Abstract
Abstract. This study presents a semi-automatic algorithm for mapping floods. Both Optical and Synthetic Aperture Radar (SAR) data are used to observe the flood that hit the Cukurova region of Adana (Turkey) in 2019. The performance of the interferometric coherence in complementing intensity component of SAR data is investigated for mapping the floods occurred in agricultural and urban environments. There was no ground truth data available from the flooded area, thus classification result of optical satellite image is used as a seed for the region growing algorithm that defines the classes according to a threshold value. The advantage of using both intensity and coherence change detection is verified with the results. The results have been evaluated through very high-resolution SPOT-6 optical image which acquired simultaneously with Sentinel-1B SAR image. The comparison with the SPOT-6 data results shows that the proposed approach can map flooded areas with acceptable accuracy using the SAR data from Sentinel-1 satellite mission. Highly affected agricultural areas along with the river line could be mapped both by optical and SAR analysis. Comparison of results from VV and VH polarization provided that cross-polarization VH has a very little effect on flood mapping. The proposed algorithm successfully distinguishes the classes among the affected region, especially in urban areas.
Highlights
1.1 General InstructionsSynthetic Aperture Radar (SAR) and optical satellite data have proven to provide essential information in case of natural disasters like flooding
Many studies have proven that SAR systems are very effective and reliable tools for flood mapping especially on vegetated areas and the bare soil as well, by benefiting from their sensitivity to surface roughness and soil moisture changes (Schlaffer et al, 2015) and (Li et al, 2018)
Radiometric calibration, speckle filtering, binarization, co-registration, Range-doppler terrain correction were performed within the Sentinel Application Platform (SNAP)
Summary
Synthetic Aperture Radar (SAR) and optical satellite data have proven to provide essential information in case of natural disasters like flooding. Many studies have proven that SAR systems are very effective and reliable tools for flood mapping especially on vegetated areas and the bare soil as well, by benefiting from their sensitivity to surface roughness and soil moisture changes (Schlaffer et al, 2015) and (Li et al, 2018). C-band radar systems (Sentinel-1) (Borah et al, 2018) and (Monti-Guarnieri et al, 2018) have a higher sensitivity to surface changes and vegetation cover, which can produce a noise signal and cause potential challenges for observation. Despite these disadvantages, Sentinel-1 data are freely available and have a higher repeat time (6-12 days) [3]. SPOT-6 optical data was used for water body detection, later used for seeding and the accuracy analysis of the proposed method
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