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基于冠层高光谱遥感的杂交水稻植被指数氮素营养诊断模型
引用本文:王晓珂,刘婷婷,许桂玲,冯跃华,彭金凤,李杰,罗强鑫,韩志丽,卢苇,PHONENASAY Somsana. 基于冠层高光谱遥感的杂交水稻植被指数氮素营养诊断模型[J]. 中国稻米, 2021, 27(3): 21-29. DOI: 10.3969/j.issn.1006-8082.2021.03.005
作者姓名:王晓珂  刘婷婷  许桂玲  冯跃华  彭金凤  李杰  罗强鑫  韩志丽  卢苇  PHONENASAY Somsana
作者单位:1.贵州大学农学院,贵阳 550025;2.黔西南州农业农村局,贵州 兴义 562400;3.贵州大学/山地植物资源保护与种质创新教育部重点实验室,贵阳550025
基金项目:国家自然科学基金(31360311;31160263);公益性行业(农业)科研专项子项目(201503118-03);贵州省农业科技攻关项目(黔科合支撑[2019]2303号;黔科合支撑[2016]2563号;黔科合NY[2013]3005号;黔科合NY[2011]3085号);贵州省特色粮油作物栽培与生理生态研究科技创新人才团队(黔科合平台人才[2019]5613号);贵州省高层次创新型人才项目(黔科合平台人才[2018]5632);贵州省普通高等学校粮油作物遗传改良与生理生态特色重点实验室项目(黔教合KY字[2015]333);贵州省生物学一流学科建设项目(GNYL[2017]009)
摘    要:以杂交水稻为研究对象,进行两因素裂区试验,主区为品种,副区为施氮水平,分析了4个植被指数(VIs)分别与叶片氮素含量(LNC)、叶片氮素积累量(LNA)和地上部氮素积累量(APNA)之间的相关性,并建立了以VIs为自变量的氮素营养诊断模型.结果 表明,4个VIs和LNC、LNA之间均存在决定系数大于0.7的波段区域且波...

关 键 词:杂交水稻  氮素营养  高光谱遥感  植被指数  诊断模型
收稿时间:2021-02-22

Nitrogen Diagnosis Model of Vegetation Indices Based on Canopy Hyperspectral Remote Sensing for Hybrid Rice
Xiaoke WANG,Tingting LIU,Guiling XU,Yuehua FENG,Jinfeng PENG,Jie LI,Qiangxin LUO,Zhili HAN,Wei LU,Somsana PHONENASAY. Nitrogen Diagnosis Model of Vegetation Indices Based on Canopy Hyperspectral Remote Sensing for Hybrid Rice[J]. China Rice, 2021, 27(3): 21-29. DOI: 10.3969/j.issn.1006-8082.2021.03.005
Authors:Xiaoke WANG  Tingting LIU  Guiling XU  Yuehua FENG  Jinfeng PENG  Jie LI  Qiangxin LUO  Zhili HAN  Wei LU  Somsana PHONENASAY
Affiliation:1.College of Agronomy, Guizhou University, Guiyang 550025, China;2.Qianxinanzhou Agriculture and Rural Affarirs Bureau, Xingyi, Guizhou 562400, China;3.Laboratory of Plant Resource Conservation and Germplasm Innovation in Mountainous Region of (Ministry of Education), Guiyang 550025, China
Abstract:A split plot experiment of hybrid rice with two factors was carried out. There were two cultivars in the main plot and five nitrogen application levels in the sub plot. The correlation between four vegetation indices (VIs) and leaf nitrogen content(LNC), leaf nitrogen accumulation(LNA) and aerial part nitrogen accumulation (APNA) were analyzed respectively, and nitrogen diagnosis models with VIs as the independent variable were established. The results showed that there were band regions with coefficient of determination (r2) greater than 0.7 between the four VIs and LNC and LNA, respectively, and the band regions are consistent. The r2 between the four VIs and APNA were low, only about 0.2; the maximum r2 between ratio vegetation index (RVI) and LNC was 0.886, and the corresponding band combination is 694 nm and 763 nm. The maximum r2 between perpendicular vegetation index(PVI) and LNC is 0.869 and the corresponding band combination was 864 nm and 483 nm, the maximum r2 between difference vegetation index (DVI) and LNC was 0.883 and the corresponding band combination was 1 292 nm and 1 258 nm, the maximum r2 of normalized vegetation index (NDVI) and LNC was 0.881 and the corresponding band combination was 1 296 nm and 1 220 nm. The optimal nitrogen diagnosis model was LNC diagnosis model, and the model expression was LNC = 1E+03NDVI2-132.55NDVI+3.72; the correlation index(R2), RMSE and RE of training set were 0.879, 0.357% and 16.267%, respectively; the R2, RMSE and RE of test set were 0.895, 0.331% and 15.136%, respectively.
Keywords:hybrid rice  nitrogen nutrition  hyperspectral remote sensing  vegetation indices  diagnosis model  
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