Evaluation of hyperspectral LiDAR for monitoring rice leaf nitrogen by comparison with multispectral LiDAR and passive spectrometer

Jia Sun,Shalei Song,Zhenbing Zhang,Jian Yang,Shuo Shi,Biwu Chen,Wei Gong,Lin Du

doi:10.1038/srep40362

Abstract

Fast and nondestructive assessment of leaf nitrogen concentration (LNC) is critical for crop growth diagnosis and nitrogen management guidance. In the last decade, multispectral LiDAR (MSL) systems have promoted developments in the earth and ecological sciences with the additional spectral information. With more wavelengths than MSL, the hyperspectral LiDAR (HSL) system provides greater possibilities for remote sensing crop physiological conditions. This study compared the performance of ASD FieldSpec Pro FR, MSL, and HSL for estimating rice (Oryza sativa) LNC. Spectral reflectance and biochemical composition were determined in rice leaves of different cultivars (Yongyou 4949 and Yangliangyou 6) throughout two growing seasons (2014–2015). Results demonstrated that HSL provided the best indicator for predicting rice LNC, yielding a coefficient of determination (R2) of 0.74 and a root mean square error of 2.80 mg/g with a support vector machine, similar to the performance of ASD (R2 = 0.73). Estimation of rice LNC could be significantly improved with the finer spectral resolution of HSL compared with MSL (R2 = 0.56).

Highlights

Multispectral and hyperspectral remote sensing are nondestructive methods of estimating the foliar biochemical concentration of vegetation[4,5]
This study focused mainly on the novel use of the light detection and ranging (LiDAR) intensities of hyperspectral LiDAR (HSL) and multispectral LiDAR (MSL) to reflect foliar biochemistry
The spectral reflectance of rice leaves was influenced strongly by the foliar chlorophyll concentration, which shares a close relationship to foliar N levels[2,17,31]

Summary

Introduction

Multispectral and hyperspectral remote sensing are nondestructive methods of estimating the foliar biochemical concentration of vegetation[4,5]. This method has been used to monitor the chlorophyll, lignin, N, and water status of vegetation[3,6,7]. LiDAR intensity is useful in retrieving plant chlorophyll content[12], nitrogen status[13], and leaf water content[14]. A few promising commercial multispectral laser scanners have been developed[18,19,20] Their wavelength number is limited to 2–3. Advantages of SVM include robustness, insensitivity to the number of dimensions, and small sample size requirement for training[26]

Objectives

Methods

Results

Conclusion