Investigating the Climatic Drivers of Seasonal Ground Motion through Multisensor SAR Soil Moisture

Seasonal ground deformations are caused by climatic and geophysical factors, including annual temperature fluctuations, elastic lithosphere response, and variations in soil moisture (SM) and groundwater levels. In particular, expansive clayey soils undergo volumetric changes depending on their moisture content, swelling with high SM and shrinking during dry periods. These processes can damage buildings, infrastructures, and hamper slope stability.

This study analyzes the impact of SM and land surface temperature (LST) on seasonal ground deformations in the Po Valley, characterized by clay–rich soils and highly vulnerable to climate-change drought. The analysis covers the period 2020–2023 at 500 m resolution, combining downscaled SMAP SM data, European Ground Motion Service (EMGS) deformation data, and MODIS LST.

SM is downscaled from 9 km to 500 m using an Extreme Gradient Boost model, trained on aggregated Sentinel-1, ALOS, and SAOCOM backscatter data and static variables. The model obtains a R²=0.80 and RMSE=0.012 m³/m³ on the test set, while validation against in-situ measurements shows improved correlation (0.6 vs 0.5) and reduced relative error (6% vs 10%) compared to the original SMAP product.

EGMS displacement time series are averaged within the 500 m cells, and seasonal deformations are extracted using a Loess method and analyzed using correlation and lagged correlation approaches. 33.5% of the samples show seasonal amplitudes greater than 2.5 mm, consistent with swelling–shrinking effects. For about half of the dataset, Spearman correlations with LST are above 0.6, while for SM are weaker (0.3). Time-lag analysis revealed that SM effects peak near zero lag but can persist up to 30 days due to groundwater influence, whereas LST effects are mostly instantaneous or exhibit lags up to 15 days.

Multivariate regression analysis quantifies the independent contributions of SM and LST: 16% of samples has R²≥0.8, indicating that these drivers explain most of the seasonal variability, 38% has 0.5≤R²<0.8, and the 46% shows R²<0.5, suggesting that other factors may be dominant. SM-driven deformations are located in valley areas, while LST-dominated signals are prevalent in mountainous zones.

These results demonstrate the effectiveness of the downscaling approach in improving SM estimation and show that SM and LST jointly explain most seasonal deformations in over half of the analyzed samples. However, SM-driven deformation is detectable in a limited number of samples, and can be masked by thermal expansion. Future work should integrate groundwater and geological data and exclude scatterers with high temperature sensitivity to better isolate SM-induced deformations.

This work was supported by the Università di Modena e Reggio Emilia – Fondazione di Modena Project “Ensembling SATellite monitoring and BIM methods in the SAFety assEssment of road infrastructure (SATSAFE)”, FAR 2024 - Bando per il finanziamento di progetti di ricerca interdisciplinari.