The accuracy of hydrological model predictions is limited by uncertainties in model structure and parameterization, and observations used for calibration, validation and model forcing. Conventionally, calibration is performed with discharge estimates. However, the internal processes in the model might be misrepresented, i.e., the model might be getting the “right results for the wrong reasons”, which compromises model reliability. An alternative is to calibrate the model parameters with remote sensing (RS) observations of the water cycle. Previous studies highlighted its potential to improve discharge estimates, but put much less effort on investigating other variables of the water cycle. In this study, we analyzed in detail the contribution of five different RS-based variables (water level (h) from Jason-2, flood extent (A) from ALOS-PALSAR, terrestrial water storage (TWS) anomalies from GRACE, evapotranspiration (ET) from MOD16 and soil moisture (W) from SMOS) to calibrate a hydrological-hydrodynamic model for a tropical study region with floodplains in the Amazon basin. Calibration with TWS, ET, W, and h+W were able to improve discharge estimates by around 16% to 48%. Water cycle representation was also improved (e.g., calibration with h improved not only h estimates but also A, TWS and ET). By analyzing differing calibration setups, a consistent selection of complementary variables for model calibration resulted in better performances than incorporating all RS variables into the calibration. By looking at multiple RS observations of the water cycle, we were able to found inconsistencies in model structure and parameterization, which would remain unknown if only discharge observations were considered.