An integrated use of radio magnetotelluric (RMT) and controlled source tensor magnetotelluric (CSTMT) measurements, the so-called CSRMT method, has been employed in environmental and resource exploration studies. A number of case histories, including a groundwater investigation in glacial deposits, a study of fracture zones for geotechnical purposes and a mining exploration study of a copper deposit, are presented in this thesis in order to illustrate the usefulness and capability of the CSRMT method. The resolutions of the estimated models using various types of data are studied. Magnetotelluric transfer functions are used to analyze the dimensionality, the near surface resistivity distortions and the near field effects in the case of CSTMT data analysis. The near field effects in CSTMT data have also been identified by performing 2½D forward modelling. Data analysis, dimensionality tests and forward modelling show that at the lowest frequencies used the CSTMT transfer functions are generally distorted by source effects, except when the source-receiver distances are sufficient large compared with the penetration depth. Regarding CSTMT transfer functions, apparent resistivities are generally less distorted than phases. TM mode transfer functions are more affected by the sources than TE mode, while tipper vectors generally contain source signatures at all frequencies. Based on the analysis of dimensionality and source effects 2D inverse modelling of CSTMT and RMT data, as well as their combination, have been performed under the plane wave assumption. The RMT method proved to be a powerful tool for imaging the upper 50 m near-surface, but their penetration depth reduces as a conductive layer structures cover the targets at depth. The penetration depth can be increased by including the CSTMT data in the modelling if the measurements are in the far field range. The resolution of the deeper parts of the models may be improved by performing a joint inversion of TE and TM modes, if the strike direction is well-defined. Alternatively, inversion of determinant data can be performed, since the determinant data are less affected by 3D structures and source effects. However the resolution of the determinant models is somewhat degraded compared to the models inverted from combined TE and TM modes.