Full TGIF Record # 328692
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Web URL(s):https://archive.lib.msu.edu/tic/ressum/2022/2022.pdf#page=240
    Last checked: 06/07/2023
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Author(s):McCall, David; Roberson, Travis
Author Affiliation:Virginia Tech
Title:Optimizing irrigation strategies through remote stress detection
Section:Integrated turfgrass management
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Ecophysiology: Water
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Source:Mike Davis Program for Advancing Golf Course Management: 2022 Progress Reports. 2022, p. 233-238.
Publishing Information:Liberty Corner, New Jersey: The United States Golf Association Green Section
# of Pages:6
See Also:Other Reports from this USGA research project: 2022-07-750
Note:Pictures, color
USGA Summary Points:Our data suggests that several vegetation indices (VI) collected through ground-based spectral data are influenced by atmospheric dew deposition, specifically popular VI such as the Normalized Difference Vegetation Index, the Normalized Green-to-Blue Index, the Water Band Index excluding the Visible Atmospheric Reflective Index. Creeping bentgrass consistently had more dew deposition on the surface that hybrid bermudagrass suggesting that it would be more critical to remove the dew from the surface before any form of spectral data collection is to occur. Removal of dew by means of rolling or light irrigation had no affect on the spectral data collected compared to leaving the dew on the surface unaffected, suggesting that minimal to no water on the surface does not diffuse or scatter solar radiation, thus affecting the spectral data collected.
ASA/CSSA/SSSA Citation (Crop Science-Like - may be incomplete):
McCall, D., and T. Roberson. 2022. Optimizing irrigation strategies through remote stress detection. USGA Turfgrass Environ. Res. Summ. p. 233-238.
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    Last checked: 06/07/2023
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    Notes: Item is within a single large file
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MSU catalog number: b3609415
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