Analysis of Different Hyperspectral Variables for Diagnosing Leaf Nitrogen Accumulation in Wheat.

Changwei Tan,Jian Zhou,Wenshan Guo,Dunliang Wang,Ying Du,Ming Luo,Yongjian Zhang

doi:10.3389/fpls.2018.00674

Abstract

Hyperspectral remote sensing is a rapid non-destructive method for diagnosing nitrogen status in wheat crops. In this study, a quantitative correlation was associated with following parameters: leaf nitrogen accumulation (LNA), raw hyperspectral reflectance, first-order differential hyperspectra, and hyperspectral characteristics of wheat. In this study, integrated linear regression of LNA was obtained with raw hyperspectral reflectance (measurement wavelength = 790.4 nm). Furthermore, an exponential regression of LNA was obtained with first-order differential hyperspectra (measurement wavelength = 831.7 nm). Coefficients (R2) were 0.813 and 0.847; root mean squared errors (RMSE) were 2.02 g·m−2 and 1.72 g·m−2; and relative errors (RE) were 25.97% and 20.85%, respectively. Both the techniques were considered as optimal in the diagnoses of wheat LNA. Nevertheless, the better one was the new normalized variable (SDr − SDb)/(SDr + SDb), which was based on vegetation indices of R2 = 0.935, RMSE = 0.98, and RE = 11.25%. In addition, (SDr − SDb)/(SDr + SDb) was reliable in the application of a different cultivar or even wheat grown elsewhere. This indicated a superior fit and better performance for (SDr − SDb)/(SDr + SDb). For diagnosing LNA in wheat, the newly normalized variable (SDr − SDb)/(SDr + SDb) was more effective than the previously reported data of raw hyperspectral reflectance, first-order differential hyperspectra, and red-edge parameters.

Highlights

Nitrogen fertilizers are currently used to produce crops of high yield and good quality
leaf nitrogen accumulation (LNA), LNC, and unit leaf dry weight was more effectively reflected by nitrogen status of wheat
We proposed a normalized variable NREAI, which was based on vegetation indices

Summary

INTRODUCTION

Nitrogen fertilizers are currently used to produce crops of high yield and good quality. Nitrogen status of crops was determined by establishing a hyperspectral diagnostic model for the nitrogen crop accumulation This information was significant for effective nitrogen fertilization (Jain et al, 2007; Mahajan et al, 2014; Morier et al, 2015). This implies that it was possible to calculate vegetation chlorophyll concentration with red-edge parameters of remote sensing This indicates that the reflectivity of visible light increased due to nitrogen deficiency in plants; reflectance was different in different plants. Shibayama et al (1993) conducted a study on wheat crops and found a good regression relationship between LNA per unit area and a linear combination of hyperspectral reflectance at 620 and 760 nm. Researchers investigated the relationship between wheat LNA and various hyperspectral remote sensing parameters. The expected results were used to provide a technical approach to nondestructive monitoring and diagnosis of nitrogen status in crops

MATERIALS AND METHODS

Findings

DISCUSSION