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

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  • Zinhle Mashaba-Munghemezulu

    (Department of Geography, Geoinformatics and Meteorology, University of Pretoria, Pretoria 0028, South Africa
    Geoinformation Science Division, Agricultural Research Council Institute for Soil, Climate and Water, Pretoria 0001, South Africa)

  • George Johannes Chirima

    (Department of Geography, Geoinformatics and Meteorology, University of Pretoria, Pretoria 0028, South Africa
    Geoinformation Science Division, Agricultural Research Council Institute for Soil, Climate and Water, Pretoria 0001, South Africa)

  • Cilence Munghemezulu

    (Geoinformation Science Division, Agricultural Research Council Institute for Soil, Climate and Water, Pretoria 0001, South Africa)

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.

Suggested Citation

  • Zinhle Mashaba-Munghemezulu & George Johannes Chirima & Cilence Munghemezulu, 2021. "Delineating Smallholder Maize Farms from Sentinel-1 Coupled with Sentinel-2 Data Using Machine Learning," Sustainability, MDPI, vol. 13(9), pages 1-16, April.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:9:p:4728-:d:541849
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    References listed on IDEAS

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    2. Romy Santpoort, 2020. "The Drivers of Maize Area Expansion in Sub-Saharan Africa. How Policies to Boost Maize Production Overlook the Interests of Smallholder Farmers," Land, MDPI, vol. 9(3), pages 1-13, February.
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