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| 基于粒子群神经网络模型反演玉米、小麦叶面积指数 | |
| 引用本文： | 王枭轩,孟庆岩,张海香,魏香琴,杨泽楠.基于粒子群神经网络模型反演玉米、小麦叶面积指数[J].浙江农业学报,2019,31(7):1170. |
| 作者姓名： | 王枭轩 孟庆岩 张海香 魏香琴 杨泽楠 |
| 作者单位： | 1.中国科学院 遥感与数字地球研究所,北京 100101; 2.昆明理工大学 国土资源工程学院,云南 昆明 650093; 3.三亚中科遥感研究所,海南 三亚 572029 |
| 基金项目： | 海南省重点研发计划(ZDYF2018231); 四川省科技计划(2018JZ0054); 三亚市院地科技合作项目(2018YD10) |
| 摘 要： | 基于高分1号遥感影像,分别采用粒子群神经网络模型、神经网络模型和植被指数回归模型3种方法,反演廊坊市玉米、小麦叶面积指数(LAI)。结果表明,粒子群神经网络模型反演玉米、小麦LAI的精度要高于其他方法,其模型的决定系数R2均高于0.9,均方根误差均低于0.196,可满足反演精度的要求。本研究提出的基于高分1号影像的粒子群神经网络模型反演玉米和小麦LAI的方法具有一定的普适性。 |
| 关 键 词： | 叶面积指数 粒子群神经网络模型 神经网络模型 植被指数回归模型 |
| 收稿时间： | 2018-12-04 |
Inversion of maize and wheat leaf area index based on particle swarm optimization neural network model |
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| WANG Xiaoxuan,MENG Qingyan,ZHANG Haixiang,WEI Xiangqin,YANG Zenan.Inversion of maize and wheat leaf area index based on particle swarm optimization neural network model[J].Acta Agriculturae Zhejiangensis,2019,31(7):1170. | |
| Authors: | WANG Xiaoxuan MENG Qingyan ZHANG Haixiang WEI Xiangqin YANG Zenan |
| Institution: | 1. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China; 2. Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China; 3. Sanya Institute of Remote Sensing, Sanya 572029, China |
| Abstract: | In this paper, based on GF-1 remote sensing image, three methods, namely particle swarm optimization neural network model, artificial neural network model, vegetation index regression model ,were adopted to invert leaf area index (LAI) of maize and wheat in Langfang City. It was shown that the accuracy of maize and wheat LAI inversion by particle swarm optimization neural network model was the highest. The calculated determination coefficient R2 of this method was higher than 0.9, and its root mean square error was lower than 0.196, which could satisfy the requirement of inversion precision. To sum up, maize and wheat LAI inversion based on the proposed particle swarm optimization neural network model was feasible on GF-1 images, and possessed universality. |
| Keywords: | leaf area index particle swarm optimization neural network model artificial neural network model vegetation index regression model |
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