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

doi:10.3969/j.issn.1004-1524.2019.07.18

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

• Biosystems Engineering • Previous Articles Next Articles

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

Abstract

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

CLC Number:

S51
TP79

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

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