Building blocks for semiempirical models for forest parameter extraction from interferometric X-band SAR images

In this work we provide basic building blocks for semi-empirical models to be applied mainly for forest height extraction from X-band interferometric SAR images. The work uses Random Volume over Ground model as the main theoretical framework, and relies on the measurement data represented by over 3000 measurements points collected in Estonia in 2011 and 2012. Here we demonstrate that the best argument for empirical models which relate coherence and forest parameters is relative interferometric tree height (tree height divided by InSAR Height of ambiguity). Our results suggest that a very simple linear model with no additional a priori parameters can be used as a first approach for estimation of forest height. However, if more extensive dataset are available, a zero extinction model can provide improvement. Moreover, proposed semi-empirical models can also be used to predict forest properties related to forest extinction coefficient. All the derived model approximations are demonstrated by model simulations and verified with extensive dataset of forest measurements. Relation of semi-empirical parameters to physics based model parameters is discussed and the models accuracy is analyzed based on empirical dataset.