Proceedings Article | 21 August 2009
Proc. SPIE. 7454, Remote Sensing and Modeling of Ecosystems for Sustainability VI
KEYWORDS: Radar, Statistical analysis, Data modeling, Polarization, Backscatter, Scattering, Synthetic aperture radar, Remote sensing, Polarimetry, Data acquisition
The object of this paper is to investigate the relationship between polarimetric SAR information and LAI. RADARSAT-
2 Fine Quad-pol SLC data with shallower and steeper incidence angles were programmed throughout the 2008 growing
season. Optical data were acquired using a hyperspectral CASI airborne sensor as well as the SPOT-4 multi-spectral
satellite. The optical data were used to generate LAI map for the entire study site. Backscatter coefficients, ratios of
backscatter intensity, three polarimeric variables and three Cloude-pottier Decomposition parameters were extracted
from the polarimetric data set. Temporal variations of the backscatter coefficient were analyzed. The results show an
increase in backscatter with corn and soybean growth. The statistical analysis quantified the relationship between the
radar parameters and LAI revealing a strong sensitivity for some radar configurations. For both corn and soybean,
RADARSAT-2 cross-polarization (HV) backscatter at either shallow or steep incidence angles was well correlated with
LAI. To avoid sensitivity to sensor calibration and changing target moisture conditions, ratios of backscatter intensity,
polarimetric variables and Cloude-pottier Decomposition parameters were investigated. For corn, the ratio of HV/HH
and HV/VV as well as pedestal height, total power, correlation coefficient, Entropy and alpha angle were highly
correlation with LAI at steeper incidence angle. For soybean, the higher correlations were found with the ratio of HV/HH
as well as pedestal height, total power, Entropy and alpha angle at shallow incidence angle. In general, the best results
were observed for corn using the FQ6 acquisition. For soybean, the FQ20 data provided the most promising results.
