Fraternal Twin Experiments for Satellite-Constrained Land Data Assimilation Using Deep Learning Surrogate Models

Recently, advances in deep learning (DL) have enabled the development of various surrogate modeling approaches to emulate traditional land surface models (LSMs), which typically require expensive computational resources. These surrogate models provide computationally efficient alternatives to conventional LSMs. In this study, we develop a DL-based autoregressive surrogate model to predict surface soil moisture (SSM) using meteorological forcing variables and the previous SSM state as inputs.

The developed surrogate model is further employed as a forecast model within a land data assimilation (LDA) framework, replacing the traditional LSMs. Since the true SSM state is unknown in the real-world applications, the fraternal twin experiments are conducted using a synthetic ground truth SSM, which is generated from an LSM nature run. In addition, a synthetic imperfect LSM SSM is generated by applying spatially correlated noise to the synthetic ground truth. Then, the surrogate model is trained to emulate this imperfect LSM simulation. 

Synthetic satellite observations are generated from the synthetic ground truth by introducing controlled observational uncertainties derived from prior studies. These experiments systematically evaluate the sensitivity of LDA performance to satellite observation errors under a wide range of realistic observational scenarios. Therefore, the proposed framework is expected to serve as a computationally efficient and scientifically rigorous testbed for exploring LDA strategies, with potential applications in future satellite mission design and water resource management.