Abstract This paper investigates the soil moisture retrievals over Tibetan Plateau using multi-temporal ALOS2-PALSAR2 data acquired in ScanSAR mode, in contrary to the commonly used StripMap SAR imaging mode. Considering the dual-polarimetry with limited observables, the surface roughness parameters such as the RMS height and auto-correlation length are first estimated by optimizing the semi-empirical Oh2004 model applied to the ALOS2 data acquired under bare soil condition. The vegetation water content corresponding to each ALOS2 acquisition time is derived from an empirical model applied to the temporally interpolated MODIS NDVI data. Then, the obtained roughness and vegetation optical depth are substituted into the Water Cloud Model which we modified by adding a double-bounce component according to the L-band scattering processes to retrieve the effective scattering albedo and soil moisture. The results show that the optimized surface RMS height and the associated slope are negatively related to a dual-angular radar index ΔHH, indicating the feasibility to optimize the relatively stable surface roughness parameters before retrieving the soil moisture dynamics. The obtained vegetation optical depth which is cross-validated against a normalized cross-polarized radar descriptor indicates a significant increase from May to August, in response to the vegetation phenological growth. Furthermore, the time-variable scattering albedo is less than 0.08, and slightly increases with the vegetation development. By accounting for the double-bounce component, the retrieved soil moisture better agrees with the ground measurements with R = 0.89 and RMSE = 0.058 m3/m3, but exhibits an overestimation issue for the soil moisture higher than 0.35 m3/m3 due to the saturation of SAR signal and the relatively strong vegetation cover. A positive correspondence (R = 0.81) between the retrieved soil moisture and the interpolated NDVI was found, verifying their close coupling in the soil-vegetation system. This study deepens the insights in the potential integration between the optical and L-band microwave observations to retrieve soil moisture over vegetated area.

中文翻译：

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利用ALOS2-ScanSAR和MODIS协同反演青藏高原土壤水分

摘要 与常用的StripMap SAR成像模式不同，本文利用ScanSAR模式下采集的多时相ALOS2-PALSAR2数据研究了青藏高原土壤水分反演。考虑到双偏振测量的可观测性有限，首先通过优化应用于裸土条件下获得的 ALOS2 数据的半经验 Oh2004 模型来估计表面粗糙度参数，例如 RMS 高度和自相关长度。对应于每个 ALOS2 采集时间的植被含水量来自应用于时间插值 MODIS NDVI 数据的经验模型。然后，将获得的粗糙度和植被光学深度代入水云模型中，我们根据 L 波段散射过程通过添加双反弹分量来修改水云模型，以检索有效散射反照率和土壤水分。结果表明，优化后的地表均方根高度和相关坡度与双角雷达指数 ΔHH 呈负相关，表明在反演土壤水分动力学之前优化相对稳定的地表粗糙度参数是可行的。获得的植被光学深度与归一化的交叉极化雷达描述符进行交叉验证，表明从 5 月到 8 月显着增加，以响应植被物候增长。此外，时变散射反照率小于 0.08，并随着植被的发育而略有增加。通过考虑双反弹分量，反演的土壤水分与地面测量结果更吻合，R = 0.89 和 RMSE = 0.058 m3/m3，但由于饱和度高于 0.35 m3/m3 的土壤水分存在高估问题SAR 信号和相对较强的植被覆盖。发现检索到的土壤水分与内插 NDVI 之间存在正对应关系 (R = 0.81)，验证了它们在土壤-植被系统中的紧密耦合。这项研究加深了对光学和 L 波段微波观测之间潜在整合的见解，以恢复植被区域的土壤水分。89 和 RMSE = 0.058 m3/m3，但由于 SAR 信号的饱和和相对较强的植被覆盖，土壤水分高于 0.35 m3/m3 存在高估问题。发现检索到的土壤水分与内插 NDVI 之间存在正对应关系 (R = 0.81)，验证了它们在土壤-植被系统中的紧密耦合。这项研究加深了对光学和 L 波段微波观测之间潜在整合的见解，以恢复植被区域的土壤水分。89 和 RMSE = 0.058 m3/m3，但由于 SAR 信号的饱和和相对较强的植被覆盖，土壤水分高于 0.35 m3/m3 存在高估问题。发现检索到的土壤水分与内插 NDVI 之间存在正对应关系 (R = 0.81)，验证了它们在土壤-植被系统中的紧密耦合。这项研究加深了对光学和 L 波段微波观测之间潜在整合的见解，以恢复植被区域的土壤水分。验证它们在土壤-植被系统中的紧密耦合。这项研究加深了对光学和 L 波段微波观测之间潜在整合的见解，以恢复植被区域的土壤水分。验证它们在土壤-植被系统中的紧密耦合。这项研究加深了对光学和 L 波段微波观测之间潜在整合的见解，以恢复植被区域的土壤水分。