Bridging Observational Gaps in Microwave Satellite Signals Using a Meteorological Foundation Models

Brightness temperature (TB) data acquired from microwave satellite systems constitute a fundamental component of global environmental monitoring and earth system analysis. This data serves as critical variables for understanding our Earth systems and predicting carbon, water and energy fluxes. While these satellite systems offer global-scale observational data comparable to physics-based land surface models, they remain subject to fundamental limitations inherent to Low Earth Orbit satellite missions. In particular, their spatial and temporal sampling is constrained by orbital geometry and revisit cycles, resulting in observational gaps and reduced capability to resolve rapidly evolving hydrometeorological processes. Moreover, the continuity and availability of satellite-derived products are strongly dependent on mission lifetimes and launch schedules, leading to potential discontinuities across different satellite generations. 

This study proposes a new deep learning-based framework to emulate TB data from microwave satellite systems. Recently, foundation models based on the Transformer architecture have been successful in specific downstream tasks. Foundation models provide superior zero-shot or out-of-distribution performance due to their broad pre-training. This has led to an increasing number of studies applying foundation models to various hydrological challenges.

Using available TB data from various microwave satellite systems as the target, the proposed model is trained to learn nonlinear relationships between latent vectors and global-scale TB dynamics. Based on these learned relationships, the model subsequently infers a suite of hydrological variables, including soil moisture and vegetation water content, thereby enabling consistent reconstruction of land surface states across space and time.