Three-Step Semi-Empirical Radiometric Terrain Correction Approach for PolSAR Data Applied to Forested Areas

In recent decades, most methods proposed for radiometric slope correction involved the backscattering intensity values in synthetic aperture radar (SAR) data. However, these methods are not fully applicable to quad-polarimetric SAR (PolSAR) matrix data. In this paper, we propose a three-step semi-empirical radiometric terrain correction approach for PolSAR forest area data. The three steps of terrain effects correction are: polarisation orientation angle (POA), effective scattering area (ESA), and angular variation effect (AVE) corrections. We propose a novel method to determine adaptively the “n” value in the third step by minimising the correlation coefficient between corrected backscattering coefficients and the local incidence angle; we then constructed the correction coefficients matrix and used it to correct PolSAR matrix data. PALSAR-2 HBQ (L-band, quad-polarisation) data were used to verify the proposed method. After three-step correction, differences between front and back slopes were significantly reduced. Our results indicate that POA, ESA, and AVE corrections are indispensable steps to producing PolSAR data. In the POA correction step, horizontal–vertical (HV) polarisation was maximally influenced by the POA shift. The max deviation of the POA correction was greater than 1 dB for HV polarisation and approximately 0.5 dB for HH/VV polarisation at an intermediate shift angle (±20◦). Based on Light Detection and Ranging (LiDAR)-derived forest aboveground biomass (AGB) data, we analysed the relationship between forest AGB and backscattering coefficient; the correlation was improved following the terrain correction. HV polarisation had the best correlation with forest AGB (R = 0.81) and the correlation improved by approximately 0.3 compared to the uncorrected data.