A Modified Model for Estimating Tree Height from PolInSAR with Compensation for Temporal Decorrelation

The RMoG (Random-Motion-over-Ground) model is commonly used to obtain tree height values from PolInSAR images. The RMoG model borrows its structure function from conventional RVoG (Random-Volume-over-Ground) model which is limited for modelling structural variety in canopy layer. This paper extends the RMoG model to improve tree height estimation accuracy by using a Fourier-Legendre polynomial as the structure function. The new model is denoted by the RMoG$_L$ model. The proposed modification makes height estimation less prone to errors by enabling more flexibility in representing the vertical structure of the vegetation layer. We applied the RMoG$_L$ model on airborne P- and L-band PolInSAR images from the Remingstorp test site in southern Sweden. We compared it with the RMoG and the conventional RVoG models using Lidar height map and field data for validation. For P-band, the relative error was equal to 37.5\% for the RVoG model, to 23.7\% for the RMoG model, and to 18.5\% for the RMoG$_L$ model. For L-band it was equal to 30.54\% for the RVoG model, to 20.02\% for the RMoG model, and to 21.63\% for the RMoG$_L$. We concluded that the RMoG$_L$ model estimates tree height more accurately in P-band, while in L-band the RMoG model was equally good. The RMoG$_L$ model is of a great value for future SAR sensors that are more focused than before on tree height and biomass estimation.