Over recent years, the quality and accuracy of land surface models has been significantly improved, be it through a better understanding of land surface processes or better calibrations of the model parameters. This has led to an improvement of weather and climate forecasts, as well as flood forecasting and water resources applications. However, many aspects still introduce errors through observational inaccuracies and parameter uncertainties. Land surface data assimilation has shown that some of those errors may be overcome when remotely sensed or in-situ observations are assimilated into the land surface model. For this session we call for papers that address recent improvements to land surface modelling in hydrology achieved through the assimilation of remotely sensed or in-situ observations of soil moisture, streamflow, evapotranspiration, as well as vegetation (eg. NDVI, LAI) and soil information.