Abstract
AbstractTimely 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.
Funder
State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University the National Key Research and Development Program of China Minzu University of China
Publisher
Springer Science and Business Media LLC
Cited by
37 articles.
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