Assessing the Impact of Spatial Resolution on the Estimation of Leaf Nitrogen Concentration Over the Full Season of Paddy Rice Using Near-Surface Imaging Spectroscopy Data

Frontiers in Plant Science
Kai ZhouYong-Chao Tian

Abstract

Timely monitoring nitrogen status of rice crops with remote sensing can help us optimize nitrogen fertilizer management and reduce environmental pollution. Recently, the use of near-surface imaging spectroscopy is emerging as a promising technology that can collect hyperspectral images with spatial resolutions ranging from millimeters to decimeters. The spatial resolution is crucial for the efficiency in the image sampling across rice plants and the separation of leaf signals from the background. However, the optimal spatial resolution of such images for monitoring the leaf nitrogen concentration (LNC) in rice crops remains unclear. To assess the impact of spatial resolution on the estimation of rice LNC, we collected ground-based hyperspectral images throughout the entire growing season over 2 consecutive years and generated ten sets of images with spatial resolutions ranging from 1.3 to 450 mm. These images were used to determine the sensitivity of LNC prediction to spatial resolution with three groups of vegetation indices (VIs) and two multivariate methods Gaussian Process regression (GPR) and Partial least squares regression (PLSR). The reflectance spectra of sunlit-, shaded-, and all-leaf leaf pixels separated from backgr...Continue Reading

References

Sep 23, 2006·Journal of Experimental Botany·George Alan Blackburn
Dec 20, 2008·Pakistan Journal of Biological Sciences : PJBS·Yan-Lin TangRen-Chao Wang
Aug 13, 2011·Environmental Science & Technology·Sujay S KaushalCarol Kendall
Oct 22, 2013·Trends in Plant Science·José Luis Araus, Jill E Cairns

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Software Mentioned

TCARI
GPR
specVIEW
ENVI
PLSR
OSAVI

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