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Space-based observations, emerged in the hydrology field in the last two decades, play a fundamental role in providing alternative information of hydrologic variables besides gauge measurements, especially in data scarce regions. Among all satellite products, products derived from Moderate Resolution Imaging Spectroradiometer (MODIS) Satellite are popular due to the satellite's rapid re-visit time and adequate spatial resolutions. However, cloud obscuration limits the usage of products derived from MODIS because clouds block satellites from capturing the ground state of the earth surface. This dissertation aims to (1) recover two cloud-free MODIS datasets of snow and flood using a 3-D interpolation technique, namely, Variational Interpolation (VI) and (2) demonstrate their usefulness for hydrologic applications.In the first part of this dissertation, the computational stability of the existing VI method is improved, then, we apply the algorithm to produce a cloud-free snow dataset for CONUS from 2000 to 2017. Moreover, by taking into consideration specific assumptions about the water body characteristic, we implement VI algorithm to remove clouds from MODIS flood maps. Promising results from a validation period over the Mississippi River are presented. We also couple the elevation information to derive the cloud-free MODIS water depth maps from the MODIS water extent maps. Water level maps are important for hydrological studies and can also act as references when the future Surface Water and Ocean Topography (SWOT) direct observations of water elevation are available in 2021.In the second part of this dissertation, we use the resulting cloud-free MODIS flood and water depth maps to improve a hydrological model by reducing model errors via calibration and data assimilation. The calibrated output inundation maps accurately reflect flood events for the Upper Mississippi River Basin in 2013 and 2014. Also, the downstream discharge via the data assimilation scheme can correctly predict flood events during the same validation period. The results indicate that the framework can be further used to monitor and forecast floods.