Interferometric synthetic aperture radar (InSAR) is an important resource for rapidly obtaining forest height information. The coherence amplitude method relies solely on interferometric coherence amplitude information for forest height inversion; however, interferometric data are commonly affected by decorrelation factors including time, baseline, and noise decorrelation, as well as observation geometry errors, leading to errors in estimated forest height. Thus, in this paper, we propose a method to improve the accuracy of forest height estimation by correcting the decorrelation and vertical wave number using a priori forest height knowledge. UAVSAR-L cross-polarization channel HV data from the AfriSAR project are used as interferometric data to invert the forest height using the coherence amplitude method, and the relative height variable RH100 from land, vegetation, and ice sensor light detection and ranging (LiDAR) is used for validation. We optimize the coherence amplitude method by iteratively setting different steps for the nonvolume decorrelation (γd) and the correction parameter (τ) for the vertical wavenumber (kz). The optimal compensation parameter is identified when the root mean square error (RMSE) between the inversion height and LiDAR height is minimized, and the stability of the returned parameter is evaluated through an independent validation sample. Our results indicate that enhancing the coherence amplitude method using a semiempirical iterative approach can effectively improve inversion accuracy. In the validation results, all compensation schemes exhibit a significant improvement in the inversion results compared with those without parameter compensation. The R2 increases by 0.13 and the RMSE decreases by 9.88 m when compensating only γd, whereas the R2 value does not change when only compensating kz, but the RMSE decreases by 19.24 m. When compensating for both γd and kz, the R2 increases by 0.08, and the RMSE decreases by 19.73 m. This changing pattern is consistent with that recorded in the training sample, indicating that our proposed parameter compensation scheme for the coherence amplitude method is effective. With the widespread usage of satellite data, such as ALOS-2 and SAOCOM, as well as the future TanDEM-L and BIOMASS satellites and NISAR program, the combination of ICESat-2 and GEDI forest height data to compensate and optimize inversion results and model parameters is expected to greatly improve the efficiency of forest resource monitoring in the future.

中文翻译：

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基于InSAR的半经验补偿最优相干幅度法反演森林高度

干涉合成孔径雷达（InSAR）是快速获取森林高度信息的重要资源。相干幅度法仅依靠干涉相干幅度信息进行森林高度反演；然而，干涉测量数据通常受到去相关因素的影响，包括时间、基线和噪声去相关，以及观测几何误差，导致估计的森林高度误差。因此，在本文中，我们提出了一种通过使用先验森林高度知识校正去相关和垂直波数来提高森林高度估计精度的方法。来自 AfriSAR 项目的 UAVSAR-L 交叉极化通道 HV 数据用作干涉测量数据，使用相干幅度方法反演森林高度，并且使用来自陆地、植被和冰传感器光探测和测距（LiDAR）的相对高度变量 RH100 进行验证。我们通过迭代设置非体积去相关 (γd) 的不同步骤和垂直波数 (kz) 的校正参数 (τ) 来优化相干幅度方法。当反演高度和激光雷达高度之间的均方根误差（RMSE）最小时，确定最佳补偿参数，并通过独立验证样本评估返回参数的稳定性。我们的结果表明，使用半经验迭代方法增强相干幅度方法可以有效地提高反演精度。在验证结果中，与没有参数补偿的方案相比，所有补偿方案的反演结果都有显着改善。仅补偿 γd 时 R2 增加 0.13，RMSE 减少 9.88 m，而仅补偿 kz 时 R2 值不变，但 RMSE 减少 19.24 m。当同时补偿 γd 和 kz 时，R2 增加 0.08，RMSE 减少 19.73 m。这种变化模式与训练样本中记录的一致，表明我们提出的相干幅度方法的参数补偿方案是有效的。随着卫星数据的广泛使用，例如 ALOS-2 和 SAOCOM，以及未来的 TanDEM-L 和 BIOMASS 卫星和 NISAR 计划，