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.

Kai Zhou,Tao Cheng,Hengbiao Zheng,Yan Zhu,Susan L Ustin,Xia Yao,Weixing Cao,Yongchao Tian

doi:10.3389/fpls.2018.00964

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 background pixels at each spatial resolution were used to predict LNC with VIs, GPR and PLSR, respectively. The results demonstrated all-leaf pixels generally exhibited more stable performance than sunlit- and shaded-leaf pixels regardless of estimation approaches. The predictions of LNC required stage-specific LNC~VI models for each vegetative stage but could be performed with a single model for all the reproductive stages. Specifically, most VIs achieved stable performances from all the resolutions finer than 14 mm for the early tillering stage but from all the resolutions finer than 56 mm for the other stages. In contrast, the global models for the prediction of LNC across the entire growing season were successfully established with the approaches of GPR or PLSR. In particular, GPR generally exhibited the best prediction of LNC with the optimal spatial resolution being found at 28 mm. These findings represent significant advances in the application of ground-based imaging spectroscopy as a promising approach to crop monitoring and understanding the effects of spatial resolution on the estimation of rice LNC.

Highlights

Nitrogen (N) is the key nutrient parameter determining the photosynthetic functioning and productivity in crops (Inoue et al, 2012)
We focused on the leaf N concentration (LNC) estimation in paddy rice canopies throughout the entire growing season using imaging spectroscopy data with various spatial resolutions ranging from millimeter- to centimeter-resolution
Discrimination of Non-vegetation Background and Vegetation To investigate the relationships between LNC and vegetation indices (VIs) derived from pure leaf pixels across the whole image at 1.3 mm spatial resolutions, we firstly identified vegetation pixels applying a threshold of the enhanced vegetation index (EVI) (Pinto et al, 2016; Zhou et al, 2017) (EVI > 0.45)

Summary

Introduction

Nitrogen (N) is the key nutrient parameter determining the photosynthetic functioning and productivity in crops (Inoue et al, 2012). A series of studies applied multivariate regression (e.g., stepwise multiple linear regression) in the selection of optimal bands or variables for detecting rice N status (e.g., Tang et al, 2007; Yu et al, 2013) (Table 1) These methods usually produce high predictive accuracy but sometimes at the cost of over-fitting and intensive computation, especially when excessive variables were selected (Yu et al, 2013). Other studies use vegetation indices (VIs) that employ two or three bands in the visible, rededge, near-infrared (NIR), or shortwave infrared regions to assess rice N status (e.g., Xue et al, 2004; Wang et al, 2012; Tian et al, 2014) These VI-based approaches are easier to operate as compared to those multivariate regressions and can produce higher accuracies and better robustness in assessing N status

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Conclusion