A hybrid model to predict nitrogen concentration in heterogeneous grassland using field spectroscopy

Field spectroscopy is a rapid and non-destructive tool used for the estimation of nitrogen concentration (N%) of vegetation. Empirical and physically-based models are widely used for retrieving N%. However, model transferability to different times and locations, and feature redundancy remain the two key challenges of field spectroscopy analysis. Here we addressed these problems by developing a hybrid method (i.e., a combination of physically-based (PROSAIL) and empirical models) to retrieve N% in grasslands. We used a large spectral dataset with >6000 samples collected over 8 years (2009–2016) for grassland farms across New Zealand. The hybrid model combines the features derived from PROSAIL inversion and an empirical model and develops a predictive model using a Gaussian Process Regression (GPR) algorithm. The model performance is tested on spatially and temporally independent data and compared with PROSAIL and empirical models. The hybrid model achieves a higher performance with an RMSE (%N), R2 and Mean Prediction Interval Width (MPIW) of 0.27, 0.78 and 0.26 as compared to empirical (RMSE = 0.28, R2 = 0.77 and MPIW = 0.32) and physically-based models (RMSE = 0.33, R2 = 0.65 and MPIW = 0.56). In addition, the hybrid model significantly outperforms the physically-based and empirical models during autumn (RMSE = 0.32, R2 = 0.78 and MPIW = 0.11) and summer (RMSE = 0.27, R2 = 0.80 and MPIW = 0.16) seasons.