Estimating Rice Leaf Nitrogen Concentration: Influence of Regression Algorithms Based on Passive and Active Leaf Reflectance

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

doi:10.3390/rs9090951

Abstract

Nitrogen (N) is important for the growth of crops. Estimating leaf nitrogen concentration (LNC) accurately and nondestructively is important for precision agriculture, reduces environmental pollution, and helps model global carbon and N cycles. Leaf reflectance, especially in the visible and near-infrared regions, has been identified as a useful indicator of LNC. Except reflectance passively acquired by spectrometers, the newly developed multispectral LiDAR and hyperspectral LiDAR provide possibilities for measuring leaf spectra actively. The regression relationship between leaf reflectance spectra and rice (Oryza sativa) LNC relies greatly on the algorithm adopted. It would be preferable to find one algorithm that performs well with respect to passive and active leaf spectra. Thus, this study assesses the influence of six popular linear and nonlinear methods on rice LNC retrieval, namely, partial least-square regression, least squares boosting, bagging, random forest, back-propagation neural network (BPNN), and support vector regression of different types/kernels/parameter values. The R2, root mean square error and relative error in rice LNC estimation using these different methods were compared through the passive and active spectral measurements of rice leaves of different varieties at different locations and time (Yongyou 4949, Suizhou, 2014, Yangliangyou 6, Wuhan, 2015). Results demonstrate that BPNN provided generally satisfactory performance in estimating rice LNC using the three kinds of passive and active reflectance spectra.

Highlights

Plants require nitrogen (N) nutrition for many important compounds, such as amino and nucleic acids [1,2]
The results showed that for every investigated algorithm except for least squares boosting (LSBoost) and supportvector vector regression (SVR), the first three principal components (PCs) showed a stronger correlation with foliar N than the hyperspectral data
This study investigated the performance of six popular linear and nonlinear regression algorithms to predict paddy rice leaf nitrogen concentration (LNC) using the spectra from three different passive and active sensors: ASD, multispectral LiDAR (MSL), and hyperspectral LiDAR (HSL)

Summary

Introduction

Plants require nitrogen (N) nutrition for many important compounds, such as amino and nucleic acids [1,2]. Plant growth is linearly dependent on N supply [3]. Higher rates of fertilization do not necessarily improve yield and can cause serious water pollution [4]. Leaf nitrogen concentration (LNC) is associated with the photosynthetic capacity of leaves and allows global-scale CO2 assimilation to be modelled based on specific leaf area and LNC [5,6]. There is a need for the N condition of plants to be determined efficiently and accurately for fertilization guidance and for understanding carbon and N cycles.

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Methods

Results

Conclusion