Abstract Surface soil moisture is a vital variable in the process of energy exchange between the land and atmosphere. Monitoring the surface soil moisture at the local and global scales has become feasible due to the development of microwave remote sensing. With the new development of potential satellite missions, it is important to evaluate existing soil moisture retrieval algorithms, which will greatly contribute to the improvement of current soil moisture products and the development of new methodologies. This paper compared the performance of four well-known soil moisture retrieval algorithms with L-band radiometry at fixed incidence angles, including the single channel algorithm at horizontal polarization (SCA-H), the single channel algorithm at vertical polarization (SCA-V), the dual channel algorithm (DCA), and the land parameter retrieval model (LPRM). The experimental data used for evaluation was from the Soil Moisture Experiment in the Luan River (SMELR), which consists of variable angular ground-based and airborne observations covering a wide range of incidence angles (22.5°–55°) at L-band. The microwave radiative transfer models are set to be consistent to guarantee that the four different algorithms are comparable. The results showed that the retrieval accuracy of the SCA-H and SCA-V is significantly affected by the input vegetation optical depth, and the calibration of vegetation parameters should be considered in the implementation of the SCA-H and SCA-V. The DCA does not rely on the auxiliary vegetation information and can also achieve good performance for both the soil moisture and vegetation optical depth. However, its retrieval requires a penalty on parameter constraints since the input brightness temperatures at horizontal and vertical polarization are correlated. The LPRM has a poor performance at incidence angles less than 30°, as it analytically utilizes the polarization difference in the brightness temperature, which is quite small at lower incidence angles. The accuracy of four soil moisture algorithms achieve their best performances at intermediate incidence angles of 40° to 45°, and is slightly degraded when the incident angles increased to larger than 50°, which is contributed to the increasing vegetation effects and depolarization that leads to an information loss. These findings provide quantitative evidence to help understand the differences in various current soil moisture algorithms and further promote the development of new methodologies for future soil moisture missions.

中文翻译：

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使用 L 波段辐射测量法从可变角度地面和机载观测中反演土壤水分

摘要 表层土壤水分是陆地与大气能量交换过程中的重要变量。由于微波遥感的发展，在局部和全球尺度上监测表层土壤水分已变得可行。随着潜在卫星任务的新发展，对现有土壤水分反演算法进行评估非常重要，这将大大有助于改进现有土壤水分产品和开发新方法。本文比较了四种著名的土壤水分反演算法与固定入射角的 L 波段辐射测量的性能，包括水平极化单通道算法 (SCA-H)、垂直极化单通道算法 (SCA-V) ，双通道算法（DCA），和土地参数检索模型（LPRM）。用于评估的实验数据来自滦河土壤水分实验 (SMELR)，该实验由可变角度的陆基和机载观测数据组成，覆盖 L 波段的大范围入射角 (22.5°–55°)。微波辐射传输模型设置为一致，以保证四种不同的算法具有可比性。结果表明，SCA-H和SCA-V的反演精度受输入植被光学深度的影响显着，在SCA-H和SCA-V的实施中应考虑植被参数的标定。DCA 不依赖于辅助植被信息，在土壤水分和植被光学深度方面也能取得良好的性能。然而，它的检索需要对参数约束进行惩罚，因为水平和垂直极化的输入亮度温度是相关的。LPRM 在入射角小于 30° 时性能较差，因为它在分析上利用了亮度温度中的偏振差异，在较低的入射角下该差异非常小。四种土壤水分算法的精度在40°到45°的中间入射角下达到最佳性能，当入射角增加到大于50°时略有下降，这有助于增加植被效应和去极化，导致信息丢失。