| 冬小麦叶面积指数的品种差异性与高光谱估算研究 |
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| 引用本文： | 郭建彪,马新明,时 雷,张娟娟,杜 盼,魏钦钦.冬小麦叶面积指数的品种差异性与高光谱估算研究[J].麦类作物学报,2018(3):340-347. |
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| 作者姓名： | 郭建彪 马新明 时 雷 张娟娟 杜 盼 魏钦钦 |
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| 作者单位： | （1.河南粮食作物协同创新中心，河南郑州 450002；2.河南农业大学农学院，河南郑州 450002；3.河南农业大学信息与管理科学学院，河南郑州 450002） |
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| 基金项目： | 国家重点研发计划项目（2016YFD0300609）；河南省现代农业产业技术体系项目（S2010-001-G04）； 河南省重点科技攻关项目（152102110056） |
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| 摘 要： | 为给小麦叶面积指数（LAI）的高光谱估算提供技术支持，基于2年大田试验，以4个河南主推品种为材料，对小麦LAI和冠层光谱变化特点、估算模型及其品种间的差异等进行了系统分析。结果表明，在生育期内不同冬小麦品种冠层光谱反射率的变化与LAI变化有差异；在相同LAI下，不同冬小麦品种的光谱曲线存在差异。利用400~900 nm范围内冠层光谱反射率的任意两波段组合的比值光谱指数（RSI）、归一化差值光谱指数（NDSI）和差值光谱指数（DSI）所建立的单品种模型以及不同品种综合模型的决定系数（r）均达到0.84以上，单品种模型的r和调整r分别较综合模型高出3.1%~4.8%和2.0%~4.2%。利用独立于建模样本以外的数据对上述模型进行检验，单品种模型预测的r较综合模型提高了0.6%~11.0%，而均方根误差降低了10.0%~37.0%。因此，在利用高光谱遥感技术估算冬小麦LAI时，可以通过建立单品种模型来提高估算精度。
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| 关 键 词： | 冬小麦 LAI 品种差异 高光谱 估算模型 |
Variety Variation and Hyperspectral Estimate Model of Leaf Area Index of Winter Wheat |
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| GUO Jianbiao,MA Xinming,SHI Lei,ZHANG Juanjuan,DU Pan,WEI Qinqin.Variety Variation and Hyperspectral Estimate Model of Leaf Area Index of Winter Wheat[J].Journal of Triticeae Crops,2018(3):340-347. |
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| Authors: | GUO Jianbiao MA Xinming SHI Lei ZHANG Juanjuan DU Pan WEI Qinqin |
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| Abstract: | In order to provide technology basis for hyperspectral estimate of leaf area index(LAI), the differences of LAI, canopy spectral, hyperspectral sensitive bands, estimation models of different varieties were systematically analyzed in this study, based on a two-year field experiment with four wheat cultivars of Henan. The results showed that, the variation of canopy spectral reflectance in different winter wheat cultivars varied from the difference of LAI during growth period. However, with the same LAI, the spectral curves of different winter wheat cultivars were different. The single variety model and integrated model of different winter wheat varieties were established by systematically analyzing the ratio vegetation index(RSI), normalized vegetation index(NDSI) and difference vegetation index(DSI) of canopy spectral reflectance with all combinations of two wavebands between 400 and 900 nm, and the all determination coefficients(r) are above 0.84. The r of single variety model and adjusted model were 3.1%-4.8% and 2.0%-4.2% higher than those of the integrated model, respectively. The models were tested using data other than the modeling sample and the predictive r of the models based on single variety data were 0.6%-11.0% higher than that of the integrated model and the root mean square error(RMSE) were 10.0%-37.0% lower. Therefore, the model based on single variety data can be used to improve the precision of the model when using hyperspectral remote sensing technology to estimate LAI of winter wheat. |
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| Keywords: | Winter wheat Leaf area index Variety variation Hyperspectral Estimation model |
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