Next Article in Journal
Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data
Previous Article in Journal
The Mangroves of the Zambezi Delta: Increase in Extent Observed via Satellite from 1994 to 2013
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2015, 7(12), 16460-16479; doi:10.3390/rs71215835

Predicting Grapevine Water Status Based on Hyperspectral Reflectance Vegetation Indices

Linking Landscape, Environment, Agriculture and Food, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, Lisboa 1349-017, Portugal
Geo-Space Sciences Research Centre, (CICGE), Rua do Campo Alegre, Porto 4169-007, Portugal
Faculdade de Ciências da Universidade do Porto, Rua do Campo Alegre, Porto 4169-007, Portugal
Associação para o Desenvolvimento da Viticultura Duriense, Quinta de Sta. Maria, Apartado 137, Godim 5050-106, Portugal
Author to whom correspondence should be addressed.
Academic Editors: Clement Atzberger and Prasad S. Thenkabail
Received: 25 August 2015 / Accepted: 27 November 2015 / Published: 5 December 2015
View Full-Text   |   Download PDF [2043 KB, uploaded 8 December 2015]   |  


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 (r2) smaller than 0.67. However, 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) showed the higher r2 and stability index results. The equations obtained through the regression between measured Ψpdpd_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 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. View Full-Text
Keywords: Douro region; remote sensing; handheld spectroradiometer; predawn leaf water potential; VARI index; vineyards water management Douro region; remote sensing; handheld spectroradiometer; predawn leaf water potential; VARI index; vineyards water management

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Pôças, I.; Rodrigues, A.; Gonçalves, S.; Costa, P.M.; Gonçalves, I.; Pereira, L.S.; Cunha, M. Predicting Grapevine Water Status Based on Hyperspectral Reflectance Vegetation Indices. Remote Sens. 2015, 7, 16460-16479.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top