中国农业科学 ›› 2019, Vol. 52 ›› Issue (13): 2220-2229.doi: 10.3864/j.issn.0578-1752.2019.13.003

• 耕作栽培·生理生化·农业信息技术 • 上一篇    下一篇

基于低空无人机影像光谱和纹理特征的棉花氮素营养诊断研究

陈鹏飞1,2,梁飞3   

  1. 1 中国科学院地理科学与资源研究所/资源与环境信息系统国家重点实验室,北京100101
    2 江苏省地理信息资源开发与利用协同创新中心,南京 210023
    3 新疆农垦科学院农田水利与土壤肥料研究所,新疆石河子832000
  • 收稿日期:2019-02-25 接受日期:2019-03-14 出版日期:2019-07-01 发布日期:2019-07-11
  • 作者简介:陈鹏飞,E-mail:pengfeichen@igsnrr.ac.cn
  • 基金资助:
    国家重点研发计划(2017YFD02015);国家重点研发计划(2017YFD0201501-05);国家自然科学基金(41871344)

Cotton Nitrogen Nutrition Diagnosis Based on Spectrum and Texture Feature of Images from Low Altitude Unmanned Aerial Vehicle

CHEN PengFei1,2,LIANG Fei3   

  1. 1 Institute of Geographical Science and Natural Resources Research, Chinese Academy of Sciences/State Key Laboratory of Resources and Environmental Information System, Beijing 100101
    2 Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023
    3 Institute of Farmland Water Conservancy and Soil Fertilizer, Xinjiang Academy of Agricultural and Reclamation Science, Shihezi 832000, Xinjiang
  • Received:2019-02-25 Accepted:2019-03-14 Online:2019-07-01 Published:2019-07-11

摘要:

【目的】基于无人机高空间分辨率影像,探讨剔除土壤背景信息及增加纹理信息对棉花植株氮浓度反演的影响,为棉花氮素营养精准探测提供新技术手段。【方法】开展棉花水、氮耦合试验,分别在棉花的不同生育期获取无人机多光谱影像和植株氮浓度信息。基于以上数据,首先探讨了土壤背景对棉花冠层光谱的影响;其次,分析了影像纹理特征与植株氮浓度间的相关性;最后,将获得的数据分为建模样本和检验样本,设置剔除土壤背景前、剔除土壤背景后、增加纹理特征等不同情景,采用光谱指数与主成分分析耦合建模的方法,来建立各种情景下植株氮浓度的反演模型,并对模型反演效果进行比较。【结果】土壤背景对棉花冠层光谱有影响,且不同生育期趋势不同;影像纹理特征参数与植株氮浓度间有显著相关关系;剔除土壤背景前植株氮浓度反演模型的建模决定系数为0.33,标准误差为0.21%,验证决定系数为0.19,标准误差为0.23%;剔除土壤背景后模型的建模决定系数为0.38,标准误差为0.20%,验证决定系数为0.30,标准误差为0.21%;增加纹理信息后模型的建模决定系数为0.57,标准误差为0.17%,验证决定系数为0.42,标准误差为0.19%。【结论】基于低空无人机高空间分辨率影像,剔除土壤背景和增加纹理特征均可提高棉花植株氮浓度的反演精度;影像纹理可以作为一种重要信息来支撑无人机遥感技术反演作物氮素营养状况。

关键词: 无人机, 多光谱, 图像纹理特征, 氮素营养诊断, 棉花

Abstract:

【Objective】 Based on the high spatial resolution images of unmanned aerial vehicle (UAV), the effects of removing soil background information and increasing image texture information on the inversion of cotton plant nitrogen concentration were investigated, in order to provide new technology for accurate estimation of cotton nitrogen nutrition status. 【Method】 Cotton water and nitrogen coupling experiment was conducted, and UAV images and plant nitrogen concentration data were measured during different cotton growth stages. Based on the above data, the effect of soil background on cotton canopy spectrum was firstly investigated. Secondly, the correlations between image texture parameters and plant nitrogen concentration were analyzed. Finally, the obtained data was divided into calibration dataset and validation dataset. Different scenarios, including before and after removing the soil background, and adding texture features, were set. The inversion models of plant nitrogen concentration under various scenarios were designed by using the coupled method of spectral indexes and principal component regression, and the performances of the models were compared. 【Result】 The soil background had an effect on the cotton canopy spectrum, and the trends were not the same at different growth stages. There existed significant correlations between image texture parameters and plant nitrogen concentration. For the scenarios before removal soil background, the plant nitrogen concentration prediction model had determination coefficient (R 2) value of 0.33 and root mean square error (RMSE) value of 0.21% during model calibration, and R 2 value of 0.19 and RMSE value of 0.23% during validation. For the scenarios after removing soil background, the plant nitrogen concentration prediction model had R 2 value of 0.38 and RMSE value of 0.20% during model calibration, and R 2 value of 0.30 and RMSE value of 0.21% during validation. For the scenarios adding image texture information, the plant nitrogen concentration prediction model had R 2 value of 0.57 and RMSE value of 0.17% during model calibration, and R 2 value of 0.42 and RMSE value of 0.19% during validation. 【Conclusion】 Based on high spatial resolution images of low-altitude UAVs, both removing soil background and adding image texture information could improve the inversion accuracy of cotton plant nitrogen concentration. Image texture could be considered as important information to support prediction of crop nitrogen nutrition status using UAV images.

Key words: unmanned aerial vehicle (UAV), multi-spectra, image texture feature, nitrogen nutrition diagnosis, cotton