| 基于无人机高光谱的冬小麦氮素营养监测 |
| |
| 引用本文： | 王玉娜,李粉玲,王伟东,陈晓凯,常庆瑞.基于无人机高光谱的冬小麦氮素营养监测[J].农业工程学报,2020,36(22):31-39. |
| |
| 作者姓名： | 王玉娜 李粉玲 王伟东 陈晓凯 常庆瑞 |
| |
| 作者单位： | 西北农林科技大学资源环境学院,杨凌 712100;西北农林科技大学资源环境学院,杨凌 712100;西北农林科技大学资源环境学院,杨凌 712100;西北农林科技大学资源环境学院,杨凌 712100;西北农林科技大学资源环境学院,杨凌 712100 |
| |
| 基金项目： | 国家自然科学基金项目（41701398）；中央高校基本科研业务项目（2452017108） |
| |
| 摘 要： | 为了实现小区域尺度上的作物氮素营养状况遥感监测，该研究利用无人机搭载Cubert UHD185成像光谱仪对2016 -2017年关中地区的冬小麦进行遥感监测，通过分析冠层光谱参数与植株氮含量、地上部生物量和氮素营养指数的相关性，筛选出对三者均敏感的光谱参数，结合多元线性逐步回归、偏最小二乘回归和随机森林回归建立抽穗期冬小麦氮素营养指数（Nitrogen Nutrition Index，NNI）估测模型，并与单个光谱参数建立的冬小麦氮素营养指数模型进行比较。结果表明，任意两波段光谱指数对氮素营养指数更为敏感，与氮素营养指数均达到了极显著性相关；基于差值光谱指数和红边归一化指数的单个光谱参数构建的模型具有粗略估算氮素营养指数的能力，相对预测偏差分别为1.53和1.56；基于随机森林回归构建的多变量冬小麦氮素营养指数估算模型具有极好的预测能力，模型决定系数为0.79，均方根误差为0.13，相对预测偏差为2.25，可以用来进行小区域范围内的冬小麦氮素营养指数遥感填图，为冬小麦氮素营养诊断、产量和品质监测及后期田间管理提供科学依据。
|
| 关 键 词： | 无人机 高光谱 监测 冬小麦 氮素营养指数 随机森林回归 |
| 收稿时间： | 2020/8/3 0:00:00 |
| 修稿时间： | 2020/9/11 0:00:00 |
Monitoring of winter wheat nitrogen nutrition based on UAV hyperspectral images |
| |
| Wang Yun,Li Fenling,Wang Weidong,Chen Xiaokai,Chang Qingrui.Monitoring of winter wheat nitrogen nutrition based on UAV hyperspectral images[J].Transactions of the Chinese Society of Agricultural Engineering,2020,36(22):31-39. |
| |
| Authors: | Wang Yun Li Fenling Wang Weidong Chen Xiaokai Chang Qingrui |
| |
| Abstract: | Abstract: Nitrogen is a large number of elements required by crop life and is closely related to crop yield and quality. Accurate diagnosis and dynamic monitoring of crops are important contents of precision agriculture research. With the advantages of high timeliness, high resolution, and low cost, Unmanned Aerial Vehicle (UAV) remote sensing is developing rapidly in the diagnosis and monitoring of nitrogen nutrition in winter wheat. In this study, Nitrogen Nutrition Index (NNI) was selected as an indicator of the nitrogen nutritional status of winter wheat. In order to quantitatively estimate the nitrogen nutrition index of winter wheat at a regional scale, hyperspectral images of the winter wheat experimental plots in Qianxian, Shaanxi province during the year 2016-2017 was obtained by the Cubert UHD185 imaging spectrometer boarded on the UAV. Meanwhile, the above-ground biomass and plant nitrogen concentration data were collected during the flying and were used to calculate the NNI according to the local critical nitrogen concentration dilution model of wheat. Three types of spectral parameters were selected, including trilateral parameters, any two bands spectral indices, and vegetation indices. According to the correlation analysis between spectral parameters and plant nitrogen content, aboveground biomass, and nitrogen nutrition index, ten spectral parameters significantly related to each of the three indicators were screened out, including red-edge area, green peak reflectance maximum, red valley reflectivity minimum, difference spectral index, normalized spectral index, ratio spectral index, red-edge position by linear interpolation, red-edge normalized difference vegetation index, Vogelmann red-edge index, and photochemical reflectance index. Finally, nitrogen nutrition index estimation models of winter wheat at the heading stage were established based on the ten spectral parameters above with simple regression, Multiple Linear Stepwise Regression (MLSR), Partial Least Squares Regression (PLSR), and Random Forest Regression (RFR) algorithms respectively. The coefficient of determination, Root Mean Square Error (RMSE), and Relative Prediction Deviation (RPD) were used to compare the accuracy of each model. The results showed that any two bands'' spectral indices were sensitive to the nitrogen nutrition index. The correlation coefficients between any two bands spectral indices and nitrogen nutrition index were dramatically superior to trilateral parameters and typical vegetation indices. The optimal any two bands spectral index was the ratio spectral index made up of the reflectance at 718 and 738 nm. Of all the nitrogen nutrition index estimation models by simple regression, Difference Spectral Index (DSI) and Red-edge Normalized Difference Vegetation Index (RNDVI) had rough estimation capabilities of nitrogen nutrition index, the relative prediction deviation of which were 1.53 and 1.56 respectively. Among the nitrogen nutrition index estimation models of winter wheat established through ten spectral parameters based on multiple linear stepwise regression, partial least squares regression and random forest regression, the model based on the random forest algorithm had the highest precision, strong reliability, and excellent prediction ability. The validation results showed that the coefficient of determination of the model was 0.79, the root mean square error was 0.13, and the relative prediction deviation was 2.25. The second was the model through the partial least squares regression method, the relative prediction deviation of which was1.54. The model by multiple linear stepwise regression methods could not be used to estimate the nitrogen nutrition index correctly. Overall, the model with the random forest algorithm was considered as the best estimation model of the nitrogen nutrition index, which could be used for the remote sensing mapping of the winter wheat nitrogen nutrition index at a regional scale. It would provide a scientific basis for the diagnosis of nitrogen nutrition, monitoring of yield and quality, and late field management of winter wheat. |
| |
| Keywords: | UAV hyperspectral detection winter wheat nitrogen nutrition index random forest regression |
| 本文献已被 万方数据 等数据库收录！ |
| 点击此处可从《农业工程学报》浏览原始摘要信息 |
| 点击此处可从《农业工程学报》下载免费的PDF全文 |
|
