With the upcoming spaceborne synthetic aperture radar (SAR) missions (BIOMASS, LuTan-1, NISAR, and TanDEM-L), it will become possible to extract vegetation height at a global scale by utilizing spaceborne low-frequency (L- and P-band) polarimetric synthetic aperture radar interferometry (PolInSAR) data. However, in the context of single-baseline parameter retrieval by the random volume over ground (RVoG) model, three main error sources that affect the inversion accuracy should be carefully considered, i.e., the ground scattering contribution, the spatial baseline configuration, and the temporal decorrelation (the main part of non-volume decorrelation). To make the estimation more reliable, several kinds of multibaseline PolInSAR inversion methods have been proposed over the past few years and have achieved improved inversion performances. Dual-baseline inversion effectively avoids the ambiguity of the ground contribution, whereas the performance is highly dependent on the appropriate combination of two spatial baselines as well as the mitigation of the non-volume decorrelation. In this study, we conducted in-depth research into the effect of these influencing factors on dual-baseline inversion, aiming to provide effective guidance on dual-baseline combination selection among multibaseline data. Accordingly, a novel multibaseline inversion scheme (MBLFPI) suitable for low-frequency PolInSAR data is proposed in this paper. The significant advantage of the new method is that the three aforementioned error sources can be taken into account simultaneously, without relying on external data. The proposed scheme was validated using L- and P-band SAR data acquired by the DLR's E-SAR/F-SAR and ONERA's SETHI systems, as well as corresponding light detection and ranging (LiDAR) data collected during the BioSAR-2008, AfriSAR-2016, and TropiSAR-2009 campaigns. A series of experiments was performed to evaluate the applicability and generalizability of the proposed method. The results showed that this innovative scheme produced forest height maps with a root-mean-square error (RMSE) of 2.37 m (R = 0.88) and 3.13–4.43 m (R = 0.35–0.94) in the L-band and P-band scenarios (boreal and tropical forest), respectively, indicating a significant improvement over the three conventional multibaseline methods and pure dual-baseline inversion. The comprehensive analysis provided in this paper should assist with and provide strong support for SAR system and mission design, and the proposed scheme could be considered a promising way for future spaceborne missions to invert vegetation parameters at a global scale.

中文翻译：

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基于双基线组合有效选择的多基线低频PolInSAR数据改进森林高度测绘


随着即将到来的星载合成孔径雷达（SAR）任务（BIOMASS、LuTan-1、NISAR 和 TanDEM-L），利用星载低频（L-和 P-）提取全球范围内的植被高度将成为可能。带）极化合成孔径雷达干涉测量（PolInSAR）数据。然而，在利用地面随机体积（RVoG）模型进行单基线参数反演时，应仔细考虑影响反演精度的三个主要误差源，即地面散射贡献、空间基线配置和时间去相关（非体积去相关的主要部分）。为了使估计更加可靠，过去几年提出了多种多基线PolInSAR反演方法，并取得了改进的反演性能。双基线反演有效地避免了地面贡献的模糊性，而性能高度依赖于两个空间基线的适当组合以及非体积去相关的缓解。本研究深入研究这些影响因素对双基线反演的影响，旨在为多基线数据中的双基线组合选择提供有效指导。因此，本文提出了一种适用于低频PolInSAR数据的新型多基线反演方案（MBLFPI）。新方法的显着优点是可以同时考虑上述三个误差源，而不依赖于外部数据。 使用 DLR 的 E-SAR/F-SAR 和 ONERA 的 SETHI 系统获取的 L 和 P 波段 SAR 数据以及 BioSAR-2008、AfriSAR 期间收集的相应光探测和测距 (LiDAR) 数据对所提出的方案进行了验证-2016 年和 TropiSAR-2009 活动。进行了一系列实验来评估所提出方法的适用性和普遍性。结果表明，这一创新方案生成的森林高度图在 L 波段和 P 波段均方根误差 (RMSE) 分别为 2.37 m (R = 0.88) 和 3.13–4.43 m (R = 0.35–0.94)。波段情景（北方和热带森林）分别表明比三种传统的多基线方法和纯双基线反演有显着改进。本文提供的综合分析将为SAR系统和任务设计提供帮助和强有力的支持，并且所提出的方案可以被认为是未来星载任务在全球范围内反演植被参数的一种有前途的方法。