Comparison of PROSAIL Model Inversion Methods for Estimating Leaf Chlorophyll Content and LAI Using UAV Imagery for Hemp Phenotyping

Impollonia, Giorgio; Croci, Michele; Blandinières, Henri; Marcone, Andrea; Amaducci, Stefano

doi:10.3390/rs14225801

Open AccessArticle

Comparison of PROSAIL Model Inversion Methods for Estimating Leaf Chlorophyll Content and LAI Using UAV Imagery for Hemp Phenotyping

by

Giorgio Impollonia

^1,2,*

,

Michele Croci

^1,2

,

Henri Blandinières

¹,

Andrea Marcone

^1,2 and

Stefano Amaducci

^1,2

¹

Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, 29122 Piacenza, Italy

²

Remote Sensing and Spatial Analysis Research Center (CRAST), Università Cattolica del Sacro Cuore, 29122 Piacenza, Italy

^*

Author to whom correspondence should be addressed.

Remote Sens. 2022, 14(22), 5801; https://doi.org/10.3390/rs14225801

Submission received: 28 September 2022 / Revised: 12 November 2022 / Accepted: 15 November 2022 / Published: 17 November 2022

(This article belongs to the Special Issue UAS Technology and Applications in Precision Agriculture)

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Abstract

Unmanned aerial vehicle (UAV) remote sensing was used to estimate the leaf area index (LAI) and leaf chlorophyll content (LCC) of two hemp cultivars during two growing seasons under four nitrogen fertilisation levels. The hemp traits were estimated by the inversion of the PROSAIL model from UAV multispectral images. The look-up table (LUT) and hybrid regression inversion methods were compared. The hybrid methods performed better than LUT methods, both for LAI and LCC, and the best accuracies were achieved by random forest for the LAI (0.75 m² m⁻² of RMSE) and by Gaussian process regression for the LCC (9.69 µg cm⁻² of RMSE). High-throughput phenotyping was carried out by applying a generalised additive model to the time series of traits estimated by the PROSAIL model. Through this approach, significant differences in LAI and LCC dynamics were observed between the two hemp cultivars and between different levels of nitrogen fertilisation.

Keywords: Cannabis sativa L.; precision agriculture; UAV remote sensing; multispectral images; PROSAIL; LUT; machine learning; trait estimation; high-throughput phenotyping

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MDPI and ACS Style

Impollonia, G.; Croci, M.; Blandinières, H.; Marcone, A.; Amaducci, S. Comparison of PROSAIL Model Inversion Methods for Estimating Leaf Chlorophyll Content and LAI Using UAV Imagery for Hemp Phenotyping. Remote Sens. 2022, 14, 5801. https://doi.org/10.3390/rs14225801

AMA Style

Impollonia G, Croci M, Blandinières H, Marcone A, Amaducci S. Comparison of PROSAIL Model Inversion Methods for Estimating Leaf Chlorophyll Content and LAI Using UAV Imagery for Hemp Phenotyping. Remote Sensing. 2022; 14(22):5801. https://doi.org/10.3390/rs14225801

Chicago/Turabian Style

Impollonia, Giorgio, Michele Croci, Henri Blandinières, Andrea Marcone, and Stefano Amaducci. 2022. "Comparison of PROSAIL Model Inversion Methods for Estimating Leaf Chlorophyll Content and LAI Using UAV Imagery for Hemp Phenotyping" Remote Sensing 14, no. 22: 5801. https://doi.org/10.3390/rs14225801

APA Style

Impollonia, G., Croci, M., Blandinières, H., Marcone, A., & Amaducci, S. (2022). Comparison of PROSAIL Model Inversion Methods for Estimating Leaf Chlorophyll Content and LAI Using UAV Imagery for Hemp Phenotyping. Remote Sensing, 14(22), 5801. https://doi.org/10.3390/rs14225801

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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Comparison of PROSAIL Model Inversion Methods for Estimating Leaf Chlorophyll Content and LAI Using UAV Imagery for Hemp Phenotyping

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