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Retrieval of hyperspectral information from multispectral data for perennial ryegrass biomass estimation

Citation

Togeiro de Alckmin, G and Kooistra, L and Rawnsley, R and de Bruin, S and Lucieer, A, Retrieval of hyperspectral information from multispectral data for perennial ryegrass biomass estimation, Sensors, 20, (24) Article 7192. ISSN 1424-8220 (2020) [Refereed Article]


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Copyright Statement

Copyright: 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons 4.0 International (CC BY 4.0) license (https://creativecommons.org/licenses/by/4.0/).

DOI: doi:10.3390/s20247192

Abstract

The use of spectral data is seen as a fast and non-destructive method capable of monitoring pasture biomass. Although there is great potential in this technique, both end users and sensor manufacturers are uncertain about the necessary sensor specifications and achievable accuracies in an operational scenario. This study presents a straightforward parametric method able to accurately retrieve the hyperspectral signature of perennial ryegrass (Lolium perenne) canopies from multispectral data collected within a two-year period in Australia and the Netherlands. The retrieved hyperspectral data were employed to generate optimal indices and continuum-removed spectral features available in the scientific literature. For performance comparison, both these simulated features and a set of currently employed vegetation indices, derived from the original band values, were used as inputs in a random forest algorithm and accuracies of both methods were compared. Our results have shown that both sets of features present similar accuracies (root mean square error (RMSE) 490 and 620 kg DM/ha) when assessed in cross-validation and spatial cross-validation, respectively. These results suggest that for pasture biomass retrieval solely from top-of-canopy reflectance (ranging from 550 to 790 nm), better performing methods do not rely on the use of hyperspectral or, yet, in a larger number of bands than those already available in current sensors.

Item Details

Item Type:Refereed Article
Keywords:vegetation indices, spectral resampling, continuum-removal, parametric-regression, spectral simulation, machine learning, random-forest
Research Division:Engineering
Research Group:Geomatic engineering
Research Field:Photogrammetry and remote sensing
Objective Division:Animal Production and Animal Primary Products
Objective Group:Pasture, browse and fodder crops
Objective Field:Sown pastures (excl. lucerne)
UTAS Author:Togeiro de Alckmin, G (Mr Gustavo Togeiro de Alckmin)
UTAS Author:Rawnsley, R (Dr Richard Rawnsley)
UTAS Author:Lucieer, A (Professor Arko Lucieer)
ID Code:148022
Year Published:2020
Web of Science® Times Cited:1
Deposited By:Geography and Spatial Science
Deposited On:2021-11-30
Last Modified:2022-01-14
Downloads:3 View Download Statistics

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