摘要
针对物理模型抗噪能力差且容易过拟合的问题,提出一种PROSAIL模型结合VMG(VARI(Visible atmospherically resistant index)、MGRVI(Modified green red vegetation index)、GRRI(Green red ratio index))多元回归模型反演冬小麦叶面积指数(Leaf area index,LAI)方法。实验基于无人机影像(Unmanned aerial vehicles,UAV),选择河南省焦作市东南部的山阳区为实验区,结合实测2个生育期冬小麦LAI数据。首先,构建RGB植被指数模型,选取其中最优VMG模型反演冬小麦LAI;然后,对PROSAIL参数敏感性进行分析,得到参数最优值,反演冬小麦LAI;最后,采用快速模拟退火(Very fast simulated annealing,VFSA)算法将两种模型结合,获得最优冬小麦LAI。结果表明:VFSA可以有效将PROSAIL模型和VMG模型结合,提高了反演精度,且优于VMG模型和PROSAIL模型,决定系数R^(2)高于0.8,均方根误差(RMSE)低于0.4 m^(2)/m^(2)。综上所述,冬小麦生长过程中,地面覆盖度增高,本文方法具有较强的辐射传输机理,为LAI反演提供一种有效的反演方法。
Aiming at the problem that the physical model has poor anti-noise ability and is easy to overfit,a PROSAIL model was proposed by combining VMG(VARI(visible atmospherically resistant index),MGRVI(modified green red vegetation index)and GRRI(green red ratio index))to retrieve the leaf area index(LAI)of winter wheat.The experiment was conducted based on unmanned aerial vehicles(UAV).Shanyang District in the southeast of Jiaozuo City,Henan Province was selected as the experimental area,and LAI data of winter wheat during two growth periods were measured.Firstly,an RGB vegetation index model was constructed,and the optimal VMG model was selected to invert LAI of winter wheat.Then,the sensitivity of PROSAIL parameters was analyzed to obtain the optimal parameter value and invert winter wheat LAI.Finally,the two models were combined using the very fast simulated annealing(VFSA)algorithm to obtain the optimal LAI of winter wheat.The results showed that VFSA can effectively combine PROSAIL model and VMG model to improve the inversion accuracy,and it was better than thta of VMG model and PROSAIL model.The coefficient of determination(R^(2))was higher than 0.8,and the root mean square error(RMSE)was lower than 0.4 m^(2)/m^(2).To sum up,the ground coverage was increased during the growth of winter wheat,and the method presented had strong radiative transmission mechanism,providing an effective inversion method for LAI inversion.
作者
王枭轩
卢小平
杨泽楠
高忠
王璐
张博文
WANG Xiaoxuan;LU Xiaoping;YANG Zenan;GAO Zhong;WANG Lu;ZHANG Bowen(Key Laboratory of Spatio ̄temporal Information and Ecological Restoration of Mines,Ministry of Natural Resources,Henan Polytechnic University,Jiaozuo 454003,China;Hebei Forestry and Grassland Survey Planning and Design Institute,Shijiazhuang 050056,China)
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2022年第6期209-216,共8页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家重点研发计划项目(2016YFC0803103)
河南省高校创新团队支持计划项目(14IRTSTHN026)
农业遥感监测关键技术研究项目。