Type
Date
Corporate author
Editor
Illustrator
Producer
Photographer
Contributor
Journal Title
Journal ISSN
Volume Title
Access Rights
Abstract
Description
This study estimated maize grain biomass, and grain biomass as a proportion of the absolute maize plant biomass using UAV-derived multispectral data. Results showed that UAV-derived data could accurately predict yield with R2 ranging from 0.80 – 0.95, RMSE ranging from 0.03 - 0.94 kg/m2 and RRMSE ranging from 2.21% - 39.91% based on the spectral datasets combined. Results of this study further revealed that the VT-R1 (56-63 days after emergence) vegetative growth stage was the most optimal stage for the early prediction of maize grain yield (R2 Combining double low line 0.85, RMSE Combining double low line 0.1, RRMSE Combining double low line 5.08%) and proportional yield (R2 Combining double low line 0.92, RMSE Combining double low line 0.06, RRMSE Combining double low line 17.56%), with the Normalized Difference Vegetation Index (NDVI), Enhanced Normalized Difference Vegetation Index (ENDVI), Soil Adjusted Vegetation Index (SAVI) and the red edge band being the most optimal prediction variables. The grain yield models produced more accurate results in estimating maize yield when compared to the biomass and proportional yield models. The results demonstrate the value of UAV-derived data in predicting maize yield on smallholder farms - a previously challenging task with coarse spatial resolution satellite sensors.
Keywords
Citation
APA citation
ISO citation
Copyright
CIMMYT 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
Journal
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Journal volume
10
Journal issue
1
Article number
Place of Publication
Germany
Publisher
Copernicus GmbH