Monitoring of Wheat Powdery Mildew under Different Nitrogen Input Levels Using Hyperspectral Remote Sensing

Wei Liu,Yilin Zhou,Yuli Song,Xiangming Xu,Jieru Fan,Yanan Zhao,Fei Xu,Chaofei Sun

doi:10.3390/rs13183753

Abstract

Both wheat powdery mildew severities and nitrogen input levels can lead to changes in spectral reflectance, but they have been rarely studied simultaneously for their effect on spectral reflectance. To determine the effects and influences of different nitrogen input levels on monitoring wheat powdery mildew and estimating yield by near-ground hyperspectral remote sensing, Canopy hyperspectral reflectance data acquired at Feekes growth stage (GS) 10.5.3, 10.5.4, and 11.1 were used to monitor wheat powdery mildew and estimate grain yield under different nitrogen input levels during the 2016–2017, 2017–2018, 2018–2019 and 2019–2020 seasons. The relationships of powdery mildew and grain yield with vegetation indices (VIs) derived from spectral reflectance data across the visible (VIS) and near-infrared (NIR) regions of the spectrum were studied. The relationships of canopy spectral reflectance or first derivative spectral reflectance with powdery mildew did not differ under different nitrogen input levels. However, the dynamics of VIs differed in their sensitivities to nitrogen input levels, disease severity, grain yield, The area of the red edge peak (Σdr680–760 nm) was a better overall predictor for both disease severity and grain yield through linear regression models. The slope parameter estimates did not differ between the two nitrogen input levels at each GSs. Hyperspectral indices can be used to monitor wheat powdery mildew and estimate grain yield under different nitrogen input levels, but such models are dependent on GS and year, further research is needed to consider how to incorporate the growth stage and year-to-year variation into future applications.

Highlights

With the rapid development of remote sensing technology, hyperspectral remote sensing has recently become an important means of surface vegetation research
Previous studies investigated using hyperspectral reflectance to detect wheat powdery mildew of several cultivars or at different planting densities [17,18]. These studies showed that the area of the red edge peak (Σdr680–760 nm ), red edge slope, differential vegetation index (DVI), soil adjusted vegetation index (SAVI), triangular vegetation index (TVI), and several other spectral parameters were highly correlated with powdery mildew severity, of which Σdr680–760 nm was the best
Correlation between the spectral reflectance in the visible red wavelengths (650–680 nm) and Disease index (DI) differed in its magnitude among seasons; for the low nitrogen input treatment, the correlation was only significant at GS10.5.3 in 2017, and GS11.1 in 2019 and 2020

Summary

Introduction

With the rapid development of remote sensing technology, hyperspectral remote sensing has recently become an important means of surface vegetation research. Most of the hyperspectral reflectance of healthy and mildewed wheat leaves in a laboratory was related to powdery mildew development [14]. Previous studies investigated using hyperspectral reflectance to detect wheat powdery mildew of several cultivars or at different planting densities [17,18]. These studies showed that the area of the red edge peak (Σdr680760 nm ), red edge slope (drred ), differential vegetation index (DVI), soil adjusted vegetation index (SAVI), triangular vegetation index (TVI), and several other spectral parameters were highly correlated with powdery mildew severity, of which Σdr680760 nm was the best. The intercepts often differed among varieties or planting densities, but the slopes did not

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Discussion

Conclusion