Remote Sensing of Environment 269 (2022) 112802 Available online 21 November 2021 0034-4257/© 2021 Elsevier Inc. All rights reserved. Improving surface soil moisture retrievals through a novel assimilation algorithm to estimate both model and observation errors Jiaxin Tian a, b , Jun Qin c, d, * , Kun Yang e , Long Zhao f , Yingying Chen a, b , Hui Lu e , Xin Li a, b , Jiancheng Shi g a National Tibetan Plateau Data Center, Key Laboratory of Tibetan Environmental Changes and Land Surfaces Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China b University of Chinese Academy of Sciences, Beijing, China c Southern Marine Science and Engineering Guangdong Laboratory, Guangzhou, China d State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China e Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China f Southwest University, Chongqing, China g State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing Normal University, Beijing, China A R T I C L E INFO Editor: Jing M. Chen Keywords: Land data assimilation Land surface model Soil moisture Model error Observation error Parameter optimization ABSTRACT 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 profle 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 specifcation of model parameters. However, it is always challenging to specify these errors and model param- eters. 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 frst 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 simulta- neously 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 fux estimates. 1. Introduction Soil moisture is a critical variable that controls the water, energy, and carbon exchanges between the land surface and the atmosphere. Thus, the accurate estimation of soil moisture is highly desirable for many research felds, such as meteorology, hydrology, ecology, and agronomy (Entekhabi et al., 2010; Mccoll et al., 2017; Reichel and Koster, 2004; Scipal et al., 2008). * Corresponding author. E-mail address: qinjun@igsnrr.ac.cn (J. Qin). Contents lists available at ScienceDirect Remote Sensing of Environment journal homepage: www.elsevier.com/locate/rse https://doi.org/10.1016/j.rse.2021.112802 Received 6 March 2021; Received in revised form 7 November 2021; Accepted 10 November 2021