Paper Info
Title
Retrieval Of Hyperspectral Information From Multispectral Data For Perennial Ryegrass Biomass Estimation
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) approximate to 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.
Year
DOI
Venue
2020
10.3390/s20247192
SENSORS
Keywords
DocType
Volume
vegetation indices, spectral resampling, continuum-removal, parametric-regression, spectral simulation, machine learning, random-forest
Journal
20
Issue
ISSN
Citations 
24
1424-8220
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Gustavo Togeiro de Alckmin100.34
Lammert Kooistra221232.25
Richard Rawnsley300.34
Sytze de Bruin48112.35
Arko Lucieer545546.51