Predicting Grapevine Water Status Based on Hyperspectral Reflectance Vegetation Indices

• Type: Article in International Scientific Journal
• Date: 2015

Authors

I. Poças (Author) FCUP; Rodrigues, A (Author) Other; Goncalves, S (Author) Other; Costa, PM (Author) Other; Goncalves, I (Author) Other; Pereira, LS (Author) Other; Mario Cunha (Author) FCUP

Journal

Title: Remote Sensing

Vol. 7

ISSN: 2072-4292

Publisher: MDPI

Indexing

ISI Web of Knowledge - 60 Citations

Scopus - 61 Citations

Other information

Authenticus ID: P-00J-ZZW

DOI: 10.3390/rs71215835

Abstract (EN): 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 ((pd)). 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 (pd). A linear regression was defined using a parameterization dataset. The correlation analysis between (pd) 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 (pd) ((pd_obs)) and optimal VARI and between (pd_obs) and optimal NDGI when using the parameterization dataset were adopted for predicting (pd) using a testing dataset. The comparison of (pd_obs) with (pd) 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 (pd), supporting vineyards irrigation management.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 20