Wietske S Brouwer, Ramon Hanssen 

InSAR enables the estimation of displacements of
(objects on) the Earth’s surface. To provide reliable estimates,
both an independent stochastic and functional model are re-
quired. However, the intrinsic problem of InSAR is that both
are unknown. Here we propose an independent definition of the
stochastic model, via an approximation scheme for the variance-
covariance matrix for double-differenced phase observations for
an arc, i.e., the phase difference between two points relative to a
reference epoch. Detecting temporal partitions in the amplitude
time series, we assign quality values to all phase observations
within each partition. To reduce the impact of outliers, we
introduce the Normalized Median Absolute Deviation (NMAD)
of the vector of amplitudes to robustly estimate the variance of
the phase observations. The method results in a scatterer-specific
and time-variable stochastic model, which is independent of the
phase observations itself and prior to parameter estimation. This
differs from many conventional methods where the quality is
often determined a posteriori from the residuals between the
model and the observations. This yields more realistic and reliable
displacement estimates, as well as improved statements on the
precision and reliability of the estimated parameters.