Predicting smallholder maize yield using sentinel-2-derived phenological metrics

dc.contributor.authorMasiza, Wonga
dc.contributor.authorNkuna, Basani Lammy
dc.contributor.authorRatshiedana, Phathutshedzo Eugene
dc.contributor.authorMadasa, Akhona
dc.contributor.authorNduku, Lwandile
dc.contributor.authorShwatja, Tumelo
dc.contributor.authorChirima, Johannes George
dc.contributor.authorNyamugama, Adolph
dc.contributor.authorAbutaleb, Khaled
dc.contributor.authorKhoboko, Pitso Walter
dc.contributor.authorHamandawana, Hamisai
dc.date.accessioned2026-03-19T10:01:30Z
dc.date.available2026-03-19T10:01:30Z
dc.date.issued2026-03
dc.descriptionDATA 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.
dc.description.abstractPlease 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.
dc.description.departmentGeography, Geoinformatics and Meteorology
dc.description.librarianhj2026
dc.description.sdgSDG-01: No poverty
dc.description.sdgSDG-02: Zero hunger
dc.description.sponsorshipSupported by the Department of Agriculture’s Policy Research and Analysis directorate.
dc.description.urihttps://www.journals.elsevier.com/smart-agricultural-technology
dc.identifier.citationMasiza, 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.
dc.identifier.issn2772-3755 (online)
dc.identifier.other10.1016/j.atech.2026.101870
dc.identifier.urihttp://hdl.handle.net/2263/109076
dc.language.isoen
dc.publisherElsevier
dc.rights© 2026 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
dc.subjectSmallholder
dc.subjectPhenology
dc.subjectRemote sensing
dc.subjectCrop yield
dc.subjectMaize
dc.titlePredicting smallholder maize yield using sentinel-2-derived phenological metrics
dc.typeArticle

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