| 不同生育时期冬小麦籽粒蛋白质含量的高光谱遥感监测模型 |
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| 引用本文： | 贺佳,刘冰峰,黎世民,郭燕,王来刚,张彦,李军.不同生育时期冬小麦籽粒蛋白质含量的高光谱遥感监测模型[J].中国生态农业学报,2017,25(6):865-875. |
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| 作者姓名： | 贺佳 刘冰峰 黎世民 郭燕 王来刚 张彦 李军 |
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| 作者单位： | 1. 河南省农业科学院农业经济与信息研究所 郑州 450002;西北农林科技大学农学院 杨凌 712100;2. 西北农林科技大学农学院 杨凌 712100;3. 河南省农业科学院农业经济与信息研究所 郑州 450002 |
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| 基金项目： | 国家高技术研究发展计划（863计划）项目（2013AA102902）、国家自然科学基金项目（31071374，30771280，41601213）和河南省农业科学院优秀青年基金项目（2016YQ21）资助 |
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| 摘 要： | 为研究不同氮磷水平下冬小麦籽粒蛋白质含量高光谱遥感监测模型,提高模型精度,本文通过连续5年定位试验研究不同氮磷耦合水平下,不同生育时期冬小麦冠层光谱反射率、植株氮含量以及成熟期籽粒蛋白质含量,以相关、回归等统计分析方法,建立基于不同生育时期植株氮含量的籽粒蛋白质含量监测模型;然后通过灰色关联度分析,筛选植株氮含量的最佳植被指数,以偏最小二乘回归法,建立基于植被指数的植株氮含量监测模型;最后以植株氮含量为链接点,按照"植被指数—植株氮含量—籽粒蛋白质含量"之间的联系,建立融合植被指数与植株氮含量的冬小麦成熟期籽粒蛋白质含量监测模型。结果表明:在拔节期、孕穗期、抽穗期、灌浆期、成熟期基于植株氮含量建立的成熟期籽粒蛋白质含量监测模型,具有较好的监测精度;拔节期、孕穗期、抽穗期、灌浆期、成熟期分别基于修正叶绿素吸收反射率指数(MCARI_1)、归一化差值叶绿素指数(NDCI)、修正归一化差异指数(mNDVI)、MCARI_1、NDCI植被指数建立植株氮含量监测模型,监测精度(R~2)分别为0.826、0.854、0.867、0.859和0.819;以植株氮含量为链接点,通过"植被指数—植株氮含量—籽粒蛋白质含量"的间接联系,建立基于拔节期、孕穗期、抽穗期、灌浆期、成熟期植被指数且融合植株氮含量的籽粒蛋白质含量监测模型,R~2分别为0.935、0.972、0.990、0.979和0.936;以独立数据对模型进行验证,模型预测值与实测值间相对误差(RE)分别为11.26%、10.74%、8.41%、10.25%和11.36%,均方根误差(RMSE)分别为2.221 g×kg~(-1)、1.825 g×kg~(-1)、1.214 g×kg~(-1)、1.767 g×kg~(-1)和2.137 g×kg~(-1)。说明基于不同生育时期植被指数链接植株氮含量可以对成熟期籽粒蛋白质含量进行有效监测,且模型具有较好的年度间重演性和品种间适应性。
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| 关 键 词： | 冬小麦 高光谱遥感 冠层光谱反射率 籽粒蛋白质含量 植株含氮量 植被指数 |
| 收稿时间： | 2016/11/25 0:00:00 |
| 修稿时间： | 2016/12/21 0:00:00 |
Winter wheat grain protein content monitoring model driven by hyperspectral remote sensing images at different growth stages |
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| HE Ji,LIU Bingfeng,LI Shimin,GUO Yan,WANG Laigang,ZHANG Yan and LI Jun.Winter wheat grain protein content monitoring model driven by hyperspectral remote sensing images at different growth stages[J].Chinese Journal of Eco-Agriculture,2017,25(6):865-875. |
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| Authors: | HE Ji LIU Bingfeng LI Shimin GUO Yan WANG Laigang ZHANG Yan and LI Jun |
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| Institution: | Institute of Agricultural Economics and Information, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China;College of Agronomy, Northwest A&F University, Yangling 712100, China,College of Agronomy, Northwest A&F University, Yangling 712100, China,Institute of Agricultural Economics and Information, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China,Institute of Agricultural Economics and Information, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China,Institute of Agricultural Economics and Information, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China,Institute of Agricultural Economics and Information, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China and College of Agronomy, Northwest A&F University, Yangling 712100, China |
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| Abstract: | Hyperspectral remote sensing data have strong band continuity, high spectral resolution and rich spectrum information. It can rapidly and nondestructively acquire vegetation information and it is an reliable real-time technology applicable in the monitoring and management of crop growth. Grain protein content (GPC) is an important indicator for wheat quality. Early detection of GPC of wheat using hyperspectral remote sensing data can enhance effective decision-making to support the acquisition and processing of high quality wheat. The objectives of this study were to establish GPC estimation model based on winter wheat canopy hyperspectral reflectance at different growth stages with different rates of nitrogen or phosphorus applications. The overall goal was to improve forecast precision of GPC estimation model at different growth stages of winter wheat. Thus experiments were carried out in 2009-2014 at Northwest A&F University, Shaanxi Province. The treatments included different winter wheat varieties with various drought resistances under five nitrogen fertilizer application rates (0, 75, 150, 225 and 300 kg·hm-2 pure nitrogen) and four phosphorus application rates (0, 60, 120 and 180 kg·hm-2 P2O5). Plant nitrogen content (PNC) and canopy hyperspectral reflectance of different wheat cultivars were measured at jointing, booting, heading, filling and ma-turity stages. Also GPC was measured at maturity stage. The relationship among PNC, canopy hyperspectral reflectance and GPC was explored using correlation analysis, regression analysis, grey relation analysis or partial least squares. The GPC monitoring model was built according to the relation of "vegetation index based on canopy hyperspectral reflectance (VI)—PNC—GPC" with PNC as the linking point. The results showed a higher GPC prediction accuracy by GPC monitoring model based on PNC at jointing, booting, heading, grain-filling and maturity stages. The monitoring models of PNC at jointing, booting, heading, filling, maturity stages of winter wheat respectively based on modified chlorophyll absorption reflectance index (MCARI1), normalized difference chlorophyll index (NDCI), modified normalized difference vegetation index (mNDVI), MCARI1 and NDCI had better estimations of PNC, with determination coefficients (R2) of 0.826, 0.854, 0.867, 0.859 and 0.819, accordingly. When linked with PNC, by using the "VI—PNC—GP" method, the GPC monitoring models for the maturity stage consisted of combinations of VI and PNC at jointing, booting, heading, filling, maturity stages had the determination coefficients (R2) of 0.935, 0.972, 0.990, 0.979 and 0.936, respectively. Then validatation of the models with measured values was conducted to verify the reliability and applicability of the models. The results showed that the relative errors (RE) between the measured and predicted values for the five vegetation indices were 11.26%, 10.74%, 8.41%, 10.25% and 11.36%, respectively. Then the corresponding root mean square errors (RMSE) were 2.221 g·kg-1, 1.825 g·kg-1, 1.214 g·kg-1, 1.767 g·kg-1 and 2.137 g·kg-1. It therefore suggested that MCARI1, NDCI, mNDVI, MCARI1 and NDCI vegetation indices were the most suitable model for monitoring winter wheat GPC at jointing, booting, heading, filling and maturity stages, respectively. There was higher prediction precision with different vegetation indices at different growth stages monitoring winter wheat GPC under different N and P rates. Furthermore, the monitoring model based on different vegetation indices at different growth stages had a higher prediction accuracy. This results provided technical support for GPC monitoring of winter wheat at different fertilization and different growth stages. |
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| Keywords: | Winter wheat Hyperspectral remote sensing Canopy spectral reflectance Grain protein content Plant nitrogen content Vegetation index |
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