Soil organic carbon (SOC) underpins soil fertility, climate regulation, and ecosystem resilience. Yet high-resolution SOC data remain scarce in many African agroecosystems. In bimodal rainfall systems, phenological phases may imprint distinct spectral signals associated with SOC processes. However, these temporal patterns are rarely exploited in digital soil mapping. We hypothesise that SOC spatial variability is most detectable when vegetation–soil coupling is strongest and that geographically weighted machine learning enhances the ecological interpretability of SOC models in data-scarce settings. To test these hypotheses, we develop a spatially explicit, seasonally-informed framework for SOC prediction in bimodal-rainfall agroecosystems. Using environmental covariates and 11-year seasonal Landsat 7 and 8 composites to capture the full phenological cycle in Tanzania (March–May 'Long Rains', June–August 'dry season', October–December 'Short Rains'), we compare global Random Forest (RF) with Geographical Random Forest (GRF) across each seasonal window, using an annual composite as our climatological baseline.</p> <p>Feature selection using Boruta reduced the covariate set by 34–50 % per temporal window without compromising accuracy. Season-specific GRF models achieved R² values of 0.292–0.405 based on spatial cross-validation, whereas random cross-validation inflated performance to 0.444–0.471 (average overestimation = 19 %). October–December covariates provided the highest predictive power using only 13 predictors. Across the temporal windows, near-infrared reflectance, green band (October–December), red band (March–May), and topsoil clay emerged as the most informative predictors. SHapley Additive exPlanations (SHAP) values extracted from the global RF were represented spatially to map the directional influence of these predictors on global predictions and subsequently assessed alongside local feature importance maps to evaluate the extent of spatial non-stationarity. This two-stage interpretability approach confirmed that the GRF models adapted dynamically to localised eco-pedological realities, such as soil texture-driven storage capacity and canopy-driven phenological masking. By demonstrating when, where and how SOC is most observable, the framework provides an operational blueprint for local organisations to design leaner, seasonally targeted soil surveys and remote sensing protocols that strengthen climate smart land management policies and national carbon accounting systems.