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

Yu Peng,Mei Zhang,Tao Zhou,Tingting Yang,Yongyi Lin,Yue Wang,Ziyan Xu,Yali Su,Huiting Wang

doi:10.1038/s41598-020-61294-7

Yu Peng, Mei Zhang + Show 7 more

Open Access

https://doi.org/10.1038/s41598-020-61294-7

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Abstract

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.

Highlights

Monitoring of global plant biogeochemical processes demands fast and highly accurate estimation of plant nutrition status, which is often estimated based on hyperspectral data
Since leaf Rubisco activity is highly correlated with leaf N content[6], strong correlations exist between leaf chlorophyll and N content[5,7]
We suggest that the identification of sensitive wavelengths from the entire range of spectra, rather than limited bands, would be a more precise way to estimate leaf N content

Summary

Introduction

Monitoring of global plant biogeochemical processes demands fast and highly accurate estimation of plant nutrition status, which is often estimated based on hyperspectral data Few such studies have been conducted on degraded vegetation. Extensive studies have been conducted to estimate N content through chlorophyll-based spectral indices[9,10,11,12], using techniques such as selecting sensitive wavelengths related to N, or by acquiring spectral reflectance data from multiple sensors with a variety of spatial resolutions[13]. We aimed to select several sensitive bands from the 500 available and develop a complete combination of reflectance and its first-order derivative (FD) from tree, shrub, and grass species in various degraded vegetation sites, with the objective of developing more general hyperspectral indices for the estimation of leaf P content

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