Title :
Validation of the AIEM Through Correlation Length Parameterization at Field Scale Using Radar Imagery in a Semi-Arid Environment
Author :
Lu Dong ; Baghdadi, N. ; Ludwig, R.
Author_Institution :
Dept. of Geogr., Ludwig-Maximilians-Univ. Muenchen, Munich, Germany
Abstract :
This letter aimed to validate the advanced integral equation model (AIEM) through different correlation length parameterizations using radar imagery for field-scale studies in a semi-arid environment. This letter compared backscattering coefficients simulated from the AIEM and retrieved from the synthetic aperture radar imagery of a study site in Sardinia. Two treatments for the correlation length were adopted, i.e., in situ measurements and empirically based correlation length estimation. The results showed an overestimation of backscattering coefficients of 2.5 dB with a root mean square error (RMSE) of 3.1 dB for HH and VV polarizations and an underestimation of 27.7 dB and an RMSE of 31.0 dB for HV polarization from the AIEM parameterized by in situ measurements. When using the AIEM with an empirical correlation length, a bias of less than 1.0 dB was found with an RMSE of 1.7 dB for HH and VV polarizations and an overestimation of 1.1 dB and an RMSE of 5.1 dB for HV polarization. Better results were obtained with surface soil moisture (SSM) measured at 10 cm than at 5 cm. Promising soil moisture data retrieval from the SAR imagery is expected from using the empirical correlation length-parameterized AIEM for field-scale purposes in semi-arid environments.
Keywords :
backscatter; data acquisition; hydrological techniques; integral equations; moisture measurement; radar imaging; radar polarimetry; remote sensing by radar; soil; synthetic aperture radar; AIEM validation; HH polarization; HV polarization; Italy; RMSE; SAR imagery; SSM measurement; Sardinia; VV polarization; advanced integral equation model; backscattering coefficient overestimation; backscattering coefficient simulation; correlation length parameterization; empirical correlation length; empirically based correlation length estimation; field-scale study; in situ measurement; radar remote sensing; root mean square error; semiarid environment; soil moisture data retrieval; surface soil moisture; synthetic aperture radar imagery; Backscatter; Correlation; Mathematical model; Remote sensing; Rough surfaces; Synthetic aperture radar; Advanced integral equation model (AIEM); C-band; SAR images; correlation length; semi-arid environment; soil moisture;
Journal_Title :
Geoscience and Remote Sensing Letters, IEEE
DOI :
10.1109/LGRS.2012.2209626