| 基于无人机多光谱遥感的夏玉米冠层叶绿素含量估计 |
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| 引用本文： | 陈浩,冯浩,杨祯婷,王乃江,李悦,王青松.基于无人机多光谱遥感的夏玉米冠层叶绿素含量估计[J].排灌机械工程学报,2021,39(6):622-629. |
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| 作者姓名： | 陈浩 冯浩 杨祯婷 王乃江 李悦 王青松 |
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| 作者单位： | 1.西北农林科技大学水利与建筑工程学院, 陕西 杨凌 712100;2. 西北农林科技大学中国旱区节水农业研究院, 陕西 杨凌 712100; 3. 西北农林科技大学水土保持研究所, 陕西 杨凌 712100; 4. 中国科学院水利部水土保持研究所, 陕西 杨凌 712100 |
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| 摘 要： | 为探讨利用无人机多光谱遥感影像监测夏玉米冠层叶绿素含量的可行性,基于2019年不同施氮水平下(0,105,210,315 kg·N/hm2)夏玉米多光谱遥感影像和田间实测冠层叶绿素含量数据,分析了不同施氮水平下夏玉米冠层叶绿素含量的变化规律,同时选取10种常用光谱植被指数与实测冠层叶绿素含量进行相关性分析,采用与实测叶绿素含量极显著相关的9种植被指数,构建了基于遥感光谱指数的夏玉米冠层叶绿素含量遥感监测模型,并通过精度检验确定最优估测模型.结果表明,施用氮肥能够提高夏玉米冠层叶绿素含量,过量氮肥不能持续提高叶绿素含量,同一施氮水平下不同追肥处理之间叶绿素含量没有明显差异.绿色归一化植被指数与叶绿素含量的相关性系数最高,达到了0.892.采用逐步回归分析方法建立的模型表现最优,决定系数为0.87,均方根误差及相对误差分别为0.15和2.68%.因此,无人机多光谱遥感结合逐步回归模型可以实现田间尺度的夏玉米冠层叶绿素含量的实时监测.
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| 关 键 词： | 夏玉米 无人机 多光谱遥感 叶绿素含量 |
| 收稿时间： | 2020-09-27 |
Estimation of chlorophyll content of summer maize canopy based on UAV multispectral remote sensing |
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| CHEN Hao,FENG Hao,YANG Zhenting,WANG Naijiang,LI Yue,WANG Qingsong.Estimation of chlorophyll content of summer maize canopy based on UAV multispectral remote sensing[J].Journal of Drainage and Irrigation Machinery Engineering,2021,39(6):622-629. |
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| Authors: | CHEN Hao FENG Hao YANG Zhenting WANG Naijiang LI Yue WANG Qingsong |
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| Institution: | 1. College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China; 2. Institute of Water-saving Agriculture in Arid Areas of China, Northwest A&F University, Yangling, Shaanxi 712100, China; 3. Institute of Soil and Water Conservation, Northwest A&F University, Yangling, Shaanxi 712100, China; 4. Institute of Soil and Water Conservation, CAS & MWR, Yangling, Shaanxi 712100, China |
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| Abstract: | The remote sensing of unmanned aerial vehicle(UAV)has the advantages of monitoring crop nutritional status accurately and flexibly. Modeling crop canopy chlorophyll content is of great significance for efficient agricultural management. In the present study, the multispectral remote sensing data of UAV at different levels of nitrogen fertilizer rate were used to estimate the chlorophyll content of summer maize canopy in 2019. Firstly, 10 spectral vegetation indexes were selected to determine the vegetation indices that were significantly related to the chlorophyll content of summer maize canopy. Secondly, the estimation models of chlorophyll content were established by linear regression and stepwise regression analyses. The results showed that the chlorophyll content of summer maize canopy increased first and then decreased with the nitrogen fertilizer rate increasing. There is no significant difference in chlorophyll content between different topdressing treatments at the same level of nitrogen fertilizer rate. 9 out of 10 spectral vegetation indexes were significantly correlated with chlorophyll content, especially GNDVI whose correlation coefficient with chlorophyll content was the highest and reached 0.892. In comparison with linear regression models, the stepwise regression model showed the best performance in modeling chlorophyll content with coefficient of determination of 0.87, root mean square error of 0.15, and relative error of 2.68%. Therefore, the real-time monitoring of chlorophyll content for summer maize canopy can be achieved by UAV multispectral remote sensing combined with stepwise regression model at the field scale. |
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| Keywords: | summer maize unmanned aerial vehicle multispectral remote sensing chlorophyll content |
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