Objectives The forest height parameters can be inversed by random volume over ground (RVoG) model and single baseline polarimetric interferometric synthetic aperture radar (PolInSAR) technology. But the observation data face significant issues related to a lack of observational information. By incorporating multi-baseline observation data, it is possible to supplement the observational information of model parameters and enhance the accuracy of forest height parameter inversion. However, due to the differences in multi-baseline observation environments, the inversion model can become over-parameterized, leading to ill-posed problems in forest height parameter inversion, which severely restricts the inversion accuracy. Therefore, addressing the ill-posed problem of multi-baseline model parameter inversion is crucial for improving the accuracy and stability of forest height parameter inversion.

Methods A multi-baseline inversion model is constructed based on the theory of least squares estimation to achieve the fusion of multi-baseline observational information for forest height parameter inversion. Subsequently, a regularization method is employed to estimate the model parameters, thereby mitigating the effects of ill-posed problem and enhancing the accuracy of forest height parameter estimation. On this basis, a regularization parameter is selected based on the variance and bias of the forest height parameter estimates as they vary with regularization parameters, effectively improving the stability of forest height parameter estimation.

Results Experiments on forest height inversion using multi-baseline PolInSAR data indicate that root mean square error (RMSE) for forest height inversion using conventional method is of 9.47 m with determination coefficient (R²) of 0.72. In comparison, RMSE for forest height inversion using multi-baseline regularization method is 7.36 m with R² of 0.80. Thus, the proposed method demonstrates 22% reduction in RMSE and the relative improvement of R², effectively enhancing the accuracy and stability of forest height inversion by PolInSAR.

Conclusions The theory of least squares estimation can effectively integrate multi-baseline observational information for forest height parameter inversion, but the influence of ill-posed problem is significant. By combining this with regularization method, the ill-posed impact can be reduced, thereby improving the accuracy and stability of forest height parameter estimation. It constitutes an effective method for PolInSAR multi-baseline integrated forest height inversion.