# Urban Flood Mapping Using SAR Intensity and Interferometric Coherence via Bayesian Network Fusion
## 5 Conclusions  
In this paper, we introduced a method for flood mapping in urban environments based on SAR intensity and interferometric coherence under the Bayesian network fusion framework. It integrates intensity and coherence information from a viewpoint of probability and takes into account the flood uncertainty in terms of intensity and coherence. The combination of intensity and coherence extracts flood information in varying land cover types and outputs both flood binary extent and flood category maps: including non-obstructed-flood (e.g., flooded bare soils and short vegetation); obstructed-flooded non-coherent area (e.g., flooded vegetation and vegetated built-up areas); and obstructed-flooded coherent area (e.g., flooded predominantly built-up areas). The approach is unsupervised and only based on SAR data, therefore favorable for operational emergency response with data from SAR missions with a short revisit time and systematic observation scenario such as Sentinel-1.  
This method was tested on two flood events that were captured by different SAR sensors: the Houston (US) 2017 flood event with Sentinel-1 (C band, 20 m resolution) time series, and the Joso (Japan) 2015 flood event with ALOS-2/PASAR-2 (L band, 3 m resolution) time series. The flood maps were validated by the reference flood masks derived from high-resolution aerial photographs and showed satisfying results in both case studies. The findings in the experiments demonstrate that the synergistic use of SAR intensity and coherence provides more reliable flood information in urban areas with varying landscapes than using intensity alone. Specifically, flood detection in less-coherent/non-coherent areas (e.g., bare soils, vegetation, vegetated built-up areas) relies on multi-temporal intensity, whereas multi-temporal coherence gives more comprehensive flood information in coherent areas (e.g., predominantly built-up areas). Nevertheless, some special flood situations such as flooded parking lots and flooded dense building blocks are still challenging for both intensity and coherence.  
As the proposed method is sensor and scene independent, it provides opportunities for urban flood mapping at a global scale and especially in low-income countries with the highly frequent and systematic observations from SAR missions such as Sentinel-1 and the RADARSAT Constellation Mission (RCM). The upcoming missions such as Tandem-L and NISAR increase the observation frequency of spaceborne SAR systems and the possibility of flood detection in vegetated areas. Besides, the proposed method provides a supplement to the current Sentinel-1 Flood Service at the German Aerospace Center (DLR) to account for flooding in urban areas.  
[PERSON] designed and performed the experiments; [PERSON] collected the ALOS-2/PASAR-2 data; [PERSON] contributed to the critical review of the methodology; [PERSON], [PERSON], and [PERSON] contributed to the interpretation of the results; [PERSON] wrote the original paper, [PERSON], [PERSON], [PERSON], and [PERSON] contributed to review and editing.  
This work is funded by the China Scholarship Council (CSC). R.N. receives a grant of Overseas Research Fellowships from Japan Society for the Promotion of Science (JSPS).  
The authors would like to thank the National Oceanic and Atmospheric Administration (NOAA) and the Geospatial Information Authority of Japan (GSI) for providing high-resolution aerial photographs. ALOS-2/PASAR-2 data were kindly provided by the Japan Aerospace Exploration Agency (JAXA, proposal number MTH1153, PI number 3043).  
The authors declare no conflict of interest.