (UAV) flight sites and districts marked (Soucre: [PERSON] et al. [34]). drone in one round #1 flight. Georeferencing was accomplished using either a Continuously Operating Reference Station (CORS) with real time kinematic (RTK)-based correction or ground control points. The orthomosiac images for each UAV flight were created using the Pix4D (Version 4.2) software package. For labeling, the orthomosaic RGB images from each flight (nominal resolution of 3 to 10 cm) were imported into a custom viewer that used ESRI's ArcGIS API for JavaScript and ArcGIS Enterprise (ESRI, Redlands, CA, USA). The viewer was designed to support multiple users simultaneously, tracking the user and date of entry. The 10 \(\times\) 10-m Sentinel pixel grid was overlaid on the UAV images in the viewer. To avoid labeling locations corresponding to S2 pixels covered by clouds in the images of interest, we created a shapefile representing these clouded pixels and applied it to mask the clouded locations in the viewer. Grid cells were labeled according to the dominant landcover (i.e., at least 75% of the cell was the same class), and cells were not labeled in clouded areas.