A Dense Medium Microwave Backscattering Model for the Remote Sensing of Oil Palm

Abstract

Very little work has been carried out to examine the potential of synthetic aperture radar (SAR) imaging data for growth monitoring, disease monitoring, and yield prediction of oil palm plantations. In this paper, we present a scattering model for oil palm based on the radiative transfer equations, solved iteratively up to the second order, to better understand the backscattering behavior of oil palm canopies for SAR image interpretation. The oil palm canopy is modeled as a multilayer background host medium embedded with discrete scatterers of different shapes, with elliptic disks representing the leaves and cylinders representing the fronds and trunks. We consider the effects of coherent scattering through the dense medium phase and amplitude correction theory, as well as the near-field interactions between closely spaced scatterers through the Fresnel phase and amplitude corrections. Ground-truth data are used as input parameters for the model. The model is used to calculate the backscattering coefficients of oil palm canopies at different stages of growth and for different polarizations, frequencies, and incident angles. Comparisons between model calculations and C-band scatterometer measurements of multipolarization backscattering coefficients of 4-year-old oil palm canopies in Bangi, Malaysia show good agreement. Our model also predicts that SAR images at L-band are more sensitive to changes in the structure of the canopies, particularly that of the fronds, so will be the best frequency for oil palm growth monitoring and disease detection.