Winter wheat leaf area index inversion by the genetic algorithms neural network model based on SAR data

Abstract

The leaf area index (LAI) is an important agroecological physiological parameter affecting vegetation growth. To apply the genetic algorithms neural network model (GANNM) to the remote sensing inversion of winter wheat LAI throughout the growth cycle and based on GaoFen-3 Synthetic aperture radar (GF-3 SAR) images and GaoFen-1 Wide Field of View (GF-1 WFV) images, the Xiangfu District in the east of Kaifeng City, Henan Province, was selected as the testing region. Winter wheat LAI data from five growth stages were combined, and optical and microwave polarization decomposition vegetation index models were used. The backscattering coefficient was extracted by modified water cloud model (MWCM), and the LAI was obtained by MWCM inversion as input factors to construct GANNM to invert LAI. The root mean square error (RMSE) and determination coefficient (R2) were used as evaluation indicators of the model. The fitting accuracy of winter wheat LAI in five growth stages by GANNM inversion was better than that of the BP neural network model; the R2 was higher than 0.8, and RMSE was lower than 0.3, indicating that the model could accurately invert the growth status of winter wheat in five growth stages .

Keywords

• leaf area index (lai)
• gf-3
• bp neural network model (bpnnm)
• genetic algorithms neural network model
• winter wheat