Abstract: Objectives: Forest aboveground biomass (AGB) is a crucial indicator for assessing forest ecosystem productivity, and its accurate estimation is vital for effective monitoring and sustainable management of forest resources. Traditional methods for estimating forest AGB using InSAR typically rely on a single forest height feature for AGB estimation, which suffers from poor robustness and limited accuracy. Addressing the aforementioned issues, this study aims to utilize few-look interferometric and local histogram statistics techniques to extract more forest structural parameters, thereby enhancing the estimation accuracy of forest AGB. Methods: This study proposes a novel approach for forest AGB estimation using high-resolution X-band single-pass InSAR data, which integrates the few-look interferometric technique, low-pass filtering, and local histogram statistics. The implementation procedure involves four key steps. Firstly, an initial Digital Surface Model (DSM) is generated via few-look interferometric processing of InSAR data. Secondly, low-pass filtering technology is employed to remove the terrain trend from the initial InSAR DSM, upon which the vertical structure profile of the forest is constructed. Then, multiple features reflecting the vertical and horizontal structural information of the forest are extracted based on the profile. Finally, a forest AGB estimation model is constructed to achieve accurate estimation of forest AGB. Results: The proposed method was experimentally validated using high-resolution airborne X-band single-pass InSAR data. We found that the forest vertical profile constructed from the differential phase heights can effectively characterize the distribution patterns of forest structure. The features extracted from the vertical structure profile exhibit strong correlations with forest biomass (correlation coefficient R > 0.80, P< 0.05). Among these features, DH5 showed the highest correlation with forest AGB, with a correlation coefficient of 0.94. And the estimation accuracy of the proposed method was 6.8% higher than that of traditional InSAR methods based on interferometric phase difference for forest height estimation. Specifically, the model achieved an overall accuracy of 80.98%, with a coefficient of determination (R²) of 0.89 and a root mean square error (RMSE) of 3.15 t/hmR². Conclusions: Based on the few-look interferometric and local height histogram statistical techniques, it is possible to effectively construct the vertical structural profile of forests and extract feature parameters sensitive to forest AGB. Compared with the single height feature estimation method, the multi feature joint estimation method significantly improves the accuracy of forest AGB estimation. Compared with traditional methods based on interference difference to extract forest height and forest AGB, the accuracy has been improved by about 6.8%. The method proposed here provides a new approach and method for accurate estimation of forest AGB in high-resolution interferometric SAR. However, the applicability of this method in different data sources and study areas still needs further research in the future.