Combining leaf physiology, hyperspectral imaging and partial least squares-regression (PLS-R) for grapevine water status assessment

Tal Rapaport, Uri Hochberg, Maxim Shoshany, Arnon Karnieli, Shimon Rachmilevitch

Research output: Contribution to journalArticlepeer-review

64 Scopus citations

Abstract

Physiological measurements are considered to be the most accurate way of assessing plant water status, but they might also be time-consuming, costly and intrusive. Since visible (VIS)-to-shortwave infrared (SWIR) imaging spectrometers are able to monitor various bio-chemical alterations in the leaf, such narrow-band instruments may offer a faster, less expensive and non-destructive alternative. This requires an intelligent downsizing of broad and noisy hyperspectra into the few most physiologically-sensitive wavelengths. In the current study, hyperspectral signatures of water-stressed grapevine leaves (Vitis vinifera L. cv. Cabernet Sauvignon) were correlated to values of midday leaf water potential (Ψl), stomatal conductance (gs) and non-photochemical quenching (NPQ) under controlled conditions, using the partial least squares-regression (PLS-R) technique. It was found that opposite reflectance trends at 530-550nm and around 1500nm - associated with independent changes in photoprotective pigment contents and water availability, respectively - were indicative of stress-induced alterations in Ψl, gs and NPQ. Furthermore, combining the spectral responses at these VIS and SWIR regions yielded three normalized water balance indices (WABIs), which were superior to various widely-used reflectance models in predicting physiological values at both the leaf and canopy levels. The potential of the novel WABI formulations also under field conditions demonstrates their applicability for water status monitoring and irrigation scheduling.

Original languageEnglish
Pages (from-to)88-97
Number of pages10
JournalISPRS Journal of Photogrammetry and Remote Sensing
Volume109
DOIs
StatePublished - 1 Nov 2015

Keywords

  • Grapevine
  • Hyperspectral imaging
  • PLS-R
  • Physiology
  • Remote sensing
  • Water stress

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