Long-term Soil Moisture Downscaling Based on Diffusion Models: Applicability Assessment of Generative Models for Geospatial Regression Tasks

Soil moisture downscaling is a challenging geospatial regression task that requires accurately capturing complex spatiotemporal relationships across scales. In this study, we conduct a preliminary applicability assessment of denoising diffusion probabilistic models (DDPMs) for continuous-value geospatial regression, exploring the potential of generative modeling frameworks for soil moisture downscaling. The model learns the relationships between coarse-resolution soil moisture observations and multi-source auxiliary features, enabling the generation of high-resolution soil moisture estimates.

During training, the model uses 36 km resolution satellite soil moisture data and conditions on auxiliary variables, including normalized difference vegetation index (NDVI), land surface temperature, surface albedo, precipitation, and digital elevation model (DEM). A conditional embedding strategy is introduced to incorporate temporal information, spatial location information, and in-situ statistics into the diffusion network via feature-wise linear modulation (FiLM), enhancing the model’s ability to capture complex spatiotemporal structures while maintaining stability. During inference, a two-stage “generation–correction” pipeline is employed: high-resolution (1 km) auxiliary features are first used to generate initial predictions through the diffusion model, which are subsequently bias-corrected using in-situ station data.

The applicability assessment combines quantitative and qualitative evaluation. Quantitative metrics include unbiased mean squared error (UMSE), root mean square error (RMSE), mean absolute error (MAE), and R², while qualitative evaluation focuses on spatial pattern consistency and temporal trend representation. Experimental results indicate that the diffusion-based generative model produces reasonable, spatially coherent, high-resolution soil moisture results and successfully captures major temporal variations. These findings demonstrate the applicability of generative frameworks for geospatial regression and their potential as a geospatial regression modeling paradigm, providing a foundation for further refinement and evaluation.