Using Small Unmanned Aircraft Systems for Early Detection of Drought Stress in Turfgrass

Abstract

Recent advances in small unmanned aircraft systems (sUAS) and sensors may improve accuracy and efficiency in turfgrass research and management compared with conventional methods. We evaluated the ability of sUAS combined with ultra‐high spatial resolution remote sensing to detect early drought stress. Results were compared with ground‐based techniques in creeping bentgrass (Agrostis stolonifera L.) irrigated at different levels from well‐watered to severe deficit (100 to 15% evapotranspiration [ET] replacement). Small UAS‐based measurements with a modified digital camera included three reflectance bands (near infrared [NIR, 680–780 nm] and overlapping green [G] and blue [B] bands [400–580 nm]) and eight derived vegetation indices (VIs). Ground‐based measurements included soil volumetric water content (VWC), turfgrass quality (TQ), green cover (GC), soil temperature (Tₛₒᵢₗ), and reflectance with handheld optical sensors. Declines in VWC in deficit‐irrigation treatments were detected with NIR and six of eight VIs from sUAS, and the normalized difference vegetation index (NDVI) and red band reflectance from a handheld sensor, before symptoms appeared in TQ and GC. The most consistently sensitive parameters of sUAS throughout the 3‐yr study were NIR and GreenBlue VI [(G − B)/(G + B)], which detected drought stress >5 d before decreases in TQ. Results indicate that ultra‐high spatial resolution remote sensing with sUAS detected drought stress before it was visible to a human observer and could be valuable for improving irrigation management in turfgrass.

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