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作 者:王来刚 徐建华[2] 贺佳 李冰[3] 杨秀忠 王利军 郭燕 WANG Lai-gang;XU Jian-hua;HE Jia;LI Bing;YANG Xiu-zhong;WANG Li-jun;GUO Yan(Institute of Agricultural Economics and Information,Henan Academy of Agricultural Sciences,Zhengzhou 450002;Jilin Academy of Agricultural Sciences,Changchun 130033;Huanghe Science and Technology University,Zhengzhou 450000,China)
机构地区:[1]河南省农业科学院农业经济与信息研究所,郑州450002 [2]吉林省农业科学院,长春130033 [3]黄河科技学院,郑州450000
出 处:《玉米科学》2020年第6期88-93,共6页Journal of Maize Sciences
基 金:国家重点研发计划项目(2016YFD0300609,2018YFD0300702);河南省农业科学院自主创新项目(2019ZC46)。
摘 要:以2018和2019年在河南省新乡县中国农业科学院农田灌溉研究所试验基地的玉米为研究对象,利用八旋翼无人机搭载的MicaSense RedEdge多光谱相机对试验区进行遥感监测,构建玉米叶面积无人机遥感监测模型和产量估算模型,并在示范区进行应用。结果表明,NDVI、EVI和GNDVI这3种植被指数在构建叶面积指数监测模型中具有较好的精度和稳定性。利用抽雄期植被指数构建的估产模型精度最高,吐丝期次之,拔节期最低。与单生育期估产模型相比,累积3个生育期植被指数构建的估产模型精度有一定提升,R2为0.87,RMSE为405.42 kg/hm2。构建的无人机遥感监测模型,可以快速有效评估玉米长势和产量。The experiments were carried out during 2018-2019 at Experimental Bases Farmland Irrigation Research Institute,Chinese Academy of Agricultural Sciences,Xinxiang,China,and study the estimate models of leaf area index and yield of maize derived from Unmanned Aerial Vehicle(UAV)multispectral remote sensing in different growth stages,and then mapping the leaf area index and yield of maize in demonstrative region based on these models.The result showed that,there were accuracy and stability of the estimate model of leaf area index based on NDVI,EVI and GNDVI.There was the highest accuracy of the yield estimate model based on vegetation indexes in tasseling stage,followed by the silking stage,and the lowest jointing stage.Compared the accuracy of the yield estimation model,the cumulative vegetation index from 3 growth stages was higher than the single growth stage,and the determination coefficient(R2)is 0.87,root mean square error(RMSE)is 405.42 kg/ha.The monitoring model of UAV remote sensing in this research,could monitoring and evaluation for growth potential and yield of maize by quickly and effectively.
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