@inproceedings{1c9376ac1f364d32b11224594518ab32,
title = "Can time series of multispectral satellite images be used to estimate stem water potential in vineyards?",
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.",
keywords = "Google earth engine, Planet-labs, Sentinel-2, Stem water potential",
author = "Y. Cohen and P. Gogumalla and I. Bahat and Y. Netzer and A. Ben-Gal and I. Lenski and Y. Michael and D. Helman",
note = "Publisher Copyright: {\textcopyright} Wageningen Academic Publishers 2019; 12th European Conference on Precision Agriculture, ECPA 2019 ; Conference date: 08-07-2019 Through 11-07-2019",
year = "2019",
doi = "10.3920/978-90-8686-888-9_55",
language = "אנגלית",
series = "Precision Agriculture 2019 - Papers Presented at the 12th European Conference on Precision Agriculture, ECPA 2019",
publisher = "Wageningen Academic Publishers",
pages = "445--451",
editor = "Stafford, {John V.}",
booktitle = "Precision Agriculture 2019 - Papers Presented at the 12th European Conference on Precision Agriculture, ECPA 2019",
address = "הולנד",
}