| 无人机多光谱遥感反演冬小麦SPAD值 |
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| 引用本文： | 周敏姑,邵国敏,张立元,姚小敏,韩文霆.无人机多光谱遥感反演冬小麦SPAD值[J].农业工程学报,2020,36(20):125-133. |
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| 作者姓名： | 周敏姑 邵国敏 张立元 姚小敏 韩文霆 |
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| 作者单位： | 西北农林科技大学旱区节水农业研究院,杨凌 712100;西北农林科技大学机械与电子工程学院,杨凌 712100;西北农林科技大学水土保持研究所,杨凌 712100 |
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| 基金项目： | 十三五国家重点研发计划项目（2017YFC0403203）；杨凌示范区产学研用协同创新重大项目（2018CXY-23） |
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| 摘 要： | 为研究无人机多光谱遥感5个波段光谱反射率反演冬小麦SPAD（Soil and Plant Analyzer Development）值的可行性，该研究采用六旋翼无人机搭载五波段多光谱相机，采集冬小麦拔节期、孕穗期、抽穗期、开花期的冠层光谱影像并提取反射率特征参数，建立SPAD值的反演模型。结果表明，当波长范围在蓝光、绿光和红光波段，冬小麦拔节期、孕穗期和开花期的无人机多光谱影像反射率参数与SPAD值呈负相关关系，而在抽穗期，二者呈正相关；当波长范围为红边及近红外波段，在整个生长期，二者均呈现正相关关系。该研究构建冬小麦SPAD值反演模型采用了主成分回归、逐步回归和岭回归法，经对比发现基于逐步回归法构建的模型效果最优，该模型的校正决定系数为0.77，主成分回归法次之，岭回归法较差。此外，冬小麦抽穗期多光谱反射率反演SPAD值效果最显著，3种回归模型的校正决定系数分别为0.72、0.74和0.77。该研究可为无人机多光谱遥感监测作物长势、实现精准农业生产管理提供技术依据。
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| 关 键 词： | 无人机 遥感 冬小麦 多光谱影像 回归模型 SPAD |
| 收稿时间： | 2020/3/4 0:00:00 |
| 修稿时间： | 2020/5/25 0:00:00 |
Inversion of SPAD value of winter wheat by multispectral remote sensing of unmanned aerial vehicles |
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| Zhou Mingu,Shao Guomin,Zhang Liyuan,Yao Xiaomin,Han Wenting.Inversion of SPAD value of winter wheat by multispectral remote sensing of unmanned aerial vehicles[J].Transactions of the Chinese Society of Agricultural Engineering,2020,36(20):125-133. |
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| Authors: | Zhou Mingu Shao Guomin Zhang Liyuan Yao Xiaomin Han Wenting |
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| Institution: | 1. Institute of Water-saving Agriculture in Arid Area of China, Northwest A&F University, Yangling 712100, China;2. College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China; 3. Institute of Soil and Water Conservation, Northwest A&F University, Yangling 712100, China |
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| Abstract: | Abstract: Remote sensing technology has been widely used to monitor the changes in SPAD, which is an important parameter. In this study, the multispectral images were acquired by a six-rotor unmanned aerial vehicle, and the SPAD of winter wheat was measured to carry out the estimation research. The four growth stages with the most obvious changes in SPAD were selected, namely the jointing stage, booting stage, heading stage, and flowering stage. The camera with five bands (475, 560, 668, 717, and 840 nm) was used to collect multispectral canopy leaves at the four stages. A total of four data collections were performed to extract spectral reflectance data and the SPAD was measured from 1st April to 27th April 2018. A total of 65 samples were selected and recorded with GPS. The test area was divided into 65 sample zones with each one measuring 2.5 m×25 m, of which one sample area of 1 m × 1 m was selected. All the zones were in a rectangle, so they could be evenly distributed 8 m from the center of the cell in the horizontal direction. The overall samples were S-shaped distribution. The samples in the middle were located at the center of the rectangular cell. The SPAD of 65 samples were measured by SPAD-502 chlorophyll meter at the same time when the UAV data was collected. In the sample area, seven leaves of different canopy parts were selected to measure the tip, middle, and base. The average of the three parts was used as the SPAD values of the leaf. Finally, the average value of the leaf blades was taken as the final SPAD value of the sample. The canopy reflectance data was extracted from multispectral images. And then the correlation coefficients of SPAD values and spectral reflectance data in four growth stages were analyzed. Herein, the reflectivity of single-band and SPAD directly had serious collinearity problems so principal component regression, stepwise regression, and ridge regression these three methods were chosen to solve it. After that, the SPAD inversion models were established separately by using the reflectance data and the SPAD values as the data source. The best inversion model and stage were selected by comparison. The results showed that a high correlation was obtained between the SPAD and canopy spectral reflectance. In the visible light band, the negative correlation was observed between canopy spectral reflectance and SPAD at the jointing stage, booting stage, and flowering stage. On the contrary, it was a positive correlation at the heading stage and a positive correlation at the red-edge and near-infrared bands at all four stages. Compared with the main bands in the model expression, the frequency of passing the screening in different growth stages was different. The highest passing frequency was the near-infrared band in the jointing stage. The blue band was selected at the booting stage, the near-infrared band at the heading stage, and the green and red bands at the flowering stage. This study compared the prediction accuracy of the models established by three regression methods. The results showed that the models of stepwise regression established at the heading stage had the highest inversion accuracy with the adjusted coefficient of determination was 0.77, and the Root Mean Square Error was 0.61. The validation showed the coefficient of determination was 0.73, and the Root Mean Square Error was 0.56. It indicated that the model could be used to estimate the crop coefficient. Compared with the four periods, the heading stage was the best inversion stage of SPAD value. The study results proved the feasibility of inversion of the winter wheat SPAD value by unmanned aerial vehicle multispectral remote sensing, and at the same time, it could provide a reference for the rapid monitoring of the SPAD value of other crops. |
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| Keywords: | unmanned aerial vehicle remote sensing winter wheat multispectral image regression model SPAD |
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