Sugarcane plays an important role in food and energy production in Brazil and worldwide. The large availability of satellite sensors and advanced techniques for processing data have improved the forecasting sugarcane yield on a local and global scale, but more work is needed on exploiting the synergy between remote sensing, meteorological and agronomic data. In this study, we combined such data sources to forecast sugarcane yield using a random forest (RF) algorithm on an extensive area of 50,000 ha, over four years. Images from Landsat satellites were processed to time series of surface reflectance and spectral indices. The approach focused on the development of predictive models which only used data acquired and accessible several months before the harvest. First, three RF models were calibrated with different predictors to forecast the sugarcane yield at harvest: using Landsat satellite images and meteorological data (RF1); agronomic and meteorological data (RF2); a combination of Landsat satellite images, agronomic and meteorological data (RF3). As a comparison, we also tested the influence of including knowledge on the future harvest date in the models RF2 and RF3 (RF4 and RF5). The average values of R2 for RF1, RF2, and RF3 were 0.66, 0.50 and 0.74, respectively. The model with the highest values of R2 (RF3) had a Root Mean Square Error (RMSE) of 9.9 ton ha-1 on yield forecast, approximately 15% of the yield average. Including the harvest date improved the RF2 and RF3 models to reach R2 = 0.69 and RMSE = 10.8 ton ha-1 for RF4, and R2 = 0.76 and RMSE of 9.4 ton ha-1 for RF5. A blind forecasting test for the 2016 yields showed similar prediction than the forecast made by in situ field expertise. This result has the potential to assist management of sugarcane production.