Improving Soil Moisture Assessment of Turfgrass Systems Utilizing Field Radiometry
The 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 irrigat...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Agronomy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4395/11/10/1960 |
| _version_ | 1797411011798499328 |
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| author | Travis L. Roberson Mike J. Badzmierowski Ryan D. Stewart Erik H. Ervin Shawn D. Askew David S. McCall |
| author_facet | Travis L. Roberson Mike J. Badzmierowski Ryan D. Stewart Erik H. Ervin Shawn D. Askew David S. McCall |
| author_sort | Travis L. Roberson |
| collection | DOAJ |
| description | The 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. |
| first_indexed | 2024-03-09T04:38:49Z |
| format | Article |
| id | doaj.art-2d31dc9407c3491a953e5d319cfe7c66 |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-09T04:38:49Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-2d31dc9407c3491a953e5d319cfe7c662023-12-03T13:23:11ZengMDPI AGAgronomy2073-43952021-09-011110196010.3390/agronomy11101960Improving Soil Moisture Assessment of Turfgrass Systems Utilizing Field RadiometryTravis L. Roberson0Mike J. Badzmierowski1Ryan D. Stewart2Erik H. Ervin3Shawn D. Askew4David S. McCall5School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA 24061, USASchool of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA 24061, USASchool of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA 24061, USADepartment of Plant and Soil Sciences, University of Delaware, Newark, DE 19716, USASchool of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA 24061, USASchool of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA 24061, USAThe 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.https://www.mdpi.com/2073-4395/11/10/1960green-to-red ratio indexhyperspectral reflectanceirrigationnormalized difference vegetation indextime-domain reflectometerturfgrass quality |
| spellingShingle | Travis L. Roberson Mike J. Badzmierowski Ryan D. Stewart Erik H. Ervin Shawn D. Askew David S. McCall Improving Soil Moisture Assessment of Turfgrass Systems Utilizing Field Radiometry Agronomy green-to-red ratio index hyperspectral reflectance irrigation normalized difference vegetation index time-domain reflectometer turfgrass quality |
| title | Improving Soil Moisture Assessment of Turfgrass Systems Utilizing Field Radiometry |
| title_full | Improving Soil Moisture Assessment of Turfgrass Systems Utilizing Field Radiometry |
| title_fullStr | Improving Soil Moisture Assessment of Turfgrass Systems Utilizing Field Radiometry |
| title_full_unstemmed | Improving Soil Moisture Assessment of Turfgrass Systems Utilizing Field Radiometry |
| title_short | Improving Soil Moisture Assessment of Turfgrass Systems Utilizing Field Radiometry |
| title_sort | improving soil moisture assessment of turfgrass systems utilizing field radiometry |
| topic | green-to-red ratio index hyperspectral reflectance irrigation normalized difference vegetation index time-domain reflectometer turfgrass quality |
| url | https://www.mdpi.com/2073-4395/11/10/1960 |
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