The accurate description of carrier transport in emerging devices based on Boltzmann's equation (BTE) is of fundamental importance. One way to solve the BTE is by applying the Monte-Carlo (MC) technique, which is very accurate but time consuming. A more efficient way to find approximate solutions is the method of moments. Modeling the scattering operator of the BTE with a macroscopic relaxation time approximation and multiplying with a proper set of weight functions, one obtains the drift-diffusion, the energy transport, the six moments model, as well as higher-order models. For an accurate description of carrier transport it is important to model transport parameters, like the carrier mobility in the drift-diffusion model, with as few simplifying assumptions as possible. A good choice is the calculation of parameter tables extracted from MC simulations for a parameter interpolation within a device simulator. So far only bulk MC data has been taken into account. The application of this data to MOSFET devices is problematic, due to the importance of surface scattering and quantization in the channel. Many investigations have been performed to describe surface roughness scattering on the carrier mobility taking the semiempirical Matthiesen rule into account. However, the impact of quantization effects and surface roughness scattering on higher-order parameters, like the energy relaxation time and the energy mobility in the energy transport model, or the second-order relaxation time and the second order mobility in the six moment transport model, has not been described satisfactorily yet. An extraction technique for higher-order transport parameters using a subband MC simulator coupled self-consistently to a Schrödinger Poisson solver has been developed. Thus quantization effects and surface roughness scattering are automatically considered. The method allows an accurate description of the parameter behavior for high electric fields, for instance in the important case of an ultra thin body SOI MOSFET. Carriers gain kinetic energy, resulting in a reoccupation of the subband ladders, which itself shifts the wave functions within the inversion layer (see Fig.1), leading to a change in the extracted parameters.