to 0.47 million hectares (8 to 24 percent) in Malawi vis-a-vis the results from the best performing model (i.e. plot mean). The analysis reveals that collecting GPS coordinates of the complete set of plot corners, as a second-best strategy, can approximate full plot boundaries and can in turn train models with comparable performance.

Furthermore, when only a few observation plots (fewer than 1,000 plots) can be visited, full plot boundaries or multiple corner points provide significant gains vis-a-vis plot corner points or plot centroid. With mid-sized samples (3,000 to 4,000 plots), plot centroids can produce similar performance to full plot boundaries. With large sample sizes (around 7,000 plots), plot centroids fall behind full plot boundaries. If only a single GPS point is to be gathered by data collectors, that location should be near the center of the plot rather than at the plot corner. However, georeferencing plot centroids should be understood as a third-best strategy for remote sensing model training purposes. The findings suggest that classification performance almost always peaks before or at around 4,000 plots under the preferred geolocation strategies - corresponding to roughly less than 60 percent of the training data. As such, it is better to collect high-quality plot boundaries from 4,000 plots as opposed to corner points from 7,000 plots.

Moreover, we demonstrate that no plot observations should be excluded from model training based on a minimum plot area threshold - another important note for future surveys. Finally, the experiments to quantify the effect of satellite data sources on crop type classification performance suggest that optical features alone can provide sufficient signal to maximize prediction quality. We observed only small differences between models built only with optical features and those using optical and SAR features. In the case of maize area mapping in Malawi, the potential benefits offered by SAR - providing signals unaffected by cloud cover - were offset by additional noise introduced with SAR imagery.

Many outstanding questions remain for future research. Crop classification accuracies of 0.9 or greater are not unusual in the literature, though small plot sizes in the African context may limit realistically attainable accuracy. Nevertheless, attempting to improve the accuracy of maize classification and gauging the sensitivity of our recommendations for alternative crops and countries should be among the foci of future research. Improvements may be gained by removing pixels with few cloud-free observations during the growing season. Experimenting with alternative machine learning approaches and an expanded set of geospatial covariates may increase performance as well. Further work is also needed to distinguish between intercropped and monocropped (“pure stand”) maize plots in order to a) improve classification performance, b) support the creation of intercrop maps and area estimates, and c) lead to continued refinements of downstream research related also to satellite-based crop yield estimation. Related to the latter, future research should similarly identify the minimum-required volume of and approach to survey data collection that would yield optimal data for training and validating remote sensing models for high-resolution crop yield estimation. Moreover, documenting the accuracy of out-of-season 34