Using Different Regression Methods to Estimate Leaf Nitrogen Content in Rice by Fusing Hyperspectral LiDAR Data and Laser-Induced Chlorophyll Fluorescence Data

Abstract

Abstract:Nitrogen is an essential nutrient element in crop photosynthesis and yield improvement. Thus, it is urgent and important to accurately estimate the leaf nitrogen contents (LNC) of crops for precision nitrogen management. Based on the correlation between LNC and reflectance spectra, the hyperspectral LiDAR (HSL) system can determine three-dimensional structural parameters and biochemical changes of crops. Thereby, HSL technology has been widely used to monitor the LNC of crops at leaf and canopy levels. In addition, the laser-induced fluorescence (LIF) of chlorophyll, related to the histological structure and physiological conditions of green plants, can also be utilized to detect nutrient stress in crops. In this study, four regression algorithms, support vector machines (SVMs), partial least squares (PLS) and two artificial neural networks (ANNs), back propagation NNs (BP-NNs) and radial basic function NNs (RBF-NNs), were selected to estimate rice LNC in booting and heading stages based on reflectance and LIF spectra. These four regression algorithms were used for 36 input variables, including the reflectance spectral variables on 32 wavelengths and four peaks of the LIF spectra. A feature weight algorithm was proposed to select different band combinations for the LNC retrieval models. The determination coefficient (R 2 ) and the root mean square error (RMSE) of the retrieval models were utilized to compare their abilities of estimating the rice LNC. The experimental results demonstrate that (I) these four regression methods are useful for estimating rice LNC in the order of RBF-NNs > SVMs > BP-NNs > PLS; (II) The LIF data in two forms, including peaks and indices, display potential in rice LNC retrieval, especially when using the PLS regression (PLSR) model for the relationship of rice LNC with spectral variables. The feature weighting algorithm is an effective and necessary method to determine appropriate band combinations for rice LNC estimation.

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

“…Although these advanced ANNs have been primarily used for classification applications, only recently they were explored to map vegetation properties from spectroscopic data (Chen et al 2015;Feng et al 2016;Pôças et al 2017;Wang et al 2013). Some of these studies mention the superiority of these advanced ANN designs as compared to standard ANN designs or other machine learning approaches in estimating vegetation properties (Du et al 2016;Li et al 2017;Pham et al 2017). Applying ANNs to spectroscopic data, nonetheless, can be quite challenging due to the multicollinearity.…”

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

“…With regard to pattern recognition algorithms, multiple linear regression methods such as partial least-squares regression21 have been widely applied to estimate vegetation biochemical parameters2223. However, the exact relationship between spectral reflectance and LNC may not be linear.…”

Novel Combined Spectral Indices Derived from Hyperspectral and Laser-Induced Fluorescence LiDAR Spectra for Leaf Nitrogen Contents Estimation of Rice

“…The rice varieties were Yongyou 4949 (2014) and Yangliangyou 6 (2015). Different levels of urea fertilizer were used in the experimental fields which could be found in the study of Shi et al [37] in detail. Three fertilization repetitions were performed for each cultivation condition in 2014 and 2015.…”