In semi-arid regions such as the Sahel surface soil moisture (SSM) strongly influences the partitioning between sensible and latent heat flux, with SSM anomalies on scales larger than 10 km favoring convection initiation. Mesoscale Convective Systems (MCSs) can be triggered as a result, which numerical weather prediction cannot accurately forecast with current available observational capabilities. This study investigates the sensitivity of MCS forecasting in the Sahel to synthetic sub-daily soil moisture observations using the Weather Research and Forecasting (WRF) model, in the context of the Sub-daily Land-Atmosphere INTEractions (SLAINTE) satellite mission concept. A high-resolution idealized case study was performed to assess the impact of SSM perturbations and data assimilation at different observation spatial resolutions (1 km, 5 km, 12 km) and observation times. Results indicate that synthetic observations three times per day at 5 km resolution would improve forecasting of precipitation intensity and timing. Finer resolution observations could improve forecasts only if observation noise is reduced, while 12 km resolution observations –representing the Advanced SCATterometer (ASCAT) satellite– tend to further disrupt them. The experiments highlight that at least one observation before 12:00 and an observation at 18:00 local time are necessary to constrain the forecasts sufficiently. These findings provide insight into the role of soil moisture observations for convective forecasting in semi-arid regions and contribute to defining requirements for future satellite missions such as SLAINTE.