drought, prediction, drone, reflectance, vegetation index
This study was conducted to evaluate early detection ability of small unmanned aerial systems (sUAS) technology for drought stress on turfgrass. Certain reflectances collected by sUAS and a handheld device declined more in less irrigated treatments before drought stress was evident in visual quality rating (VQ) and percentage green cover (PGC). The near infrared (NIR) band and GreenBlue vegetation index performed the best consistently for drought stress prediction among the other vegetation indices (VI) or bands from sUAS. Results indicate using ultra-high resolution remote sensing with sUAS can detect drought stress as well as, if not better than, a handheld device before it is visible to the human eye, and may provide valuable evidence for irrigation management in turfgrass.
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Hong, Mu; Bremer, Dale; and van der Merwe, Deon
"Evaluating Small Unmanned Aerial Systems for Detecting Drought Stress on Turfgrass,"
Kansas Agricultural Experiment Station Research Reports: