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.
Highlights
Precision agriculture (PA) and Remote Sensing (RS) employ spectral observations to estimate biophysical and biochemical properties of vegetation canopies [1]
Spectral Data: The inspection of the principal component analysis (PCA) (Figure 5b) indicates that 98% of the MS variance could be decomposed in two principal components
Our results have shown that a satisfactory reconstruction of the hyperspectral signature of a vegetated canopy can be retrieved from multispectral measurements compatible with a commercially available sensor (Figure 6a)
Summary
Precision agriculture (PA) and Remote Sensing (RS) employ spectral observations to estimate biophysical and biochemical properties of vegetation canopies [1]. With the recent popularization of Unmanned Aerial Systems (UASs) for PA, farmers can employ Multispectral (MS) cameras for spectral data acquisition, allowing for a high degree of flexibility in terms of data collection interval and custom-designed sensing systems [2]. Such systems can potentially be employed in precision pasture management as a monitoring tool [3], mapping key attributes such as biomass [4]. In essence, summarizes a fraction of the spectral variability while tracking changes in the canopy [9] without necessarily resorting to the complete spectral information In this sense, VIs are a valuable trade-off between a small number of bands and the full information available in a spectral signature [10]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
