Non-Destructive Monitoring of Maize Nitrogen Concentration Using a Hyperspectral LiDAR: An Evaluation from Leaf-Level to Plant-Level

Kaiyi Bi,Jie Bai,Zheng Niu,Ji Wang,Gang Sun,Zeying Han,Shuai Gao,Shunfu Xiao

doi:10.3390/rs13245025

Abstract

Advanced remote sensing techniques for estimating crop nitrogen (N) are crucial for optimizing N fertilizer management. Hyperspectral LiDAR (HSL) data, with both spectral and spatial information of the targets, can extract more plant properties than traditional LiDAR and hyperspectral imaging systems. In this study, we tested the ability of HSL in terms of estimating maize N concentration at the leaf-level by using spectral indices and partial least squares regression (PLSR) methods. Subsequently, the N estimation was scaled up to the plant-level based on HSL point clouds. Biomass, extracted with structural proxies, was utilized to exhibit its supplemental effect on N concentration. The results show that HSL has the ability to extract N concentrations at both the leaf-level and the canopy-level, and PLSR showed better performance (R2 > 0.6) than the single spectral index (R2 > 0.4). In comparison to the stem height and maximum canopy width, the plant height had the strongest ability (R2 = 0.88) to estimate biomass. Future research should utilize larger datasets to test the viability of using HSL to monitor the N concentration of crops, which is beneficial for precision agriculture.

Highlights

Nitrogen (N) is a crucial element in agriculture, since N significantly affects the physiological processes of plants and, affects their growth and yield [1,2]
The distribution of N within a plant canopy is spatially heterogeneous, since plant canopies are three-dimensional (3D) structures [6,7]. This vertical distribution pattern matches the available radiation along the plant height to achieve the maximum canopy photosynthesis [8], since about 75% of leaf nitrogen is involved in the plant photosynthesis process [2], and previous studies have shown that shaded leaves in the canopy bottom have lower N than upper leaves that are exposed to sunlight [9,10]
Using a prototype Hyperspectral Light detection and ranging (LiDAR) (HSL) device developed by our research community [49], this study estimated the maize N concentration from the 2D leaf level to the 3D plant level

Summary

Introduction

Nitrogen (N) is a crucial element in agriculture, since N significantly affects the physiological processes of plants and, affects their growth and yield [1,2]. The distribution of N within a plant canopy is spatially heterogeneous, since plant canopies are three-dimensional (3D) structures [6,7] This vertical distribution pattern matches the available radiation along the plant height to achieve the maximum canopy photosynthesis [8], since about 75% of leaf nitrogen is involved in the plant photosynthesis process [2], and previous studies have shown that shaded leaves in the canopy bottom have lower N than upper leaves that are exposed to sunlight [9,10]. The N distribution is a dynamic process (instead of a static one), which is related to the growth stages and environmental conditions [1].

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Conclusion