Remotely sensed radiance measurements provide useful but indirect observations of the land surface states (moisture and temperature) that ultimately control the fluxes of moisture and energy at the surface. These measurements typically are characterized by infrequent sampling, have coarse spatial resolution, and are sensitive only to the state in the upper few centimeters of the soil column. Mathematical models can be used to interpolate and extend data but rely on many simplifications and approximations that depend on inputs that are difficult to obtain over extensive areas. The only effective way to achieve flux estimates with the accuracy and coverage required for hydrologic and meteorological applications is to merge information from satellites, ground-based stations, and models. Here we describe a data assimilation procedure based on the ensemble Kalman filter (EnKF). This technique is appealing because it is easy to implement and is capable of including non-traditional model error in the assimilation framework. Two case studies are used to illustrate the potential for real-time flux estimation using remote sensing measurements. In the first example, an application to the Southern Great Plains 1997 (SGP97) field experiment shows the ability to estimate surface fluxes from the assimilation of airborne L-band microwave observations. Results from the SGP97 application of the EnKF include large scale maps of soil moisture estimates and comparisons of estimated soil moisture and latent heat flux to ground truth measurements (gravimetric and flux tower observations). The ground truth comparisons show that the filter is able to track soil moisture fluctuations and the associated variability in surface fluxes. In the second example, an application to the First ISLSCP Field Experiment (FIFE) site in Kansas is used to illustrate the estimation of surface fluxes through the assimilation of surface infrared temperature and standard reference-level micrometeorological measurements. Results show that using both surface temperature and reference-level micrometeorology measurements allows for the accurate and robust estimation of land surface fluxes even during non-ideal conditions, where the evaporation rate is atmospherically-controlled and boundary layer advection-related model errors are present. Overall, the results from these field tests indicate that the ensemble Kalman filter is an accurate, efficient, and flexible data assimilation option that is able to extract useful information from remote sensing measurements.