ArticleRemote sensing2022
Retrieval of Crop Variables from Proximal Multispectral UAV Image Data Using PROSAIL in Maize Canopy.
Article in Remote sensing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- Multi-sensor phenotyping of yield and yield stability for genotype selection in durum wheat.Plant phenomics (Washington, D.C.) · 2026Article
- Exploring the depth of the maize canopy LAI detected by spectroscopy based on simulations and in situ measurements.Plant phenomics (Washington, D.C.) · 2025Article
- A Portable High-Resolution Snapshot Multispectral Imaging Device Leveraging Spatial and Spectral Features for Non-Invasive Corn Nitrogen Treatment Classification.Sensors (Basel, Switzerland) · 2025Article
- Field phenotyping of ten wheat cultivars under elevated COFrontiers in plant science · 2023Article
- A Generic Model to Estimate Wheat LAI over Growing Season Regardless of the Soil-Type Background.Plant phenomics (Washington, D.C.) · 2023Article
- Daytime and seasonal reflectance of maize grown in varying compass directions.Frontiers in plant science · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Mapping crop variables at different growth stages is crucial to inform farmers and plant breeders about the crop status. For mapping purposes, inversion of canopy radiative transfer models (RTMs) is a viable alternative to parametric and non-parametric regression models, which often lack transferability in time and space. Due to the physical nature of RTMs, inversion outputs can be delivered in sound physical units that reflect the underlying processes in the canopy. In this study, we explored the capabilities of the coupled leaf-canopy RTM PROSAIL applied to high-spatial-resolution (0.015 m) multispectral unmanned aerial vehicle (UAV) data to retrieve the leaf chlorophyll content (LCC), leaf area index (LAI) and canopy chlorophyll content (CCC) of sweet and silage maize throughout one growing season. Two different retrieval methods were tested: (i) applying the RTM inversion scheme to mean reflectance data derived from single breeding plots (mean reflectance approach) and (ii) applying the same inversion scheme to an orthomosaic to separately retrieve the target variables for each pixel of the breeding plots (pixel-based approach). For LCC retrieval, soil and shaded pixels were removed by applying simple vegetation index thresholding. Retrieval of LCC from UAV data yielded promising results compared to ground measurements (sweet maize RMSE = 4.92 μg/cm
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Registered trials
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