Improving Soil Moisture Assessment of Turfgrass Systems Utilizing Field Radiometry

dc.contributor.authorRoberson, Travis L.en
dc.contributor.authorBadzmierowski, Mike J.en
dc.contributor.authorStewart, Ryan D.en
dc.contributor.authorErvin, Erik H.en
dc.contributor.authorAskew, Shawn D.en
dc.contributor.authorMcCall, David S.en
dc.date.accessioned2021-10-13T12:26:25Zen
dc.date.available2021-10-13T12:26:25Zen
dc.date.issued2021-09-29en
dc.date.updated2021-10-12T14:18:04Zen
dc.description.abstractThe need for water conservation continues to increase as global freshwater resources dwindle. Turfgrass mangers are adapting to these concerns by implementing new tools to reduce water consumption. Time-domain reflectometer (TDR) soil moisture sensors can decrease water usage when scheduling irrigation, but nonuniformity across unsampled locations creates irrigation inefficiencies. Remote sensing data have been used to estimate soil moisture stress in turfgrass systems through the normalized difference vegetation index (NDVI). However, numerous stressors other than moisture constraints impact NDVI values. The water band index (WBI) is an alternative index that uses narrowband, near-infrared light reflectance to estimate moisture limitations within the plant canopy. The green-to-red ratio index (GRI) is a vegetation index that has been proposed as a cheaper alternative to WBI as it can be measured using digital values of visible light instead of relying on more costly hyperspectral reflectance measurements. A replicated 2 × 3 factorial experimental design was used to repeatedly measure turf canopy reflectance and soil moisture over time as soils dried. Pots of ‘007’ creeping bentgrass (CBG) and ‘Latitude 36’ hybrid bermudagrass (HBG) were grown on three soil textures: United States Golf Association (USGA) 90:10 sand, loam, and clay. Reflectance data were collected hourly between 07:00 and 19:00 using a hyperspectral radiometer and volumetric water content (VWC) data were collected continuously using an embedded soil moisture sensor from soil saturation until complete turf necrosis by drought stress. The WBI had the strongest relationship to VWC (<i>r</i> = 0.62) compared to GRI (<i>r</i> = 0.56) and NDVI (<i>r</i> = 0.47). The WBI and GRI identified significant moisture stress approximately 28 h earlier than NDVI (<i>p</i> = 0.0010). Those metrics also predicted moisture stress prior to fifty percent visual estimation of wilt (<i>p</i> = 0.0317), with lead times of 12 h (WBI) and 9 h (GRI). By contrast, NDVI provided 2 h of prediction time. Nonlinear regression analysis showed that WBI and GRI can be useful for predicting moisture stress of CBG and HBG grown on three different soil textures in a controlled environment.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationRoberson, T.L.; Badzmierowski, M.J.; Stewart, R.D.; Ervin, E.H.; Askew, S.D.; McCall, D.S. Improving Soil Moisture Assessment of Turfgrass Systems Utilizing Field Radiometry. Agronomy 2021, 11, 1960.en
dc.identifier.doihttps://doi.org/10.3390/agronomy11101960en
dc.identifier.urihttp://hdl.handle.net/10919/105270en
dc.language.isoenen
dc.publisherMDPIen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectgreen-to-red ratio indexen
dc.subjecthyperspectral reflectanceen
dc.subjectirrigationen
dc.subjectnormalized difference vegetation indexen
dc.subjecttime-domain reflectometeren
dc.subjectturfgrass qualityen
dc.subjectvegetation indexen
dc.subjectsoil volumetric water contenten
dc.subjectwater band indexen
dc.titleImproving Soil Moisture Assessment of Turfgrass Systems Utilizing Field Radiometryen
dc.title.serialAgronomyen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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