Assessing the prospects of remote sensing maize leaf area index using uav-derived multi-spectral data in smallholder farms across the growing season

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dc.contributor.authorButhelezi, Siphiwokuhle
dc.contributor.authorMutanga, Onisimo
dc.contributor.authorSibanda, Mbulisi
dc.date.accessioned2023-04-20T09:38:58Z
dc.date.available2023-04-20T09:38:58Z
dc.date.issued2023
dc.description.abstractMaize (Zea Mays) is one of the most valuable food crops in sub-Saharan Africa and is a critical component of local, national and regional economies. Whereas over 50% of maize production in the region is produced by smallholder farmers, spatially explicit information on smallholder farm maize production, which is necessary for optimizing productivity, remains scarce due to a lack of appropriate technologies. Maize leaf area index (LAI) is closely related to and influences its canopy physiological processes, which closely relate to its productivity. Hence, understanding maize LAI is critical in assessing maize crop productivity. Unmanned Aerial Vehicle (UAV) imagery in concert with vegetation indices (VIs) obtained at high spatial resolution provides appropriate technologies for determining maize LAI at a farm scale. Five DJI Matrice 300 UAV images were acquired during the maize growing season, and 57 vegetation indices (VIs) were generated from the derived images.en_US
dc.identifier.citationButhelezi, S. et al. (2023). Assessing the prospects of remote sensing maize leaf area index using uav-derived multi-spectral data in smallholder farms across the growing season. Remote Sensing, 15(6), 1597. https://doi.org/10.3390/rs15061597en_US
dc.identifier.issn2072-4292
dc.identifier.urihttps://doi.org/10.3390/rs15061597
dc.identifier.urihttps://hdl.handle.net/10566/8840
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.subjectFarmingen_US
dc.subjectAgricultureen_US
dc.subjectRemote sensingen_US
dc.subjectFood securityen_US
dc.subjectsub-Saharan Africaen_US
dc.titleAssessing the prospects of remote sensing maize leaf area index using uav-derived multi-spectral data in smallholder farms across the growing seasonen_US
dc.typeArticleen_US

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