Impact of Backscatter in Pol-InSAR Forest Height Retrieval Based on the Multimodel Random Forest Algorithm

For forest with complex structure, the vertical structure backscatter is influenced by a combination of factors, including the frequencies of the radar waves and the forest biophysical parameters (i.e., density, species). The backscatter-induced error is thus a critical element in limiting the accuracy of polarimetric synthetic aperture radar interferometry (Pol-InSAR) forest height inversion based on a single model. It is, therefore, necessary to select the optimal backscatter profile from the multiple possible solutions in each pixel of the test site. In this letter, the impacts of backscatter in forest height estimation based on the models of random volume over ground (RVoG) ( $\sigma >0$ ), RVoG ( $\sigma ), and Gaussian vertical backscatter (GVB) were investigated in the complex plane, and then with the combined use of Pol-InSAR and light detection and ranging (LiDAR), a random forest (RF) classifier is trained to obtain the optimal backscatter function and Pol-InSAR forest height from the results based on the different models in each pixel. The proposed method was tested with single- and multi baseline Pol-InSAR data in the P-band, and the root-mean-square errors (RMSEs) of the proposed approach were 2.85 and 2.69 m, respectively, which represented average improvements of 20.6% and 17.7% over the optimal single-model inversion.