| 基于无人机可见光影像与生理指标的小麦估产模型研究 |
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| 引用本文： | 王嘉盼,武红旗,王德俊,轩俊伟,郭 涛,李永康.基于无人机可见光影像与生理指标的小麦估产模型研究[J].麦类作物学报,2021(10):1307-1316. |
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| 作者姓名： | 王嘉盼 武红旗 王德俊 轩俊伟 郭 涛 李永康 |
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| 作者单位： | （1.新疆农业大学草业与环境科学学院, 新疆乌鲁木齐 830052; 2.新疆土壤与植物生态过程实验室, 新疆乌鲁木齐 830052; 3.新疆草地修复与环境信息重点实验室，新疆乌鲁木齐 830052） |
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| 基金项目： | 国家自然科学基金项目（31560340） |
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| 摘 要： | 为及时、准确地掌握小麦产量动态信息，基于无人机遥感平台，分别分析了小麦4项生理指标［地面实测叶面积指数、叶片含氮量、叶片含水量及叶片叶绿素相对含量（SPAD值）］及10项植被指数与产量的相关性，以筛选出与产量最为敏感的生理指标与植被指数，并比较了3种建模方法（一元回归UR、多元逐步回归SMLR和主成分回归PCAR）在小麦各生育时期估产的适用性，进而得到小麦最优估产模型。结果表明：（1）不同生育时期两类变量与产量的相关性变化特征一致，均表现为抽穗期>灌浆期>成熟期；不同生理指标、植被指数与产量的相关性在各生育时期均存在差异，生理指标表现为叶片含氮量>LAI>SPAD>叶片含水量；而植被指数在各时期表现不同；（2）以生理指标与植被指数为自变量，采用SMLR模型构建的抽穗期估产模型拟合精度最高，R、RMSE和nRMSE分别为0.828、362.53 kg·hm-2和12.35%；（3）小麦估产模型在各生育时期的预测精度表现为抽穗期>灌浆期>成熟期。
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| 关 键 词： | 无人机 小麦 生理指标 植被指数 估产模型 |
Research on Wheat Yield Estimation Model Based on UAV Visible Light Image and Physiological Index |
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| WANG Jiapan,WU Hongqi,WANG Dejun,XUAN Junwei,GUO Tao,LI Yongkang.Research on Wheat Yield Estimation Model Based on UAV Visible Light Image and Physiological Index[J].Journal of Triticeae Crops,2021(10):1307-1316. |
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| Authors: | WANG Jiapan WU Hongqi WANG Dejun XUAN Junwei GUO Tao LI Yongkang |
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| Abstract: | In order to timely and accurately obtain the dynamic information of wheat yield, based on the UAV remote sensing platform, the correlation between four physiological indices (leaf area index, leaf nitrogen content, leaf water content and chlorophyll) and 10 vegetation indices and wheat yield was analyzed respectively, so as to select the most sensitive physiological index and vegetation index, and the three modeling methods (one-way regression) were compared. The applicability of multiple stepwise regression (UR), multiple stepwise regression (SMLR) and principal component regression (PCAR) in wheat yield estimation at different growth stages was studied, and then the optimal yield estimation model of wheat was obtained. The results showed that:(1) the correlation characteristics of two kinds of variables and yield at different growth stages were the same, ranking as heading stage > filling stage > mature stage; the correlation of different physiological indices, vegetation index and yield were different at each growth stage; and the physiological indices ranked as leaf nitrogen content > LAI> SPAD > leaf water content, while the vegetation index was different at each growth stage; (2) the correlation of different physiological indices, vegetation index and yield was different at each growth stage. SMLR model had the highest fitting accuracy, with R, RMSE and nRMSE of 0.828 kg·hm-2, 362.53 kg·hm-2 and 12.35%,respectively; (3) the prediction accuracy of wheat yield estimation model at different growth stages ranked as heading stage > filling stage > maturity stage. |
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| Keywords: | Unmanned aerial vehicle Wheat Physiological index Vegetation index Yield estimation model |
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