Predicting Grapevine Water Status Based on Hyperspectral Reflectance Vegetation Indices

Abstract

Several vegetation indices (VI) derived from handheld spectroradiometer reflectance data in the visible spectral region were 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 between VIs and Ψpd. A linear regression was defined using a parameterization dataset. The correlation analysis between Ψpd and the VIs computed with the standard formulation showed relatively poor results, with values for squared Pearson correlation coefficient (r 2 ) smaller than 0.67. However, the results of r 2 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) showed the higher r 2 and stability index results. The equations obtained through the regression between measured Ψpd (Ψpd_obs) and optimal VARI and between Ψpd_obs and optimal NDGI when using the parameterization dataset were adopted for predicting Ψpd using a testing dataset. The comparison of Ψpd_obs with Ψpd predicted based on VARI led to R 2 = 0.79 and a regression coefficient b = 0.96. Similar R 2 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.

No-Reference Hyperspectral Image Quality Assessment via Quality-Sensitive Features Learning

“…2017, 9, 305 7 of 24 In order to further exploit the texture information, we also analyze the statistics of directional gradient of the Log-Gabor filtering response map. The vertical gradient of 1,3 o is shown in Figure 8a. The histograms of the vertical gradient of 1,3 o under different distortions are given in Figure 8b.…”

“…The histogram follows Weibull distribution [27,28] In addition to directional gradient, gradient magnitude of the Log-Gabor filtering response map is also analyzed. The gradient magnitude of 1,3 o is shown in Figure 9a …”

“…We randomly crop 200 pristine sub-images of size 64 × 64 × 224 from the AVIRIS dataset, and introduce different kinds of distortions to each sub-image. We apply Log-Gabor filters on each sub-image, then we fit the histograms of 1,3 o and the vertical gradient of In addition to directional gradient, gradient magnitude of the Log-Gabor filtering response map is also analyzed. The gradient magnitude of 1,3 o is shown in Figure 9a.…”

“…We apply Log-Gabor filters on each sub-image, then we fit the histograms of 1,3 o and the vertical gradient of In addition to directional gradient, gradient magnitude of the Log-Gabor filtering response map is also analyzed. The gradient magnitude of 1,3 o is shown in Figure 9a. The histograms of gradient magnitude of 1,3 o under different distortions are presented in Figure 9b.…”

“…Hyperspectral image (HSI) with rich spatial and spectral information of the scene is useful in many fields such as mineral exploitation, agriculture, and environment management [1][2][3]. To improve the quality of the acquired HSI due to limited spatial resolution, super-resolution is an important enhancement technique [4][5][6][7][8][9][10][11][12].…”