Study on Estimating Total Nitrogen Content in Sugar Beet Leaves Under Drip Irrigation Based on Vis-NIR Hyperspectral Data and Chlorophyll Content
文献类型： 外文期刊
作者： Li, Zong-fei 1 ; Chen, Bing 2 ; Fan, Hua 1 ; Fei, Cong 1 ; Su, Ji-xia 1 ; Li, Yang-yang 1 ; Liu, Ning-ning 1 ; Zhou, Hong-liang 1 ; Zhang, Li-juan 1 ; Wang, Kai-yong 1 ;
作者机构： 1.Shihezi Univ, Agron Coll, Xinjiang, Peoples R China
2.Xinjiang Acad Agr & Reclamat Sci, Cotton Inst, Xinjiang, Peoples R China
期刊名称：SPECTROSCOPY （ 影响因子：0.5； 五年影响因子：0.8 ）
ISSN： 0887-6703
年卷期： 2023 年 38 卷 4 期
页码：
收录情况： SCI
摘要： The relationship between the leaf nitrogen content (LNC) and hyperspectral remote sensing imagery (HYP) was determined to construct an estimation model of the LNC of drip-irrigated sugar beets, aiming to provide supports for the in-time monitoring of sugar beet growth and nitrogen management in arid areas. In this study, a field hyperspectrometer was used to collect the leaf reflectance at the 350-2500 nm for each treatment on the 65th, 85th, 104th, 124th, and 140th day after emergence, and the LNC and leaf chlorophyll content (CHL) of sugar beets were also determined. The spectral characteristic parameters were selected to construct the vegetation indices. The LNC estimation model using HYP as the independent variable (HYP-LNC), and that using CHL and HYP as the independent variables (HYP-CHL-LNC), were compared. The results shows that the HYP-CHL-LNC models had a better linear relationship and a higher fitting accuracy than the HYP-LNC models.
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