Savanna grass nitrogen to phosphorus ratio estimation using field spectroscopy and the potential for estimation with imaging spectroscopy

Abstract

Determining the foliar N: P ratio provides a tool for understanding nutrient limitation on plant production and consequently for the feeding patterns of herbivores. In order to understand the nutrient limitation at landscape scale, remote sensing techniques offer that opportunity. The objective of this study is to investigate the utility of in situ hyperspectral remote sensing to estimate foliar N: P ratio. Field spectral measurements were undertaken, and grass samples were collected for foliar N and P extraction. The foliar N: P ratio prediction models were developed using partial least square regression (PLSR) with original spectra and transformed spectra. Spectral transformations included the continuum removal (CR), water removal (WR), first difference derivative (FD) and log transformation (Log(1/R)). The results showed that CR and WR spectra in combination with PLSR predicted foliar N: P ratio with higher accuracy as compared to FD and R spectra. The performance of CR and WR spectra were attributed to their ability to minimize sensor and water effects on the fresh leaf spectra, respectively. The study demonstrated a potential to predict foliar N: P ratio using field and HyMap simulated spectra and shortwave infrared (SWIR) found to be highly sensitive to foliar N: P ratio. The study recommends the prediction of foliar N: P ratio at landscape level using airborne hyperspectral data and could be used by the resource managers, park managers, farmers and ecologists to understand the feeding patterns, resource selection and distribution of herbivores (i.e. wild and livestock).