Assessing infield temporal and spatial variability of leaf water potential using satellite imagery and meteorological data

In: Precision agriculture '19
Authors:
O. Beeri Manna-Irrigation, Gvat-Yagur 1, Gvat 3657900, Israel.

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,
R. Pelta Manna-Irrigation, Gvat-Yagur 1, Gvat 3657900, Israel.

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T. Shilo Manna-Irrigation, Gvat-Yagur 1, Gvat 3657900, Israel.

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J. Raz Manna-Irrigation, Gvat-Yagur 1, Gvat 3657900, Israel.

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S. Mey-tal Manna-Irrigation, Gvat-Yagur 1, Gvat 3657900, Israel.

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Type:
Chapter
Pages:
263–270
DOI:
https://doi.org/10.3920/978-90-8686-888-9_32

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The goal of this study was to examine whether the combination of meteorological data along with satellite imagery can be used to represent the spatial and temporal changes in cotton leaf water potential (LWP). Daily plant measurements showed a decrease in LWP after each irrigation event and then a gradual increase until the next irrigation event. However, in most events, the lowest LWP was on the day after irrigation and sometimes even two days after. These changes generally correlated to seasonal meteorological changes but were not sensitive to the daily LWP changes. In contrast, spectral indices based on satellite imagery were able to map infield LWP variability and after calibration enabled the estimation of LWP with an RMSE of 0.26 MPa and R2 of 0.72, regardless of sensor type.

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Cover Precision agriculture '19
E-Book ISBN:
9789086868889
Publisher:
Wageningen Academic
Print Publication Date:
08 Jul 2019
Front Matter
Section 1 – Precision agriculture
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Section 2 – Precision horticulture
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Section 3 – Precision viticulture
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Section 4 – Precision crop protection
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Section 5 – Proximal and remote sensing of soil and crop
Design of a portable sensor suite for real-time monitoring of crop water stress index in maize breeding plots
Characterisation of fungal diseases on winter wheat crop using proximal and remote multispectral imaging
Characterisation of cereal morphology through proximal stereo vision under contrasting nitrogen inputs
Early detection of corn and sunflower stress induced by chemical spraying
Hyperspectral imaging application under field conditions: assessment of the spatio-temporal variability of grape composition within a vineyard
Potential of optical sensors for nitrogen management in spring barley
Disentangling the sources of chlorophyll-content variability in banana fields
Sentinel-2 vegetation indices and apparent electrical conductivity to predict barley (Hordeum vulgare L.) yield
Accessing the plant architecture in 3D for plant phenotyping - recent approaches and requirements
Vegetation indices from remote sensing imagery as proxies for yield and grain N in wheat
Using terrestrial photogrammetry for leaf area estimation in maize under different plant growth stages
Early detection of Fusarium infection in corn using spectral analysis
Proximal versus remote sensing to monitor pasture quality in a Mediterranean Montado ecosystem
Section 6 – Applications of unmanned aerial systems
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Section 7 – Satellite-based applications for precision agriculture
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Section 8 – Site-specific nutrient, lime and seed management
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Section 9 – Site-specific canopy and harvest quality
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Section 10 – Precision pasture management
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Section 11 – Drainage optimization and variable rate irrigation
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Section 12 – Geostatistics, mapping and spatial data analysis
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Section 13 – Robotics, guidance and automation
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Section 14 – Economics of precision agriculture
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Section 15 – Adoption of precision agriculture
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Section 16 – Small farms/small holders and precision agriculture
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Section 17 – On farm experimentation with site-specific technologies
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Section 18 – Software and mobile apps for precision agriculture
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Section 19 – Decision support for precision agriculture
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Section 20 – Data mining for precision agriculture
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Back Matter

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