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Understanding how African tropical forests respond to climate and land use change requires dense, reliable time series of vegetation dynamics, yet persistent cloud cover and atmospheric variability strongly limit existing satellite products over the Congo Basin. As a result, key aspects of forest phenology, seasonality, and short term variability remain poorly resolved, constraining our ability to detect early signs of functional change or destabilisation.Here, we develop and evaluate a high quality vegetation time series for Central African forests by combining hyper temporal resolution observations from the MSG SEVIRI geostationary sensor with high spatial resolution Sentinel 2. At a later stage, these results will also be compared with drone data and ground surveys from the CoForFunc international project. To ensure that observed variability reflects changes in vegetation rather than atmospheric fluctuations, we implement a dedicated atmospheric correction for the SEVIRI data adapted from recent developments for Himawari geostationary satellites and further optimised for humid tropical forest conditions. The near continuous sampling of SEVIRI is exploited to reduce cloud related artefacts and improve temporal consistency, while Sentinel 2 and drone observations provide spatial detail and validation at finer scales. The study establishes a robust observational baseline of forest canopy dynamics against which future climate and land use impacts can be more reliably assessed.Initial results indicate that the combined dataset captures vegetation dynamics and seasonal transitions more consistently than commonly used products such as MODIS, revealing phenological patterns that are otherwise obscured by cloud contamination and atmospheric noise. By improving the accuracy of functional signals in one of the world’s most data limited tropical regions, this work provides a critical foundation for assessing carbon dynamics, ecosystem resilience, and potential tipping behaviour in African tropical forests under ongoing environmental change.