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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/).
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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