Leaf area index estimation from the time-series SAR data using the AIEM-MWCM model

The leaf area index (LAI) inversion accuracy of the water cloud model (WCM) based on the SAR images is low. The main reason is that the WCM model assumes two independent contributions from plant and soil surfaces, without taking into account their interactions. Based on this, this study proposes an efficient LAI estimation method that combines the advanced integral equation model (AIEM) and modified water cloud model (MWCM). Images collected by GaoFen-3 synthetic aperture radar (GF-3 SAR) in the Xiangfu area in the east of Kaifeng City, Henan Province, are processed with a modified SAR-BM3D method and used as test data of the proposed method. The proposed AIEM-MWCM method that combines the AIEM and MWCM models is employed to perform the remote sensing inversion of winter wheat LAI throughout the growth cycle. The results show that the fitting accuracy of winter wheat LAI in the five growth stages achieved by the proposed AIEM-MWCM method inversion is better than that of the Dubois-MWCM model. The R2 value of the proposed method is higher than 0.8, and its root mean squared error (RMSE) is lower than 0.3.