Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Several vegetation indices (VI) derived from handheld spectroradiometer reflectance data in the visible spectral region re tested for modelling grapevine water status estimated by the predawn leaf water potential (Ψpd). The experimental trial was carried out in a vineyard in Douro wine region, Portugal. A statistical approach was used to evaluate which VI and which combination of wavelengths per VI allows the best correlation beten VIs and Ψpd. A linear regression was defined using a parameterization dataset. The correlation analysis beten Ψpd and the VIs computed with the standard formulation shod relatively poor results, with values for squared Pearson correlation coefficient (r2) smaller than 0.67. Hover, the results of r2 highly improved for all VIs when computed with the selected best combination of wavelengths (optimal VIs). The optimal Visible Atmospherically Resistant Index (VARI) and Normalized Difference Greenness Vegetation Index (NDGI) shod the higher r2 and stability index results. The equations obtained through the regression beten measured Ψpd (Ψpd_obs) and optimal VARI and beten Ψpd_obs and optimal NDGI when using the parameterization dataset re adopted for predicting Ψpd using a testing dataset. The comparison of Ψpd_obs with Ψpd predicted based on VARI led to R2 = 0.79 and a regression coefficient b = 0.96. Similar R2 was achieved for the prediction based on NDGI, but b was smaller (b = 0.93). Results obtained allow the future use of optimal VARI and NDGI for estimating Ψpd, supporting vineyards irrigation management.
['Isabel Pôças', 'Arlete Rodrigues', 'Sara Gonçalves', 'Patrícia Monteiro Costa', 'Igor Severo Gonçalves', 'Luis S. Pereira', 'Mário Cunha']
I am writing a research paper on a idea Predicting Grapevine Water Status Based on Hyperspectral Reflectance Vegetation Indices
[{"from": "human", "value": "I am writing a research paper on a idea Predicting Grapevine Water Status Based on Hyperspectral Reflectance Vegetation Indices"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Several vegetation indices (VI) derived from handheld spectroradiometer reflectance data in the visible spectral region re tested for modelling grapevine water status estimated by the predawn leaf water potential (\u03a8pd). The experimental trial was carried out in a vineyard in Douro wine region, Portugal. A statistical approach was used to evaluate which VI and which combination of wavelengths per VI allows the best correlation beten VIs and \u03a8pd. A linear regression was defined using a parameterization dataset. The correlation analysis beten \u03a8pd and the VIs computed with the standard formulation shod relatively poor results, with values for squared Pearson correlation coefficient (r2) smaller than 0.67. Hover, the results of r2 highly improved for all VIs when computed with the selected best combination of wavelengths (optimal VIs). The optimal Visible Atmospherically Resistant Index (VARI) and Normalized Difference Greenness Vegetation Index (NDGI) shod the higher r2 and stability index results. The equations obtained through the regression beten measured \u03a8pd (\u03a8pd_obs) and optimal VARI and beten \u03a8pd_obs and optimal NDGI when using the parameterization dataset re adopted for predicting \u03a8pd using a testing dataset. The comparison of \u03a8pd_obs with \u03a8pd predicted based on VARI led to R2 = 0.79 and a regression coefficient b = 0.96. Similar R2 was achieved for the prediction based on NDGI, but b was smaller (b = 0.93). Results obtained allow the future use of optimal VARI and NDGI for estimating \u03a8pd, supporting vineyards irrigation management."}]