Estimation of leaf nutrition status in degraded vegetation based on field survey and hyperspectral data

Timely monitoring of global plant biogeochemical processes demands fast and highly accurate estimation of plant nutrition status, which is often estimated based on hyperspectral data. However, few such studies have been conducted on degraded vegetation. In this study, complete combinations of either original reflectance or first-order derivative spectra have been developed to quantify leaf nitrogen (N), phosphorus (P), and potassium (K) contents of tree, shrub, and grass species using hyperspectral datasets from light, moderate, and severely degraded vegetation sites in Helin County, China. Leaf N, P, and K contents were correlated to identify suitable combinations. The most effective combinations were those of reflectance difference (Dij), normalized differences (ND), first-order derivative (FD), and first-order derivative difference (FD(D)). Linear regression analysis was used to further optimize sensitive band-based combinations, which were compared with 43 frequently used empirical spectral indices. The proposed hyperspectral indices were shown to effectively quantify leaf N, P, and K content (R2 > 0.5, p < 0.05), confirming that hyperspectral data can be potentially used for fine scale monitoring of degraded vegetation.