Savannas， characterized by their low vegetation density and substantial total aboveground biomass， represent a critical region for global carbon cycle. Nevertheless， significant spatial heterogeneity exists within these ecosystems， leading to considerable uncertainty in remote sensing biomass estimations. The Global Ecosystem Dynamics Investigation （GEDI） provides high-quality estimates of above-ground biomass within its observed footprint by leveraging three-dimensional surface vegetation information from LiDAR. However， it lacks spatial continuous above-ground biomass data. Sentinel-2， PALSAR-2 and tree cover data were used to extract 28 features， and a random forest model was established with GEDI footprint level above-ground biomass data to build a high-resolution above-ground biomass estimation method for African savanna. The results show that the algorithm can generate spatially continuous above-ground biomass data in study area， and effectively extract tree information in non-forested areas that are often ignored in previous studies. The mean absolute error and root-mean-square error of the model are 15.798 Mg/ha and 24.626 Mg/ha， respectively. The accuracy remains consistent when using optical images from different seasons. When modeling with optical data acquired during rainy season， spectral bands such as red， red edge， and short-wave infrared， along with their relative spectral indices， play crucial roles. In contrast， when using dry season optical data， tree cover and InSAR become significantly more important. When conducting large-scale biomass estimation of African savannas， the use of multiple data sources can help to obtain better estimation accuracy. This study provides a method for low-cost monitoring of aboveground biomass in Savannas in the future， and contributes to the in-depth study of vegetation carbon cycle in this region.