Soil moisture controls the land surface water and energy budget and plays a crucial role in land surface processes. Based on certain mathematical rules, data assimilation can merge satellite observations and land surface models, and produce spatiotemporally continuous profile soil moisture. The two mainstream assimilation algorithms (variational-based and sequential-based) both need model error and observation error estimates, which greatly impact the assimilation results. Moreover, the performance of land data assimilation relies heavily on the specification of model parameters. However, it is always challenging to specify these errors and model parameters. In this study, a dual-cycle assimilation algorithm was proposed for addressing the above issue. In the inner cycle, the Ensemble Kalman Filter (EnKF) is run with parameters of both model and observation operators and their errors, which are provided by the outer cycle. Both the analyzed state variable and the innovation are reserved at each analysis moment. In the outer cycle, the innovation time series kept by the inner cycle are fed into a likelihood function to adjust the values of parameters of both the model and observation operators and their errors through an optimization algorithm. A series of assimilation experiments were first performed based on the Lorenz-63 model. The results illustrate that the performance of the dual-cycle algorithm substantially surpasses those of both the classical parameter calibration and the standard EnKF. Subsequently, the Advanced Microwave Scanning Radiometer of earth Observing System (AMSR-E) brightness temperatures were assimilated into the simple biosphere model scheme version 2 (SiB2) with a radiative transfer model as the observation operator in two experimental areas, namely Naqu on the Tibetan Plateau and a Coordinate Enhanced Observing (CEOP) reference site in Mongolia. The results indicate that the dual-cycle assimilation algorithm can simultaneously estimate model parameters, observation operator parameters, model error, and observation error, thus improving surface soil moisture estimation in comparison with other assimilation algorithms. Since the dual-cycle assimilation algorithm can estimate the observation errors, it provides the potential for assimilating multi-source remote sensing data to generate physically consistent land surface state and flux estimates.

土壤水分控制着地表水和能量收支，在地表过程中起着至关重要的作用。基于一定的数学规律，数据同化可以将卫星观测和地表模型相结合，产生时空连续的剖面土壤水分。两种主流的同化算法（基于变分的和基于序列的）都需要模型误差和观测误差的估计，这对同化结果影响很大。此外，土地数据同化的性能在很大程度上依赖于模型参数的规范。然而，指定这些误差和模型参数总是具有挑战性。本研究针对上述问题提出了一种双周期同化算法。在内部循环中，集成卡尔曼滤波器 (EnKF) 使用模型和观测算子的参数及其误差运行，这些参数由外循环提供。在每个分析时刻都保留了被分析的状态变量和创新。在外循环中，将内循环保存的创新时间序列输入似然函数，通过优化算法调整模型和观测算子的参数值​​及其误差。首先基于 Lorenz-63 模型进行了一系列同化实验。结果表明，双循环算法的性能大大超过了经典参数校准和标准 EnKF 的性能。随后，地球观测系统先进微波扫描辐射计（AMSR-E）亮温同化到简单生物圈模型方案第2版（SiB2）中，以辐射传递模型为观测算子，分别在青藏高原那曲和位于蒙古的坐标增强观测 (CEOP) 参考站点。结果表明，双循环同化算法可以同时估计模型参数、观测算子参数、模型误差和观测误差，与其他同化算法相比，提高了地表土壤水分估计。由于双周期同化算法可以估计观测误差，