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

dc.creatorButhelezi, S.
dc.creatorMutanga, O.
dc.creatorSibanda, M.
dc.creatorOdindi, J.
dc.creatorClulow, A.D.
dc.creatorChimonyo, V.G.P.
dc.creatorMabhaudhi, T.
dc.date2023-04-28T00:30:16Z
dc.date2023-04-28T00:30:16Z
dc.date2023
dc.date.accessioned2023-07-17T20:10:33Z
dc.date.available2023-07-17T20:10:33Z
dc.identifierhttps://hdl.handle.net/10883/22585
dc.identifier10.3390/rs15061597
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7514328
dc.descriptionMaize (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. Maize LAI samples were collected across the growing season, a Random Forest (RF) regression ensemble based on UAV spectral data and the collected maize LAI samples was used to estimate maize LAI. The results showed that the optimal stage for estimating maize LAI using UAV-derived VIs in concert with the RF ensemble was during the vegetative stage (V8–V10) with an RMSE of 0.15 and an R2 of 0.91 (RRMSE = 8%). The findings also showed that UAV-derived traditional, red edge-based and new VIs could reliably predict maize LAI across the growing season with an R2 of 0.89–0.93, an RMSE of 0.15–0.65 m2/m2 and an RRMSE of 8.13–19.61%. The blue, red edge and NIR sections of the electromagnetic spectrum were critical in predicting maize LAI. Furthermore, combining traditional, red edge-based and new VIs was useful in attaining high LAI estimation accuracies. These results are a step towards achieving robust, efficient and spatially explicit monitoring frameworks for sub-Saharan African smallholder farm productivity.
dc.languageEnglish
dc.publisherMDPI
dc.rightsCIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose
dc.rightsOpen Access
dc.source6
dc.source15
dc.source2072-4292
dc.sourceRemote Sensing
dc.source1597
dc.subjectAGRICULTURAL SCIENCES AND BIOTECHNOLOGY
dc.subjectSmallholder Farming
dc.subjectVegetation Indices
dc.subjectRandom Forest Algorithm
dc.subjectCROPS
dc.subjectFARMS
dc.subjectFORESTRY
dc.subjectGRAIN
dc.subjectUNMANNED AERIAL VEHICLES
dc.subjectVEGETATION
dc.subjectLEAF AREA INDEX
dc.subjectMAIZE
dc.subjectREMOTE SENSING
dc.subjectSMALLHOLDERS
dc.subjectSustainable Agrifood Systems
dc.titleAssessing the prospects of remote sensing maize leaf area index using UAV-derived multi-spectral data in smallholder farms across the growing season
dc.typeArticle
dc.typePublished Version
dc.coverageBasel (Switzerland)


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