Forest above-ground biomass (AGB) is the basis of terrestrial carbon storage estimation, and making full use of the seasonal characteristics of remote sensing imagery can improve the estimation accuracy. In this study, we used multi-source time series and sample plots with the Random Forest (RF) model to estimate the AGB. The sources included Sentinel-1 (S-1), Sentinel-2 (S-2), and the S-1 and S-2 combination (S-1S-2). Time series included single season, annual, and multi-season. This study aims to (1) explore the optimal image acquisition season to estimate AGB; (2) determine whether the ability to estimate the AGB of multi-seasonal imagery exceeded that of annual and single-season imagery; (3) discover the sensitivity of different data to AGB according to phenological conditions. The results showed that: (1) images acquired in autumn were more useful for AGB estimation than spring, summer, and winter; (2) the S-1 multi-seasonal AGB model had higher accuracy than the annual or single-season one; (3) in autumn and spring, S-1 had higher estimation accuracy than S-2, and in autumn and spring, estimation accuracy from S-1S-2 was higher than that from S-1 and S-2; (4) in 16 AGB estimation models, the best estimation accuracy was achieved by the autumn AGB model from S-1S-2 (R² = 0.90, RMSE = 16.26 t/ha, p = 0.82, and rRMSE = 18.97). This study could be useful to identify the optimal image acquisition season for AGB estimation, thus reducing the economic cost of image acquisition and improving the estimation accuracy.