冠层光谱红边参数结合随机森林机器学习估算冬小麦叶绿素含量  

Estimation of winter wheat chlorophyll content by combing canopy spectrum red edge parameters with random forest machine learning

作　　者：王震 李映雪[4] 吴芳 邹晓晨 WANG Zhen;LI Yingxue;WU Fang;ZOU Xiaochen(School of Remote Sensing and Surveying Engineering,Nanjing University of Information Science and Technology,Nanjing 210044,China;Technology Innovation Center of Integration Applicaitons in Remote Sensing and Navigation,Ministry of Natural Resources,P.R.China,Nanjing 210044,China;Jiangsu Engineering Center for Collaborative Navigation/Positioning and Smart Applications,Nanjing 210044,China;School of Ecology and Applied Meteorology,Nanjing University of Information Science and Technology,Nanjing 210044,China;Xinghua Meteorological Bureau,Xinghua 225700,China)

机构地区：[1]南京信息工程大学遥感与测绘工程学院,南京210044 [2]自然资源部遥感导航一体化应用工程技术创新中心,南京210044 [3]江苏省协同精密导航定位与智能应用工程研究中心,南京210044 [4]南京信息工程大学生态与应用气象学院,南京210044 [5]兴化市气象局,兴化225700

出　　处：《农业工程学报》2024年第4期166-176,共11页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金项目(41801243)。

摘　　要：作物光谱红边参数与叶绿素含量密切相关,是作物生长发育和营养状况的指示器。基于红边参数构建叶绿素含量探测模型是大尺度监测作物长势的有效方法。为提升冬小麦叶绿素含量探测精度,构建适用于不同生育期和施氮水平条件的叶片叶绿素相对含量(chlorophyll content,CHL)估算模型。该研究通过4 a大田试验,获取冬小麦4个关键生育期(拔节期、抽穗期、开花期和灌浆期)和3种施氮水平条件下的冠层光谱反射率和叶片CHL。系统比较和评估了47种光谱红边参数对CHL的敏感性,同时采用逐步选择红边参数相对重要性提升了随机森林机器学习模型估算冬小麦CHL的精度。结果表明:光谱红边参数对CHL的敏感性受到冬小麦生育期和施氮水平的影响,在单一生育期中的最佳红边参数与CHL的决定系数R^(2)在0.39和0.89之间。全生育期中最佳红边参数为NDDRmid,与CHL的决定系数R^(2)为0.76。灌浆期敏感性最高,红边参数REPRpi、NDDRmid、RVI2、RVI4、RVI5、RVI6、NDRE、RVI12和RVI13与CHL的决定系数都高于0.80,红边参数RVI5与CHL的决定系数R^(2)为0.89。单一施氮水平条件下敏感性最佳的红边参数与CHL的决定系数在0.75和0.81之间。在N1和N2条件下,最佳红边参数为NDDRmid。在N3条件下RIDRfd与CHL的决定系数最高,R^(2)为0.81。在所评估的光谱红边参数中,NDDRmid、RVI5、RVI12和DIDA在单一生育期和施氮水平条件下都表现出较高的相关性。逐步选择相对重要性红边参数特征优化随机森林模型提升了CHL的估算精度,全生育期中最佳估算精度为R^(2)=0.80和RMSE=4.25。不同生育期和施氮水平条件下,红边参数DIDA和RVI13都作为随机森林模型的重要特征。研究结果揭示了光谱红边参数在不同生育期和施氮条件下估算冬小麦CHL的潜力,同时也为基于红边参数特征的其他类型农作物叶绿素含量探测研究提供了参考。The sudden increase in vegetation canopy reflectance from low reflectance in red band to near infrared band forms the red-edge spectral characteristics,which is unique to healthy vegetation.Many parameters that can describe this characteristic have been designed and developed as important indicators of crop growth and nutrition status.However,few studies have systematically compared and evaluated the applicability of these red-edge parameters to estimate winter wheat leaf CHL values at different growth stages and nitrogen application levels.In this study,canopy spectral reflectance and leaf CHL of winter wheat at 4 key growth stages(jointing stage,heading stage,flowering stage and filling stage)and 3 nitrogen application levels were obtained through a 4-year field experiment.The sensitivity of 47 spectral red-edge parameters to CHL was evaluated,and the relative importance of spectral red-edge parameters was used to optimize the random forest machine learning model to estimate winter wheat CHL.The results showed that the sensitivity of spectral red edge parameter to CHL was affected by the growth period and nitrogen application level of winter wheat,and the correlation R^(2) between the best red edge parameter and CHL in a single growth period was between 0.39 and 0.89.The best red-edge parameter in the whole growth period was NDDRmid,and the coefficient of determination between it and CHL was 0.76.The sensitivity was the highest in filling stage,and the coefficient of determination between the best red edge parameter RVI5 and CHL was 0.89 and the R^(2) between red edge parameters REPRpi,NDDRmid,RVI2,RVI4,RVI5,RVI6,NDRE,RVI12 and RVI13 with CHL were all higher than 0.80.Nitrogen application level increased the sensitivity of red-edge parameter to CHL.At single nitrogen application level,the coefficient of determination between best red edge parameter and CHL was between 0.75 and 0.81.At N1 and N2 conditions the best red-edge parameter is NDDRmid,and at N3 condition the best red-edge parameter is RIDRfd(R^(2)=0.81

关 键 词：机器学习 冬小麦 叶绿素 红边参数 SPAD 随机森林 

分 类 号：S126[农业科学—农业基础科学]