Satellite Gravimetry Level-2 Data De-striping Based on Signal Contrast
for Small-scale Applications
Abstract
As a result of uneven density of data collection, level-2 satellite
gravimetry data suffer from global north-south striping. By applying
various filtering methods, several studies have addressed the mitigation
of the data. However, the studies mainly addressed the issue on a global
scale, and the local effects were not considered. On the other hand,
water research, especially inland hydrology, usually deals with
small-scale fitures such as lakes and watersheds. Therefore, the local
data de-striping methods need special attention. This research presents
a new analytical method to de-stripe gravimetry data based on the
spatial contrast of signals. The approach strikes a balance between
de-striping and signal preservation. Using a-priori information obtained
from the gravimetry data, the de-striping method first estimates the
spatial gradient of the signal and optimizes a Poisson filter based on
this information to de-stripe the data. Unlike the other approaches, the
optimized filter is dynamic and accounts for temporal variations in the
signal contrast, such as seasonality. The proposed approach is applied
to ten globally distributed study areas to derive a general scheme.
Detailed processes and evaluations are applied to two study areas: the
Caspian Sea and the Congo River Basin. Results are visually assessed for
spatial fit and for temporal consistency by comparison with results from
other filters. The use of a dynamic filter set specified for each region
and time point allows us to preserve local hydrologic signals that are
susceptible to globally optimized filters. It also allows filter-related
errors to be effectively constrained.