Abstract
AbstractCanopy vegetation indices can predict the spatial variability in grain yield before harvest across large spatial scale. Spring wheat (Triticum aestivum L.) canopy reflectance data using a hand‐held active sensor (RapidScan CS‐45) and a small unmanned aerial vehicle (sUAV)‐based passive light optical sensor (Micasense RedEdge) were collected three times during flag leaf to heading stages (in the month of June and July) from 16 site‐years across the Red River Valley of Minnesota and North Dakota. Potential of vegetation indices (VIs), red normalized vegetation (RNDVI) and red edge normalized vegetation (RENDVI), were compared to predict grain yield and protein content. Linear regression between two sensors showed a significant (p < .05) relationship based on RNDVI (R2 = .69) and RENDVI (R2 = .55) values (n = 48). Only for the 2nd year, RENDVI from both sensors could predict the grain yield; but only the RENDVI from sUAV‐based passive sensor could predict the protein content at Haun stages between five and eight (R2 = .60, RMSE = 14.8, p = .02). Use of sUAV based passive sensor has potential to predict protein content but selecting optimal growth stage and validation of developed regression models in neighboring fields are critical for the accurate prediction.
Published Version
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