Alternating Polarization Data Research Articles

First Results of Estimating Surface Soil Moisture in the Vegetated Areas Using ASAR and Hyperion Data: The Chinese Heihe River Basin Case Study

This study introduces a new approach to estimate surface soil moisture in vegetated areas using Synthetic Aperture Radar (SAR) and hyperspectral data. To achieve this, the Michigan Microwave Canopy Scattering (MIMICS) model was initially used to simulate backscatter from vegetated surfaces containing various canopy water contents, across three frequency bands (i.e., L, S, and C). Using this simulated dataset, the influence of the canopy water content on the backscattered signals was further analyzed. In addition, we developed a modified Water-Cloud model which adds in the crown-ground interaction term. Finally, a soil moisture retrieval model for an agricultural region was developed. Alternating polarization data with ASAR and Hyperion hyperspectral data were used to retrieve soil moisture and validate the feasibility of the retrieval model. The field measured data from the Heihe river basin was used to confirm the proposed model. Results revealed an average absolute deviation (AAD) and average absolute relative deviation (AARD) of 0.051 cm3∙cm−3 and 19.7%, respectively, between the estimated soil moisture and the field measurements.

A semi‐empirical backscattering model for estimation of leaf area index (LAI) of rice in southern China

Most paddy rice in southern China grows in warm, humid and rainy areas where it is hard to acquire optical remote sensing data. In this study, a semi‐empirical backscattering model was proposed to estimate leaf area index (LAI) of rice in the area using ENVISAT Advanced Synthetic Aperture Radar (ASAR) alternating polarization data. Ground measurements of LAI, water content and height of rice in the test site were collected and the model fitted at the same time as the acquisition of ASAR data. LAI estimated from the model was compared with ground measurements to evaluate the accuracy of the model. The results showed that the model provides a promising alternative to optical remote sensing data for predicting LAI of rice in southern China.