Fusion of Optical and SAR Data Using Three Approaches for the Estimation of LAI With Modified Integral Equation Model

This research article presents a comprehensive investigation of leaf area index (LAI) estimation using Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 Optical L2A datasets for the wheat crop. The water cloud model (WCM) and PROSAIL radiative transfer models (RTMs) are used to estimate LAI from SAR and optical data, respectively. To model the surface backscattering in WCM, the integral equation model (IEM) at VV and VH polarizations is used with the Gaussian correlation function. The results demonstrate that LAI derived from SAR at VH polarization ( $R^{2}=0.72, \ \text {RMSE}= 0.60~\text {m}^{2}\text {m}^{-2}$ ) exhibits superior accuracy compared with optical LAI ( $R^{2}=0.70,\ \text {RMSE}=0.82~\text {m}^{2}\text {m}^{-2}$ ). A fusion approach incorporating deep learning, principal component analysis (PCA), and nonlinear regression techniques is applied to fuse the SAR and optical datasets to further enhance LAI estimation accuracy. The accuracy of these estimations is tested against the ground-truth LAI taken at different locations. Among the fusion methods tested, deep learning emerges as the most effective and accurate approach ( $R^{2}=0.91,\ \text {RMSE}= 0.38~ \text {m}^{2}\text {m}^{-2}$ ). This study provides valuable insights into the estimation of LAI using multisource remote sensing data and highlights the potential of deep learning for improved accuracy in fusion applications.