The sensitivity of RADARSAT-2 polarimetric SAR data to corn and soybean leaf area index

In this study, quadrature-polarization (quad-pol) RADARSAT-2 data at steep (25o) and shallow (40o) incidence angles were acquired during the 2008 season, imaging 13 corn and soybean fields. The leaf area index (LAI) was derived from optical imagery, and volumetric soil moisture was measured coincident with each overpass. Many synthetic aperture radar (SAR) parameters were significantly correlated with derived corn and soybean LAI. The highest correlations were observed for parameters sensitive to volume scattering (HV, LL, and RR backscatter, pedestal height, and the Freeman–Durden volume-scattering parameter) at the steeper angle. For corn, the minimum correlation coefficient was 0.95. For soybeans, the coefficients were between 0.83 and 0.86. Sensitivity to LAI was lost late in the season, when the derived LAI exceeded 3.0 m2m−2. The derived LAI and the measured soil moisture were used to model several radar parameters (HV backscatter, pedestal height, and the Freeman–Durden volume-scattering parameter) using the water-cloud model. Early in the season, the SAR response was primarily affected by the vegetation, but soil moisture was also an important contributor. When the derived LAI exceeded 1, soil-moisture contributions became minimal. The water-cloud model adequately simulated SAR responses as the canopy developed and LAI increased, demonstrating the potential of polarimetric SAR data for monitoring indicators of crop productivity.