Title: Stem water potential monitoring in pear orchards through Worldview 2 multispectral imagery
Authors: Van Beek, Jonathan ×
Tits, Laurent
Somers, Ben
Coppin, Pol #
Issue Date: 2014
Publisher: Molecular Diversity Preservation International (MDPI)
Series Title: Remote Sensing vol:5 issue:12 pages:6647-6666
Abstract: Remote sensing can provide good alternatives for traditional in situ water status measurements in orchard crops, such as stem water potential (Ψstem). However, the heterogeneity of these cropping systems causes significant differences with regards to remote sensing products within one orchard and between orchards. In this study, robust spectral indicators of Ψstem were sought after, independent of sensor viewing geometry, orchard architecture and management. To this end, Ψstem was monitored throughout three consecutive growing seasons in (deficit) irrigated and rainfed pear orchards and related to spectral observations of leaves, canopies and WorldView-2 imagery. On a leaf and canopy level, high correlations were observed between the shortwave infrared reflectance and in situ measured Ψstem. Additionally, for canopy measurements, visible and near-infrared wavelengths (R530/R600, R530/R700 and R720/R800) showed significant correlations. Therefore, the Red-edge Normalized Difference Vegetation Index (ReNDVI) was applied on fully sunlit satellite imagery and found strongly related with Ψstem (R2 = 0.47; RMSE = 0.36 MPa), undoubtedly showing the potential of WorldView-2 to monitor water stress in pear orchards. The relationship between ReNDVI and Ψstem was independent of management, irrigation setup, phenology and environmental conditions. In addition, results showed that this relation was also independent of off-nadir viewing angle and almost independent of viewing geometry, as the correlation decreased after the inclusion of fully shaded scenes. With further research focusing on issues related to viewing geometry and shadows, high spatial water status monitoring with space borne remote sensing is achievable.
ISSN: 2072-4292
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Division Forest, Nature and Landscape Research
Division M3-BIORES: Measure, Model & Manage Bioresponses (-)
× corresponding author
# (joint) last author

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