Abstract Polarimetric Synthetic Aperture Radar Interferometry (PolInSAR) has shown potential for the retrieval of a forest canopy height model (CHM) and the underlying solid earth digital terrain model (DTM). However, because of non-volume decorrelation and other unavoidable errors, the robustness of retrieval heights is sensitive to the spatial baseline of the selected InSAR pairs, which relates forest parameters to measured coherence. Within the context of the random volume over ground (RVoG) model and the three-stage inversion method, we aimed to quantify the influence of spatial baseline on the inversions at P-band, which are distinct from the inversions at higher frequency due to the non-negligible ground contributions. This information assists in optimal baseline selection and the development of robust inversion schemes. Assumptions about the extinction coefficient and additional DTM or DEM were used to reduce the influence of ground contribution on CHM and DTM inversion, respectively. Inversions from published airborne repeat-pass P-band PolInSAR data with four different spatial baselines were validated against LiDAR-derived DTM and CHM data. The results show that a longer spatial baseline performed better in DTM inversion. The longest baseline produced the best R2 of 0.995 and RMSE of 0.555 m, much better than the smallest baseline with an R2 of 0.794 and RMSE of 3.74 m. A threshold height could be identified that determines the overestimation and underestimation of CHM inversion due to the non-volume decorrelation. Different baselines produced different threshold heights, making CHM inversion only accurate for a limited range of forest height around the threshold. The optimal baseline produced a CHM with R2 of 0.605 and RMSE of 2.67 m. Additionally, we found that using multiple baselines has the potential to improve CHM inversion, improving the R2 to 0.827 and RMSE to 0.876 m in our study.

中文翻译：

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基于P波段PolInSAR数据的空间基线对森林冠层高度模型和数字地形模型反演的影响

摘要 极化合成孔径雷达干涉测量法 (PolInSAR) 已显示出用于检索森林冠层高度模型 (CHM) 和底层固体地球数字地形模型 (DTM) 的潜力。然而，由于非体积去相关和其他不可避免的错误，检索高度的鲁棒性对所选 InSAR 对的空间基线敏感，这将森林参数与测量的相干性联系起来。在地面随机体积 (RVoG) 模型和三阶段反演方法的背景下，我们旨在量化空间基线对 P 波段反演的影响，由于不可忽视的地面贡献。该信息有助于最佳基线选择和稳健反演方案的开发。关于消光系数和附加 DTM 或 DEM 的假设分别用于减少地面贡献对 CHM 和 DTM 反演的影响。已发布的具有四种不同空间基线的机载重复通过 P 波段 PolInSAR 数据的反演针对 LiDAR 衍生的 DTM 和 CHM 数据进行了验证。结果表明，较长的空间基线在 DTM 反演中表现更好。最长的基线产生了 0.995 的最佳 R2 和 0.555 m 的 RMSE，远好于 R2 为 0.794 和 RMSE 为 3.74 m 的最小基线。由于非体积去相关，可以识别确定 CHM 反演的高估和低估的阈值高度。不同的基线产生不同的阈值高度，使 CHM 反演仅在阈值附近的有限森林高度范围内准确。最佳基线产生了 R2 为 0.605 和 RMSE 为 2.67 m 的 CHM。此外，我们发现使用多个基线有可能改善 CHM 反演，在我们的研究中将 R2 提高到 0.827，将 RMSE 提高到 0.876 m。