RESEARCH ON INVERSION MODELS FOR FOREST HEIGHT ESTIMATION USING POLARIMETRIC SAR INTERFEROMETRY

8 citations as recorded by crossref.

1. Retrieval of Boreal Forest Heights Using an Improved Random Volume over Ground (RVoG) Model Based on Repeat-Pass Spaceborne Polarimetric SAR Interferometry: The Case Study of Saihanba, China Y. Mao et al. https://doi.org/10.3390/rs13214306
2. A Method for Forest Canopy Height Inversion Based on Machine Learning and Feature Mining Using UAVSAR H. Luo et al. https://doi.org/10.3390/rs14225849
3. A framework for montane forest canopy height estimation via integrating deep learning and multi-source remote sensing data H. Luo et al. https://doi.org/10.1016/j.jag.2025.104474
4. Correcting Underestimation and Overestimation in PolInSAR Forest Canopy Height Estimation Using Microwave Penetration Depth H. Luo et al. https://doi.org/10.3390/rs14236145
5. High-density forest AGB estimation in tropical forest integrated with PolInSAR multidimensional features and optimized machine learning algorithms H. Luo et al. https://doi.org/10.1016/j.ecolind.2024.111878
6. Multi-Source Data Fusion and Ensemble Learning for Canopy Height Estimation: Application of PolInSAR-Derived Labels in Tropical Forests Y. Li et al. https://doi.org/10.3390/rs17233822
7. Forest height estimation based on the RVoG inversion model and the PolInSAR decomposition technique A. Aghabalaei et al. https://doi.org/10.1080/01431161.2019.1694726
8. An efficient method for estimating tropical forest canopy height from airborne PolInSAR data H. Luo et al. https://doi.org/10.1016/j.ecolind.2024.112566