Can time series of multispectral satellite images be used to estimate stem water potential in vineyards?

Y. Cohen*, P. Gogumalla, I. Bahat, Y. Netzer, A. Ben-Gal, I. Lenski, Y. Michael, D. Helman

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

Vegetation indices (VI) derived from a new generation of high-spatial resolution satellites, namely Planet and Sentinel-2, were tested as proxies for stem water potential (Ψ-stem) in commercial vineyards. Multivariable linear regression models were developed from the Planet and Sentinel-2 data and in-situ Ψ-stem measurements in 82 vineyards in Israel, providing Ψ-stem estimates every 1-2 weeks. With multivariable regression VIs in the VIS-NIR region (Planet) and moisture VIs in the NIR-SWIR region (Sentinel-2) were correlated with in-situ Ψ-stem measurements. In general, Sentinel-based models performed better (higher correlations and higher ability to capture temporal and spatial variability) than Planet-based models. The trade-off between spectral, spatial and temporal resolutions of these two satellites are further discussed.

Original languageAmerican English
Title of host publicationPrecision Agriculture 2019 - Papers Presented at the 12th European Conference on Precision Agriculture, ECPA 2019
EditorsJohn V. Stafford
PublisherWageningen Academic Publishers
Pages445-451
Number of pages7
ISBN (Electronic)9789086863372
DOIs
StatePublished - 2019
Externally publishedYes
Event12th European Conference on Precision Agriculture, ECPA 2019 - Montpellier, France
Duration: 8 Jul 201911 Jul 2019

Publication series

NamePrecision Agriculture 2019 - Papers Presented at the 12th European Conference on Precision Agriculture, ECPA 2019

Conference

Conference12th European Conference on Precision Agriculture, ECPA 2019
Country/TerritoryFrance
CityMontpellier
Period8/07/1911/07/19

Bibliographical note

Publisher Copyright:
© Wageningen Academic Publishers 2019

Keywords

  • Google earth engine
  • Planet-labs
  • Sentinel-2
  • Stem water potential

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