# Remote Crop Mapping at Scale: Using Satellite Imagery and UAV-Acquired Data as Ground Truth
## 1 Introduction  
Up-to-date agricultural information is needed for planning and forecasting to identify areas at risk of food insecurity and better inform resource allocation and sustainability efforts in developing countries. The Food and Agriculture Organization (FAO) of the United Nations in its \"Global Strategy to Improve Agricultural and Rural Statistics, Phase 2, 2020-2025\" identified remote sensing as an important component of its research agenda, calling for new data and methodologies to enable high-accuracy, high-refresh crop mapping, and crop yield estimation [1; 2; 3]. While the potential of satellite-based remote sensing to provide information on agricultural production has been recognized since the mid-1970s [4; 5], data at a sufficient resolution and revisit frequency for accurately mapping smallholder agricultural systems have only recently been made freely available with the launch of the Sentinel satellites. Crop maps can be generated by applying supervised machine learning (ML) models to satellite data. [PERSON] et al. classified seven crop types (six summer and one winter/bare soil) in Austria using Sentinel-2 images acquired on a single date using pixel-based (overall accuracy: 83%) and object-based (overall accuracy: 77%) approaches [6]. Confusion between crop types was attributed to the sub-optimal timing of the Sentinel-2 image used [6]. Building on this work, [PERSON] et al. used a pixel-based approach for classifying nine crop types over a two-year time frame using data frommultiple versus single dates [7]. They found that using multi-temporal information improved overall accuracy (91-95%), and the highest single-date classification accuracies occurred when the summer crops reached the peak of their development and winter crops were harvested [6; 7]. While these results are promising, analysis of smallholder farms remains a challenge. Higher resolution imagery can be used to accurately predict maize yields in smallholder agricultural systems [8; 9], but these data are not freely available. Using a hybrid approach, [PERSON] et al. used an object-based approach to segment smallholder agricultural systems in Madagascar into crop versus non-crop classes (using high-resolution Pleiades imagery) before further classifying individual crops (using Landsat 8 and SPOT-5 imagery) [10]. [PERSON] et al. also showed that several vegetation indices (i.e., leaf area index, leaf chlorophyll content, and canopy chlorophyll content) could be derived from the Sentinel-2 bands available at a 10m spatial resolution, which has important implications for smallholder agriculture [11]. Relying on multispectral information alone is challenging in areas with significant cloud cover as data from multiple dates may not be available and the most suitable imagery may correspond to sub-optimal crop separation conditions (especially for rainfed crops). Synthetic aperture radar (SAR) data are not affected by cloud cover, and backscatter in the VV and VH polarizations correlates with the normalized difference vegetation index (NDVI) [12] and is sensitive to changes in soil surface conditions (i.e., fallow, preparation for planting, and active growth) as well as plant phenology [13]. Using multi-temporal SAR models, [PERSON] et al. were able to identify summer corn, rice, soybean, and peanut fields at two sites in China with overall accuracies exceeding 90% [14]. With respect to smallholder agriculture, models using SAR data only show higher accuracies for parcels >2 ha [15], which may limit their utility in the developing world where fields are often <1 ha. Multispectral and SAR data can be combined into a single model to take advantage of the strengths offered by each data source. [PERSON] et al. classified six crop types at the parcel level using Sentinel-1 and -2 imagery [16]. They tested four ML algorithms and achieved the highest accuracy (96.8%) using the kernel extreme learning machine (KELM) [16]. [PERSON] et al.