Forest above ground biomass（AGB） estimation using microwave backscattering coefficient is normally limited to low level AGB because of the "saturation" problem in backscattering coefficient.In addition

forest height may be used to estimate AGB by allometric equation

but the changing conditions of the forest in terms of density

tree species composition etc.limit the accuracy and performance of the method.In order to overcome the above disadvantages and improve the estimation accuracy

a method for AGB estimation is proposed in this paper

which is based on polarization coherence tomography（PCT） technology.Using repeat pass ESAR L-band PolInSAR data collected by DLR at the Traunstein test site

the radar relative refiectivity function of each pixel is reconstructed using PCT

from which the average relative refiectivity profiles for the 20 validation stands are computed.Then 9 profile characteristic parameters closely related to biomass are defined and extracted for each forest stand.The natural logarithms of these 9 profile parameters are taken as independent variables for multivariate linear regression analysis with the natural logarithm of the field-measured AGB as dependent variable using stepwise regression method.Forest AGB estimation model is established and evaluated

and the factors possibly affecting the performance of the AGB estimation model are also analyzed.The results show that these parameters

which are extracted from the average relative refiectivity function inversed with PCT

are sensitive to forest AGB.The accuracy of AGB estimation can be improved if we make full use of the information contained in the relative refiectivity function.