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14 May 2019 Using hyperspectral imagery to detect water stress in vineyards
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Abstract
The purpose of this study is to examine water stress as determined by the stem water potential of two blocks of a commercial California vineyard growing two different varietals: Chenin Blanc and Cabernet. Hyperspectral reflectance data was collected from a system capturing spectra from 400 nm to 1,000 nm at a spectral resolution of 4 nm. The sensor was carried at an altitude of 75 meters above ground level by a helicopter UAS, producing a ground resolution of 3 cm. Sixty-six standard vegetative indices found in the literature were examined to see how well they predicted stem water potential utilizing simple linear regression. The five vegetative indices most related to stem water potential in terms of the coefficient of determination were the Photochemical Reflectance Index, the Green Red Ratio Index, the Ratio Vegetation Index, the Simple Ratio Index, and the Greenness Index. These indices had a coefficient of variation around 0.3. Nearly a third of the vegetative indices had a coefficient of determination less than 0.1 including the Water Band Index, the Water Index, and the Floating Position Water Band Index. Correlations of vegetative indices with stem water potentials were not improved by examining the two varietals separately.
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© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Sean P. Hurley, Marc Horney, and Aaron Drake "Using hyperspectral imagery to detect water stress in vineyards", Proc. SPIE 11008, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV, 1100807 (14 May 2019); https://doi.org/10.1117/12.2518660
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