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

文献类型： 外文期刊

第一作者： Liu, Wei

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

作者机构：

关键词： canopy hyperspectral reflectance; wheat powdery mildew; nitrogen input levels; detection models

期刊名称：REMOTE SENSING （ 影响因子：4.848； 五年影响因子：5.353 ）

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年卷期： 2021 年 13 卷 18 期

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收录情况： SCI

摘要： 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 (sigma dr(680-760 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.

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