摘要
为进一步提高无人机遥感估产的精度,本研究以2021—2022年的覆膜冬小麦为研究对象,对返青期、拔节期、抽穗期和灌浆期的多光谱影像进行覆膜背景剔除,并优选最佳遥感窗口期,基于最优植被指数构建覆膜冬小麦估产模型。结果表明,利用支持向量机监督分类法剔除覆膜背景后冠层反射率更接近真实值,抽穗期和灌浆期的估产精度更高。将不同生育期的植被指数与产量进行相关性分析发现,最佳遥感窗口期为抽穗期。基于逐步回归和全子集回归法优选最优植被指数时发现,基于逐步回归法筛选变量为MCARI、MSR、EVI2、NDRE、VARI、NDGI、NGBDI、ExG时产量反演模型精度最高。此外,利用偏最小二乘法、人工神经网络和随机森林3种机器学习法构建的产量反演模型中,基于逐步回归法的随机森林模型的反演精度最高,R^(2)为0.82,RMSE为0.84 t/hm^(2)。该研究可为提高遥感估产精度、实现农业生产精细化管理提供技术支持。
In order to further improve the accuracy of UAV remote sensing yield estimation,taking the mulched winter wheat from 2021 to 2022 as the research object,the coating background of the multispectral images at the greening stage,jointing stage,ear pumping stage and filling stage was removed,and the best remote sensing window period was selected,and a mulched winter wheat yield estimation model was constructed based on the optimal vegetation index.The results showed that the canopy reflectivity was closer to the true value after removing the coating background by the support vector machine supervised classification method,and the yield estimation accuracy of the ear stage and the grouting stage was higher.The correlation analysis between vegetation index and yield at different growth stages showed that the best remote sensing window period was the ear extraction period.When the optimal vegetation index was selected based on stepwise regression and full subset regression,it was found that the yield inversion model had the highest accuracy when the screening variables were MCARI,MSR,EVI2,NDRE,VARI,NDGI,NGBDI,ExG based on stepwise regression.In addition,among the yield inversion models constructed by three machine learning methods,partial least squares,artificial neural network and random forest,the random forest model based on stepwise regression method had the highest inversion accuracy,with an R^(2) of 0.82 and an RMSE of 0.84 t/hm^(2).The research result can provide technical support for improving the accuracy of remote sensing yield estimation and realizing the fine management of agricultural production.
作者
韦春宇
杜娅丹
程智楷
周智辉
谷晓博
WEI Chunyu;DU Yadan;CHENG Zhikai;ZHOU Zhihui;GU Xiaobo(Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas,Ministry of Education,Northwest A&F University,Yangling,Shaanxi 712100,China)
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2024年第4期146-154,175,共10页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家重点研发计划项目(2021YFD1900700)
陕西省重点研发计划项目(2022NY-114)。
关键词
覆膜冬小麦
植被指数
产量估计
无人机遥感
特征选择
机器学习
mulched winter wheat
vegetation index
yield estimation
UAV remote sensing
feature selection
machine learning
