Changes in Vertical Distribution of Spectral Reflectance within Spring Barley Canopy as an Indicator of Nitrogen Nutrition, Canopy Structure and Yield Parameters

Abstract

The main objective of this study was to evaluate the spectral reflectance in the vertical profile of spring barley canopy at the booting growth stage and to determine how the reflectance gradient changes in relation to crop density and nitrogen (N) nutrition. Vertical gradients of spectral reflectance were studied in field trials with three sowing densities (2, 4 and 6 million of germinating seeds/ha) and two levels of N nutrition (0 and 90 kg/ha). It was found that differences in vegetation indices caused by N nutrition are most pronounced in the second and third leaf from the top, and these increase with increasing sowing density. The vertical gradient of reflectance, specifically the ratio between the leaves F-3/F-1 for vegetation indices based on red-edge reflectance, represents a reliable indicator of number of ears per area unit (R = –0.87 for Normalised Red Edge-Red Index (NRERI) and –0.93 for Zarco-Teja-da and Miller Simple Ratio Index (ZM)). A close relationship to ear productivity was found almost for all observed vegetation indices and any leaf in vertical profile (R = 0.79–0.97). In contrast, the prediction of protein content in barley grain was the most reliable when the red-edge reflectance indices (ZM and NRERI) particularly from upper three leaves were used (R = 0.81–0.88). The results show that the knowledge of reflectance heterogeneity in the vertical profile of canopy can significantly contribute to the interpretation of the measured data, to the differentiation of the N nutrition effect from the response to canopy density, and finally to a more accurate estimation of yield parameters and protein content in grain.

The Effect of Controlled Tile Drainage on Growth and Grain Yield of Spring Barley as Detected by UAV Images, Yield Map and Soil Moisture Content

“…We included sampling points from the Pod Hvězdou field in models only to provide more data to increase the power of the test. The closest relationship between vegetation indices and shoot parameters, which was derived from a linear logarithmic regression, was exhibited by the Normalized Red Edge-Red Index (NRERI, [33]) based on the RE band. This vegetation index was further used to test the effect of CTD with defined RDFC on the biomass growth and grain yield of spring barley in the Za Frajmankou field.…”

“…The commonly used vegetation index associated with R and NIR wavebands is Normalized Difference Vegetation Index (NDVI, [29]) mostly recommended in monitoring green vegetation cover. However, when the vegetation cover becomes dense, i.e., when the leaf area index (LAI) is higher than three, NDVI tends to be saturated, leading to underestimated biomass yield predictions [30][31][32][33]. Saturation effect can be reduced by using vegetation indices based on reflectance in a narrow band of the red-edge region (RE, 700-750 nm, e.g., REIP, NDRE, NRERI and RENDVI), showing sensitive increase corresponding strongly to vegetation chlorophyll content and the plant N uptake [34][35][36].…”

Identification of High Nitrogen Use Efficiency Phenotype in Rice (Oryza sativa L.) Through Entire Growth Duration by Unmanned Aerial Vehicle Multispectral Imagery

“…As an instrument that uses optical methods for the estimation of N levels in leaves, the N-pen meter [Photon Systems Instruments (PSI), spol.s.r.o, Czechia] was used to measure the reflectance of the 51 rice varieties to obtain the Normalized Difference Greenness Index (NDGI). The nitrogen content of each rice variety was measured as reported in maize, wheat, and barley, and the nitrogen content was calculated from a correlation with the NDGI ( Klem, 2008 ). Since the N-pen meter measures the N levels non-destructively, measurements from the same leaf could be taken multiple times, and the optical data could be collected before the leaf was removed for the Elementary Quantitative Analysis (EQA) analysis.…”

Simulation and Verification of Vertical Heterogeneity Spectral Response of Winter Wheat Based on the mSCOPE Model

“…Thus, combining empirical methods and models is necessary. Klem et al [29] emphasized the influence of canopy vertical heterogeneity on spectral reflectivity by calculating the spectral index of spring wheat in different vertical parts. The mSCOPE model proposed by Yang et al can be used as an effective tool to study the influence of vertical heterogeneity on canopy reflectance.…”