0000000000986019

AUTHOR

Martin Schlerf

showing 2 related works from this author

Comparison of Crop Trait Retrieval Strategies Using UAV-Based VNIR Hyperspectral Imaging.

2021

Hyperspectral cameras onboard unmanned aerial vehicles (UAVs) have recently emerged for monitoring crop traits at the sub-field scale. Different physical, statistical, and hybrid methods for crop trait retrieval have been developed. However, spectra collected from UAVs can be confounded by various issues, including illumination variation throughout the crop growing season, the effect of which on the retrieval performance is not well understood at present. In this study, four retrieval methods are compared, in terms of retrieving the leaf area index (LAI), fractional vegetation cover (fCover), and canopy chlorophyll content (CCC) of potato plants over an agricultural field for six dates duri…

Canopystatistical method010504 meteorology & atmospheric sciencesScience0211 other engineering and technologiesGrowing season02 engineering and technologyLUT-based inversion; hybrid method; statistical method; leaf area index; fractional vegetation cover; canopy chlorophyll content01 natural sciencesLUT-based inversionhybrid methodLeaf area index021101 geological & geomatics engineering0105 earth and related environmental sciencesMathematicsRemote sensingfractional vegetation coverleaf area indexQHyperspectral imagingcanopy chlorophyll contentStatistical modelRandom forestVNIRGeneral Earth and Planetary SciencesScale (map)Remote sensing
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Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review

2022

Remote detection and monitoring of the vegetation responses to stress became relevant for sustainable agriculture. Ongoing developments in optical remote sensing technologies have provided tools to increase our understanding of stress-related physiological processes. Therefore, this study aimed to provide an overview of the main spectral technologies and retrieval approaches for detecting crop stress in agriculture. Firstly, we present integrated views on: i) biotic and abiotic stress factors, the phases of stress, and respective plant responses, and ii) the affected traits, appropriate spectral domains and corresponding methods for measuring traits remotely. Secondly, representative result…

Precision agriculturemultispectralbiotic and abiotic stresatelliteSoil Sciencesolar induced fluorescenceGeologymulti-modalPrecision agriculture multi-modal solar-induced fluorescence satellite hyperspectral multispectral biotic and abiotic stressUNESCO::CIENCIAS TECNOLÓGICASITC-HYBRIDhyperspectralITC-ISI-JOURNAL-ARTICLEddc:550Computers in Earth Sciences
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