The intense research of the last decades in the field of flood monitoring has shown that microwave
sensors provide valuable information about the spatial and temporal flood extent. The new
generation of satellites, such as the Sentinel-1 (S-1) constellation, provide a unique, temporally
high-resolution detection of the earth's surface and its environmental changes. This opens up new
possibilities for accurate and rapid flood monitoring that can support operational applications. Due
to the observation of the earth's surface from space, large-scale flood events and their spatiotemporal changes can be monitored. This requires the adaptation of existing or the development of
new algorithms, which on the one hand enable precise and computationally efficient flood
detection and on the other hand can process a large amounts of data.
In order to capture the entire extent of the flood area, it is essential to detect temporary flooded
vegetation (TFV) areas in addition to the open water areas. The disregard of temporary flooded
vegetation areas can lead to severe underestimation of the extent and volume of the flood. Under
certain system and environmental conditions, Synthetic Aperture Radar (SAR) can be utilized to
extract information from under the vegetation cover. Due to multiple backscattering of the SAR
signal between the water surface and the vegetation, the flooded vegetation areas are mostly
characterized by increased backscatter values. Using this information in combination with a
continuous monitoring of the earth's surface by the S-1 satellites, characteristic time series-based
patterns for temporary flooded vegetation can be identified. This combination of information
provides the foundation for the time series approach presented here.
This work provides a comprehensive overview of the relevant sensor and environmental
parameters and their impact on the SAR signal regarding temporary open water (TOW) and TFV
areas. In addition, existing methods for the derivation of flooded vegetation are reviewed and their
benefits, limitations, methodological trends and potential research needs for this area are identified
and assessed. The focus of the work lies in the development of a SAR and time series-based
approach for the improved extraction of flooded areas by the supplementation of TFV and on the
provision of a precise and rapid method for the detection of the entire flood extent.
The approach developed in this thesis allows for the precise extraction of large-scale flood areas
using dual-polarized C-band time series data and additional information such as topography and
urban areas. The time series features include the characteristic variations (decrease and/or
increase of backscatter values) on the flood date for the flood-related classes compared to the
whole time series. These features are generated individually for each available polarization (VV,
VH) and their ratios (VV/VH, VV-VH, VV+VV). The generation of the time series features was
performed by Z-transform for each image element, taking into account the backscatter values on
the flood date and the mean value and standard deviation of the backscatter values from the nonflood dates. This allowed the comparison of backscatter intensity changes between the image
elements. The time series features constitute the foundation for the hierarchical threshold method
for deriving flood-related classes. Using the Random Forest algorithm, the importance of the time
series data for the individual flood-related classes was analyzed and evaluated. The results showed
that the dual-polarized time series features are particularly relevant for the derivation of TFV.
However, this may differ depending on the vegetation type and other environmental conditions.
The analyses based on S-1 data in Namibia, Greece/Turkey and China during large-scale floods
show the effectiveness of the method presented here in terms of classification accuracy. Theiv
supplementary integration of temporary flooded vegetation areas and the use of additional
information resulted in a significant improvement in the detection of the entire flood extent. It
could be shown that a comparably high classification accuracy (~ 80%) was achieved for the flood
extent in each of study areas. The transferability of the approach due to the application of a single
time series feature regarding the derivation of open water areas could be confirmed for all study
areas. Considering the seasonal component by using time series data, the seasonal variability of the
backscatter signal for vegetation can be detected. This allows for an improved differentiation
between flooded and non-flooded vegetation areas. Simultaneously, changes in the backscatter
signal can be assigned to changes in the environmental conditions, since on the one hand a time
series of the same image element is considered and on the other hand the sensor parameters do
not change due to the same acquisition geometry. Overall, the proposed time series approach
allows for a considerable improvement in the derivation of the entire flood extent by
supplementing the TOW areas with the TFV areas