Perspective on satellite-based land data assimilation to estimate water cycle components in an era of advanced data availability and model sophistication

The beginning of the 21^st century is marked by a rapid growth of land surface satellite data and model sophistication. This offers new opportunities to estimate multiple components of the water cycle via satellite-based land data assimilation (DA) across multiple scales. By resolving more processes in land surface models and by...

Mapping high-resolution soil moisture and properties using distributed temperature sensing data and an adaptive particle batch smoother

This study demonstrated a new method for mapping high-resolution (spatial: 1 m, and temporal: 1 h) soil moisture by assimilating distributed temperature sensing (DTS) observed soil temperatures at intermediate scales. In order to provide robust soil moisture and property estimates, we first proposed an adaptive particle batch smoother...

Assimilation of soil moisture data with the ensemble Kalman filter for the intermediate scale soil moisture predictions in the Netherlands

This thesis discusses the applicability of assimilation of artificial SMAP data into a quasi steady state hydrological model to improve soil moisture estimates. The model used for this research was SIMGRO and since no real SMAP data were available at the time of the research, artificial SMAP data were used. The ensemble Kalman filter was used to...

Surface and subsurface water from space: On the integration of microwave remote sensing observations with flood prediction systems

The technique of active microwave remote sensing has made much progress toward its high potential to monitor water storage changes in terrestrial surface and subsurface water bodies at various spatial and temporal scales. The number of studies demonstrating the support these data can offer in hydrological and hydraulic model building, model...