An investigation of inversion methodologies to retrieve the leaf area index of corn from C-band SAR data

Abstract Studies on the sensitivity of microwave scattering to vegetation canopies have led the researchers to conclude that crop biophysical parameters can be modeled from Synthetic Aperture Radar (SAR) backscatter. In this study, we assess different methods of modeling the Leaf Area Index (LAI), an important biophysical indicator of crop productivity, from dual-polarized SAR. Particularly, we evaluate the performance of the Water Cloud Model (WCM) to estimate the LAI of corn using VV and VH backscatter derived from RADARSAT-2 and Sentinel-1 satellites over two test sites (Canada and Poland). We tested the performance of four different approaches to invert the WCM. These are: (a) iterative optimization (IO), (b) Look-up table (LUT) search, (c) Support Vector Regression (SVR) and (d) Random Forest Regression (RFR). The accuracy of each inversion was measured by comparing the estimates from the WCM to the LAI of corn measured in-situ. Our results indicated that the inversion of the WCM using the SVR method delivered the best performance, yielding a correlation (r-value) between estimated and measured LAI of 0.92 and a root mean square error (RMSE) of 0.677 m 2 m −2 . The other approaches produced higher errors, with the LUT search resulting in the greatest error (RMSE of 0.977 m 2 m −2 ). This study will be of interest to the agricultural sector as this community works towards developing robust methods for tracking crop productivity from SAR technologies across multiple sites and using data from multiple satellite platforms.