The ionosphere is the upper part of the atmosphere where sufficient free electrons exist to affect the propagation
of radio waves. Typically, the ionosphere extends from about 50 - 1000 km and its morphology is mainly
driven by solar radiation, particle precipitation and charge exchange. Due to the strong ionospheric impact on
many applications dealing with trans-ionospheric signals such as Global Navigation Satellite Systems (GNSS)
positioning, navigation and remote sensing, the demand for a highly accurate reconstruction of the electron density
is ever increasing.
Within the Helmholtz Alliance project “Remote Sensing and Earth System Dynamics” (EDA) the utilization
of the upcoming radar mission TanDEM-L and its related products are prepared. The TanDEM-L mission
will operate in L-band with a wavelength of approximately 24 cm and aims at an improved understanding of
environmental processes and ecosystem change, e.g. earthquakes, volcanos, glaciers, soil moisture and carbon
cycle. Since its lower frequency compared to the X-band (3 cm) and C-band (5 cm) radar missions, the influence of
the ionosphere will increase and might lead to a significant degradation of the radar image quality if no correction
is applied.
Consequently, our interest is the reconstruction of the ionospheric electron density in order to mitigate the
ionospheric delay. Following the ionosphere’s behaviour we establish a non-stationary and anisotropic spatial
covariance model of the electron density separated into a vertical and horizontal component. In order to estimate
the model’s parameters we chose a maximum likelihood approach. This approach incorporates GNSS total
electron content measurements, representing integral measurements of the electron density between satellite to
receiver ray paths, and the NeQuick model as a non-stationary trend. Based on a multivariate normal distribution
the spatial covariance model parameters are optimized and afterwards the 3D electron density can be calculated
by kriging for arbitrary points or grids of interest
