基于粒子群神经网络模型反演玉米、小麦叶面积指数

doi:10.3969/j.issn.1004-1524.2019.07.18

浙江农业学报 ›› 2019, Vol. 31 ›› Issue (7): 1170-1176.DOI: 10.3969/j.issn.1004-1524.2019.07.18

基于粒子群神经网络模型反演玉米、小麦叶面积指数

王枭轩^1,2, 孟庆岩^1,3,*, 张海香¹, 魏香琴¹, 杨泽楠²

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

收稿日期:2018-12-04 出版日期:2019-07-25 发布日期:2019-08-07
通讯作者: *孟庆岩,E-mail: mengqy@radi.ac.cn
作者简介:王枭轩(1992—),男,山西阳泉人,硕士研究生,主要从事定量遥感和农业遥感研究。E-mail: 1244377865@qq.com
基金资助:
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)

Inversion of maize and wheat leaf area index based on particle swarm optimization neural network model

WANG Xiaoxuan^1,2, MENG Qingyan^1,3,*, ZHANG Haixiang¹, WEI Xiangqin¹, YANG Zenan²

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

Received:2018-12-04 Online:2019-07-25 Published:2019-08-07

摘要/Abstract

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

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.

Key words: leaf area index, particle swarm optimization neural network model, artificial neural network model, vegetation index regression model

中图分类号:

S51
TP79

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): 1170-1176.

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]. , 2019, 31(7): 1170-1176.

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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

Inversion of maize and wheat leaf area index based on particle swarm optimization neural network model

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