Abstract:
Rural communities rely on smallholder maize farms for subsistence agriculture, the main
driver of local economic activity and food security. However, their planted area estimates are
unknown in most developing countries. This study explores the use of Sentinel-1 and Sentinel-2
data to map smallholder maize farms. The random forest (RF), support vector (SVM) machine
learning algorithms and model stacking (ST) were applied. Results show that the classification of
combined Sentinel-1 and Sentinel-2 data improved the RF, SVM and ST algorithms by 24.2%, 8.7%,
and 9.1%, respectively, compared to the classification of Sentinel-1 data individually. Similarities
in the estimated areas (7001.35 ± 1.2 ha for RF, 7926.03 ± 0.7 ha for SVM and 7099.59 ± 0.8 ha
for ST) show that machine learning can estimate smallholder maize areas with high accuracies.
The study concludes that the single-date Sentinel-1 data were insufficient to map smallholder maize
farms. However, single-date Sentinel-1 combined with Sentinel-2 data were sufficient in mapping
smallholder farms. These results can be used to support the generation and validation of national
crop statistics, thus contributing to food security.
