Predicting smallholder maize yield using sentinel-2-derived phenological metrics
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Date
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Publisher
Elsevier
Abstract
Please read abstract in the article.
HIGHLIGHTS
• Phenological metrics derived from multiple spectral indices are used to predict maize yields.
• Regularized linear models were trained with limited data to predict maize yields.
• Pre-peak and cumulative integrals of red-edge indices best predicted maize yield.
• Parsimonious models trained with key features showed no measurable loss of accuracy.
Description
DATA AND CODE AVAILABILITY : The Python code used for data processing, modelling, and feature-importance analysis in this study is publicly available at: https://github.com/masizawonga63-eng/sentinel2-maize-yield-phenology. Due to farmer confidentiality agreements and ethical restrictions, the raw field-level yield data and associated farm identifiers cannot be publicly shared. Derived phenological metrics and aggregated results supporting the findings of this study are available from the corresponding author upon reasonable request.
Keywords
Smallholder, Phenology, Remote sensing, Crop yield, Maize
Sustainable Development Goals
SDG-01: No poverty
SDG-02: Zero hunger
SDG-02: Zero hunger
Citation
Masiza, W., Nkuna, B.L., Ratshiedana, P.E. et al. 2026, 'Predicting smallholder maize yield using sentinel-2-derived phenological metrics', Smart Agricultural Technology, vol. 13, art. 101870, pp. 1-12, doi : 10.1016/j.atech.2026.101870.