Effective monitoring of soil and vegetation properties on a global scale is essential for better understanding climate changes, hydrological dynamics, and ecological processes. Passive microwave remote sensing at C-band radio frequency, with long observation period and relatively high penetration capability, has been widely used to retrieve soil moisture (SM) and vegetation optical depth (C-VOD). The retrieval process is generally achieved by inversion of the τ-ω radiative transfer model, which depends on crucial parameters such as effective scattering albedo (ω) and soil roughness (H) for accurate retrievals. Current SM/C-VOD retrieval algorithms, such as the Land Parameter Retrieval Model (LPRM), predominantly rely on globally-constant ω and H values, ignoring the inherent sensitivity of those parameters to varying soil conditions and vegetation types. To evaluate the impact of ω and H variables on SM and C-VOD retrievals and to improve their accuracy, this study proposed and evaluated a novel retrieval approach from AMSR2 C-band observations during 2017–2020 using the C-band Microwave Emission of the Biosphere (C-MEB) model. We evaluated two new retrieval algorithms, considering either a globally-constant calibration or a land cover-based calibration of ω and H. As a benchmark for the calibration, we optimized the values of ω and H by evaluating the retrieved SM against in situ measurements from the International Soil Moisture Network (ISMN) and OzNet hydrological monitoring networks. The main originality compared to previous algorithms is that i) it includes a comprehensive calibration exploring the optimal values of ω and H, applicable globally or tailored to specific land cover; ii) field SM measurements were leveraged to constrain the calibrated value of ω and H.

中文翻译：

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一种新颖的全球C波段植被光学深度和土壤湿度AMSR2反演算法（AMSR2 IB）：参数标定、评估和相互比较


在全球范围内有效监测土壤和植被特性对于更好地了解气候变化、水文动态和生态过程至关重要。 C波段射频无源微波遥感具有观测周期长、穿透能力强等特点，被广泛应用于土壤水分（SM）和植被光学深度（C-VOD）反演。反演过程通常通过τ-ω辐射传输模型的反演来实现，该模型依赖于有效散射反照率（ω）和土壤粗糙度（H）等关键参数来实现精确反演。当前的 SM/C-VOD 检索算法，例如土地参数检索模型 (LPRM)，主要依赖于全局恒定的 ω 和 H 值，忽略了这些参数对不同土壤条件和植被类型的固有敏感性。为了评估 ω 和 H 变量对 SM 和 C-VOD 反演的影响并提高其准确性，本研究提出并评估了一种利用 2017-2020 年 AMSR2 C 波段观测数据的新型反演方法，该方法使用生物圈（C-MEB）模型。我们评估了两种新的检索算法，考虑全局恒定校准或基于土地覆盖的 ω 和 H 校准。作为校准的基准，我们通过根据原位测量评估检索的 SM 来优化 ω 和 H 的值来自国际土壤湿度网络 (ISMN) 和 OzNet 水文监测网络。与之前的算法相比，主要的独创性在于 i) 它包括探索 ω 和 H 的最佳值的全面校准，适用于全球或针对特定的土地覆盖情况； ii) 利用现场 SM 测量来约束 ω 和 H 的校准值。