Relationship of Drone‐Based Vegetation Indices with Corn and Sugarbeet Yields. (1st September 2019)

• Record Type: Journal Article
• Authors: Olson, Dan; Chatterjee, Amitava; Franzen, Dave W.; Day, Stephanie S.
• Is Part Of: Agronomy Journal. Volume 111:Number 5(2019)
• Page Start: 2545
• Page End: 2557
• Publication Date: 2019-09-01
• Subjects: Agronomy -- Periodicals; 630
• DOI: 10.2134/agronj2019.04.0260
• Languages: English
• ISSNs: 0002-1962

Abstract

Abstract : Successful adoption of drone‐based remote sensing depends on changes in sensitivity over vegetation indices (VIs) and growth stage(s). During 2017–2018, experiments were conducted to relate between vegetation indices and corn ( Zea mays L.) and sugarbeet ( Beta vulgaris L.) yields in western Minnesota. Aerial images were collected using an unmanned aerial vehicle (UAV) equipped with a passive light optical sensor (Micasense RedEdge). Using Pix4D software, spectral reflectance data were derived from flights at V6 and VT growth stages of corn, and V10 and V15 growth stages of sugarbeet in 2017. In 2018, images were collected every week from the V4 to R2 growth stages in corn, and from the V4 to V15 stages in sugarbeet. In addition to red normalized vegetation index (RNDVI) and red edge normalized vegetation index (RENDVI), crop height was determined from UAV based digital terrain and digital surface models. Yield prediction (YP) model was derived from the linear regression between crop yield and vegetation indices. For corn‐YP model, R 2 value increased over the growing period and optimized at the R1 growth stage. Considering 3 site‐years, RENDVI was the best predictor for corn YP than other VIs based on the maximum R 2 value. For sugarbeet YP, model R 2 value declined over the growing season and optimized at V7 or V10 growth stages. Considering 4 site‐years, RNDVI was best related to root yield and recoverable sugar yield. Drone‐based remote sensing can be successfully used for corn and sugarbeet YP. Drone‐based remote sensing has potential in corn and sugarbeet YP, but it varied over growing seasons. Core Ideas Drone‐based passive optical sensor can be used to predict crop's yield. Red‐ and red edge‐normalized vegetation index and crop height are potential indices. Red edge normalized vegetation index best correlated with corn yield. Red normalized vegetation index best correlated with root yield prediction. Over growing season predictability improved for corn but declined for sugarbeet.