The C-band Sentinel-1 satellite constellation enables the continuous monitoring of the Earth’s surface within short revisit times. Thus, it provides Synthetic Aperture Radar (SAR) time series data that can be used to detect changes over time regardless of daylight or weather conditions. Within this study, a time series classification approach is developed for the extraction of the flood extent with a focus on temporary flooded vegetation (TFV). This method is based on Sentinel-1 data, as well as auxiliary land cover information, and combines a pixel-based and an object-oriented approach. Multi-temporal characteristics and patterns are applied to generate novel times series features, which represent a basis for the developed approach. The method is tested on a study area in Namibia characterized by a large flood event in April 2017. Sentinel-1 times series were used for the period between September 2016 and July 2017. It is shown that the supplement of TFV areas to the temporary open water areas prevents the underestimation of the flood area, allowing the derivation of the entire flood extent. Furthermore, a quantitative evaluation of the generated flood mask was carried out using optical Sentinel-2 images, whereby it was shown that overall accuracy increased by 27% after the inclusion of the TFV.

Synthetic Aperture Radar (SAR) is particularly suitable for large-scale mapping of inundations, as this tool allows data acquisition regardless of illumination and weather conditions. Precise information about the flood extent is an essential foundation for local relief workers, decision-makers from crisis management authorities or insurance companies. In order to capture the full extent of the flood, open water and especially temporary flooded vegetation (TFV) areas have to be considered. The Sentinel-1 (S-1) satellite constellation enables the continuous monitoring of the earths surface with a short revisit time. In particular, the ability of S-1 data to penetrate the vegetation provides information about water areas underneath the vegetation. Different TFV types, such as high grassland/reed and forested areas, from independent study areas were analyzed to show both the potential and limitations of a developed SAR time series classification approach using S-1 data. In particular, the time series feature that would be most suitable for the extraction of the TFV for all study areas was investigated in order to demonstrate the potential of the time series approaches for transferability and thus for operational use. It is shown that the result is strongly influenced by the TFV type and by other environmental conditions. A quantitative evaluation of the generated inundation maps for the individual study areas is carried out by optical imagery. It shows that analyzed study areas have obtained Producer’s/User’s accuracy values for TFV between 28% and 90%/77% and 97% for pixel-based classification and between 6% and 91%/74% and 92% for object-based classification depending on the time series feature used. The analysis of the transferability for the time series approach showed that the time series feature based on VV (vertical/vertical) polarization is particularly suitable for deriving TFV types for different study areas and based on pixel elements is recommended for operational use.

The potential of multi parametric polarimetric SAR (PolSAR) data for soil surface roughness estimation is investigated and its potential for hydrological modeling is evaluated. The study utilizes microwave backscatter collected from the DEMMIN test site in the North East of Germany during the AgriSAR 2006 campaign using fully polarimetric L-band E-SAR data. In addition to various measurements of soil physical properties, soil surface roughness was measured extensively using photogrammetric image matching techniques for ground truthing. The resulting micro-DSMs are analyzed to correlate a soil surface roughness index to three well established polarimetric roughness estimators. Good results are obtained for Re_[ρRRLL] vs. RMS Height for areas with a polarimetric alpha angel α<40°, which is thus used to produce multi temporal roughness data of the test site. The proposed roughness inversion scheme showed sufficiently accurate results (RMSE=0.1) to allow for a first order assessment of soil-hydrological parameters (soil porosity, void ratio), which are crucial for the initialization and operation of hydrological surface models. While uncertainties remain, the dependency of soil bulk density parameters from surface roughness can be shown and thus highlights the potential of the retrieval approach for hydrological model applications.