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

Zinhle Mashaba-Munghemezulu,George Johannes Chirima,Cilence Munghemezulu

doi:10.3390/su13094728

Zinhle Mashaba-Munghemezulu, George Johannes Chirima + Show 1 more

Open Access

https://doi.org/10.3390/su13094728

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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.

Highlights

Maize (Zea mays L.) is an essential cereal crop worldwide for food consumption, animal feed, and the production of industrial products such as biofuels [1]
Smallholder maize farms are important for the livelihoods of rural communities in Africa who depend on agriculture for food security and their local economic activities
The vegetation indices increased the accuracies by 24.2% for random forest (RF), 8.7% for support vector machine (SVM) and 9.1% for ST

Summary

Introduction

Maize (Zea mays L.) is an essential cereal crop worldwide for food consumption, animal feed, and the production of industrial products such as biofuels [1]. Global climate forecasts have reported that Africa could be one of the most susceptible regions to the effects of climate change by 2050 This phenomenon will cause growing water shortages and scarcity of suitable land, which will affect the production of cereal crops including maize [4,5]. Smallholder maize farms are important for the livelihoods of rural communities in Africa who depend on agriculture for food security and their local economic activities. These farmers are faced with problems such as inadequate rainfall due to droughts; they often have poor soils and limited irrigation infrastructure, which hinder their maximum productivity [6]

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