Delineating Smallholder Maize Farms from Sentinel-1 Coupled with Sentinel-2 Data Using Machine Learning

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 smallholde...

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Bibliographic Details
Published in:Sustainability 2021-05, Vol.13 (9), p.4728
Main Authors: Mashaba-Munghemezulu, Zinhle, Chirima, George Johannes, Munghemezulu, Cilence
Format: Article
Language:English
Subjects:
Agriculture
Algorithms
Classification
Climate change
Corn
Developing countries
Economic conditions
Farms
Food security
Global positioning systems
GPS
LDCs
Learning algorithms
Machine learning
Mapping
Rain
Remote sensing
Rural areas
Rural communities
Sensors
Small farms
Subsistence agriculture
Topography
Vegetation
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Summary: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.
ISSN:2071-1050
2071-1050
DOI:10.3390/su13094728