Inversion of geophysical properties from EnKF analysis of satellite data over semi-arid regions

As more and more satellites, specifically designed for hydrological monitoring, have been recently launched, the needs of satellite data utilization study are increasingly growing in the fields of hydrology, atmospheric science and geoscience. The development of inverse method is intended for such research needs. Main objective of this thesis is to propose the method inverting geophysical parameters from the measurements after filtering out the measurement errors, by means of data assimilation, specifically Ensemble Kalman Filter (EnKF). Significance of this method lies in overcoming the limitations of empirical formulations. The globally available satellite data-based inversion method appropriately addresses the characteristics in the extreme climatic conditions misestimated by means of empirical formulations. This thesis is organized as follows: EnKF was implemented with Surface Energy Balance System (SEBS)-retrieved sensible heat flux, and Synthetic Aperture Radar (SAR) and Soil Moisture and Ocean Salinity (SMOS)-retrieved surface soil moisture products. These EnKF analyses were further used as the reference data in the inverse method. The inversion of aerodynamic roughness in the SEBS model was conducted with the Tibet- Global Energy and Water cycle Experiment (GEWEX) Asian Monsoon Experiment (GAME) datasets. The inversion of soil hydraulic input variables in the Soil Vegetation Atmosphere Transfer (SVAT) model was implemented with the Tibet-GAME and GEWEX-Analyses Multidisciplinaires de la Mousson Africaine (AMMA) datasets. Prior to an inverse modelling, the EnKF scheme for filtering out satellite errors was explored and assessed because those observation errors may adversely affect the parameter inversion minimizing a mismatch between simulation and observation. Two different schemes of stationary and sequential EnKF were compared to examine whether observation error correction can replace the time-evolution of sequential ensemble. Because the stationary ensemble-based Ensemble Optimal Interpolation (EnOI) scheme is a computationally cost-effective but suboptimal approach, the two-step stationary EnKF scheme empirically defining the observation errors by means of L-band Microwave Emission of the Biosphere (L-MEB) radiative transfer model-based SMOS L2 processor was suggested, in contrast to a sequential EnKF assuming global constant a priori error. The result suggested that the sequential EnKF scheme consuming a longer record of satellite data may not be required if the SMOS brightness temperature errors in EnOI are empirically adjusted. The operational merit of the two-step stationary EnKF scheme lies within a short analysis time step, when compared with the Cumulative Distribution Functions (CDF) matching requiring a long record (usually, at least one year) of satellite data and the sequential EnKF scheme. Additionally, there is no need to assume a slow evolution or a global constant for the observation error parameter in the observation operator of EnKF or to define the length of the localising function for reducing sampling errors. The EnKF analysis of heat flux and soil moisture was further employed for inverting geophysical properties. The first geophysical parameter inverted was aerodynamic roughness height. It is a key input required in various models such as land surface model, energy balance model or weather prediction model. Although the errors in heat flux estimations are largely dependent on an accurate optimization of this parameter, it remains uncertain, mostly because of non-linear relationship of Monin-Obukhov Similarity (MOS) equations and uncertainty in the vertical characterization of vegetations. Previous studies determined aerodynamic roughness using a traditional wind profile method, remotely sensed vegetation index, a minimization of cost function over MOS equations or a linear regression. However, these are the complicated procedures presuming high accuracy for other related parameters embedded in MOS equations. In order to avoid such a complicated procedure and reduce the number of parameters in need, a new approach inverting aerodynamic roughness height from the EnKF-analysis of heat flux was suggested. To the best of knowledge, no previous study has applied EnKF to the estimation of aerodynamic roughness. In adition, the inversion was applied for soil hydraulic input variables of SVAT model. The performance of SVAT model is largely constrained by uncertainties in spatially distributed soil and hydraulic information, which is mainly because any Pedo-Transfer Function (PTF) estimating soil hydraulic properties is empirically defined. Accordingly, its applicability is limited. To overcome this limitation, a new calibration for inverting soil hydraulic variables from EnKF-analyzed SAR and SMOS surface soil moisture products over the Tibet-GAME and the AMMA datasets was suggested. When inverted surface variables were used, these calibrated SVAT model demonstrated a better match with the field measurement and a non-linear relationship between surface and root zone soil moisture.