Temporal resolution trumps spectral resolution in UAV-based monitoring of cereal senescence dynamics

Remote-sensing based proxies derived from both RGB and multispectral sensors can capture the senescence process accurately. The sub-sampling emphasized the importance of timely and frequent assessments, but also highlighted the need for robust methods that enable such frequent assessments to be made under variable environmental conditions. The proposed measurement and data processing strategies can improve the measurement and understanding of senescence dynamics, facilitating adaptive crop breeding strategies in the context of climate change.

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Publication Details

Journal
Plant Methods
Published
2024-12-19
DOI
https://doi.org/10.1186/s13007-024-01308-x
Citations
8
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
3.67

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article

Temporal resolution trumps spectral resolution in UAV-based monitoring of cereal senescence dynamics

Jonas Anderegg, Flavian Tschurr, Achim Walter, Lukas Roth et al.
8 citations
Plant Methods
Remote Sensing in Agriculture
3.67
article

Temporal resolution trumps spectral resolution in UAV-based monitoring of cereal senescence dynamics

Jonas Anderegg, Flavian Tschurr, Achim Walter, Lukas Roth, Olivia Zumsteg, Nicola Storni
article en
8 citations

Abstract

Remote-sensing based proxies derived from both RGB and multispectral sensors can capture the senescence process accurately. The sub-sampling emphasized the importance of timely and frequent assessments, but also highlighted the need for robust methods that enable such frequent assessments to be made under variable environmental conditions. The proposed measurement and data processing strategies can improve the measurement and understanding of senescence dynamics, facilitating adaptive crop breeding strategies in the context of climate change.

Plant MethodsVol. 20(1)
ETH Zurich (CH)
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
Climate action
Openalex Percentile: Top 7%
Remote Sensing in Agriculture
3.67
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