Quantification of Grassland Biomass and Nitrogen Content through UAV Hyperspectral Imagery—Active Sample Selection for Model Transfer

• Published in: Drones (Basel) Vol. 6; no. 3; p. 73
• Main Authors: Franceschini, Marston, Becker, Rolf, Wichern, Florian, Kooistra, Lammert
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2022
• Subjects: Active learning; Agricultural production; Biomass; Cost analysis; Datasets; Empirical analysis; Fertilizers; Grasslands; hyperspectral imagery; Hyperspectral imaging; Least squares method; Monitoring; Multivariate analysis; multivariate linear model; Nitrogen; nitrogen management; pasture growth monitoring; Reduction; Spectra; Spectral sensitivity; Training; Uncertainty; uncertainty analysis; Unmanned aerial vehicles; Vegetation; Vis-NIR spectroscopy; Germany
• ISSN: 2504-446X; 2504-446X
• DOI: 10.3390/drones6030073

Accurate retrieval of grassland traits is important to support management of pasture production and phenotyping studies. In general, conventional methods used to measure forage yield and quality rely on costly destructive sampling and laboratory analysis, which is often not viable in practical applications. Optical imaging systems carried as payload in Unmanned Aerial Vehicles (UAVs) platforms have increasingly been proposed as alternative non-destructive solutions for crop characterization and monitoring. The vegetation spectral response in the visible and near-infrared wavelengths provides information on many aspects of its composition and structure. Combining spectral measurements and multivariate modelling approaches it is possible to represent the often complex relationship between canopy reflectance and specific plant traits. However, empirical models are limited and strictly represent characteristics of the observations used during model training, therefore having low generalization potential. A method to mitigate this issue consists of adding informative samples from the target domain (i.e., new observations) to the training dataset. This approach searches for a compromise between representing the variability in new data and selecting only a minimal number of additional samples for calibration transfer. In this study, a method to actively choose new training samples based on their spectral diversity and prediction uncertainty was implemented and tested using a multi-annual dataset. Accurate predictions were obtained using hyperspectral imagery and linear multivariate models (Partial Least Squares Regression—PLSR) for grassland dry matter (DM; R2 = 0.92, RMSE = 3.25 dt ha−1), nitrogen (N) content in % of DM (R2 = 0.58, RMSE = 0.27%) and N-uptake (R2 = 0.91, RMSE = 6.50 kg ha−1). In addition, the number of samples from the target dates added to the training dataset could be reduced by up to 77% and 74% for DM and N-related traits, respectively, after model transfer. Despite this reduction, RMSE values for optimal transfer sets (identified after validation and used as benchmark) were only 20–30% lower than those values obtained after model transfer based on prediction uncertainty reduction, indicating that loss of accuracy was relatively small. These results demonstrate that considerably simple approaches based on UAV hyperspectral data can be applied in preliminary grassland monitoring frameworks, even with limited datasets.