Sentinel-2 vegetation indices and apparent electrical conductivity to predict barley (Hordeum vulgare L.) yield

In: Precision agriculture '19
Authors:
J.A. Martínez-Casasnovas Research Group in AgroICT & Precision Agriculture (GRAP), University of Lleida, Agrotecnio Center, Lleida, Catalonia, Spain.

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A. Uribeetxebarría Research Group in AgroICT & Precision Agriculture (GRAP), University of Lleida, Agrotecnio Center, Lleida, Catalonia, Spain.

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A. Escolà Research Group in AgroICT & Precision Agriculture (GRAP), University of Lleida, Agrotecnio Center, Lleida, Catalonia, Spain.

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J. Arnó Research Group in AgroICT & Precision Agriculture (GRAP), University of Lleida, Agrotecnio Center, Lleida, Catalonia, Spain.

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Type:
Chapter
Pages:
307–313
DOI:
https://doi.org/10.3920/978-90-8686-888-9_38

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The launch of the satellites of the Sentinel-2 mission has opened a new era of possibilities in precision agriculture. The objective of the present work was to explore the use of Sentinel-2 visible, red-edge and near infrared bands to predict barley (Hordeum vulgare L.) yield in early spring phenological stages at field level. Spectral vegetation indices for different dates were analysed together with apparent electrical conductivity (ECa) to obtain the best linear model to predict yield in critical moments of the barley cycle, in which it is still possible to apply variable rates of sidedress fertilizer to improve yield. The research was carried out in 2017 in a farm located in NE Spain. ECa surveys were by means of a Veris 3100 ECa surveyor and yield data were acquired by means of a yield monitor. Images acquired the first week of April (barley Zadoks DC 30-32) were the ones presenting the best correlation with yield. Multiple regression models resulted in R2 values of 0.75, confirming that vegetation indices from the first week of April produced lower prediction error than earlier dates.

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