An Improvement of Vegetation Height Estimation Using Multi-baseline Polarimetric Interferometric SAR Data

This paper proposes a method for improving the estimation accuracy of vegetation height using multi-baseline Polarimetric Interferometric Synthetic Aperture Radar (Pol-InSAR) data. Single-baseline Pol-InSAR technique has been applied to retrieve the vegetation parameters based on the random volume over ground (RVoG) model. There are two main error sources which might decrease the estimation accuracy. One is the non-volumetric decorrelation, such as thermal noise decorrelation, temporal decorrelation, etc. The other is the ground ambiguity and ideal assumption that volume-only coherence can be acquired in at least one polarization. This assumption may fail when vegetation is thick, dense, or the penetration of electromagnetic wave is weak. This paper proposes a method to solve both the abovementioned two problems at the same time based on the use of multi-baseline Pol-InSAR data. Firstly, the two main error sources are analyzed and an inversion model for representing them is constructed based on the RVoG model. With the constructed model, inversion procedure for estimating vegetation height using the multi-baseline Pol-InSAR data is presented. The performance of this new method is validated using simulated data, and the ratio between baselines and their effects on the estimation performance are also presented.