Assimilation of Land-Surface Observations in Coupled Hydrological Models

How can remote-sensing data be used for data assimilation and uncertainty reduction of land-surface processes?

PhD Researcher: Chang-Hwan Park
Supervisors: Volker Wulfmeyer (University of Hohenheim), Andreas Behrendt (University of Hohenheim), Ellsworth LeDrew (University of Waterloo)

PhD Thesis

My research interest is to maximize the benefit of remote sensing data for deeper understanding about our world and better prediction for the future. In this end, my PhD study within IRTG is the remote sensing data assimilation in the water resource management and prediction in the HydroGeoSphere model. The modern remote sensing application provides temporally and spatially highly resolved observation data from local to global scales. However, it is challenging to extend this method to the subsurface area. We expect that the highly resolved surface condition with physically proper propagation strategy in the model can effectively improve modeling and prediction of subsurface water and material fluxes. Therefore, the first step in this research is to develop the observation operator between remotely sensed signal and targeted land surface properties like soil texture, canopy types, and their temperature and water contents.

The link between observed signal and physical properties is the dielectric constant. Comparing to the dielectric constants of dry biomass and soil particles, water shows 10 times higher dielectric permittivity. The key part of our observation operator is the implementation of effective dielectric constants of these complex components applying mixing approach. Dielectric mixing approach consistently decomposes the optical properties of soil, biomass, water and air into finer elements. The observation operator is examined with passive microwave remote sensing data (L,C and X-band observations) and in-situ measurements (canopy type, soil texture, soil temperature and soil water). The further development of the operator will be performed for active microwave and thermal infrared as a hyper spectral observation operator including atmospheric layer containing absorbing gases.

Publications

Park, C.-H., Behrendt, A., LeDrew, E., Wulfmeyer, V. (2017): New Approach for Calculating the Effective Dielectric Constant of the Moist Soil for Microwaves. Remote Sens. 9(7), 732; doi: 10.3390/rs9070732

How can remote-sensing data be used for data assimilation and uncertainty reduction of land-surface processes?

PhD Thesis

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