Dr. ir. Susan Steele-Dunne
Estimating soil moisture using microwave remote-sensing
Soil moisture plays an important role in the water and energy balance at the land surface, and so is a critical variable in surface hydrology and land-atmosphere interactions. Soil moisture is highly variable in space and time, and so it is impossible to measure at scales of interest using in-situ observations. In the next five years, ESA and NASA will launch the first dedicated missions to make global measurements of soil moisture from space. Both satellites use microwaves to measure changes in land surface properties due to soil moisture. I am particularly interested in how the presence of vegetation affects our ability to measure soil moisture. Rather than treating the vegetation as something that prevents us from measuring soil moisture, can we say something about soil moisture from how the vegetation affects the microwave signal? I am also interested in methods to combine different kinds of microwave observations to estimate soil moisture.
Using data assimilation techniques to merge hydrological models and observations
Data assimilation refers to a collection of techniques which can be used to optimally combine imperfect models with uncertain observations. These methods are very powerful in hydrology because we often have to use poor forcing data, uncertain model parameters and models which include simplifications and assumptions. We often have observations which are at scales other than those of interest, or which are indirect observations. I have used ensemble smoothing methods to demonstrate that hydrological processes which have inherent memory are best estimated using smoothing/batch techniques. Currently, I am investigating how data assimilation techniques can be used support water resources management in West Africa, where in-situ data is sparse and models are difficult to calibrate and validate.
Using distributed temperature sensing to estimate soil moisture
ESA’s Soil Moisture and Ocean Salinity (SMOS) mission and NASA’s Soil Moisture Active/Passive (SMAP) mission will measure large-scale (~3km-40km) changes in soil moisture. Current methods to validate these satellite observations are limited to point-scale in-situ soil moisture measurements. We are working with colleagues at Oregon State University to use distributed temperature sensing (DTS) to provide distributed soil moisture observations over large areas (up to 10km) at 1m resolution. I am using data assimilation techniques to infer root zone soil moisture from temperature measurements in fibre-optic cables near the surface. I am also using data assimilation techniques to investigate the optimum experiment design to estimate surface and root zone soil moisture using combined active and passive DTS.
Vegetation: A novel soil moisture sensor?
Assimilation of GRACE data into the HBV model in the River Rhine basin
SoilDTS: Using distributed temperature sensing to estimate soil moisture
A list of publications by Susan Steele-Dunne can be found here
Courses Taught at TUDelft
CT5401 Spatial Methods (Microwave remote-sensing module)
Watermanagement (Master) (1)
Spatial Tools in Water Resources Management
Project(s) Innovative Weather Sensing