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Flood depth is one of the important attributes in damage assessment especially in areas where agro-based activities takes place. Assessing impacted region turns out to be exceptionally difficult in the case of low-lying catchments with arable lands, which inundates due to extreme precipitation and flood modelling becomes impossible. Under these circumstances, SAR derived flood maps are valuable in removing different constraints related to flood modelling with high degree of profundity. Depth estimation from non-contact based methods requires, flood boundary as a primary input. However, detecting flood depth in emerging vegetation poses a complex challenge in terms of boundary estimation due to composite signatures that are manifested on SAR data. In this paper, a new approach is proposed to extract flood boundaries by fusing SAR data of two different frequencies, which are sensitive to water level changes. In the first step, wavelet fusion is applied to combine L- and C- band SAR data followed by Otsu&#x2019;s segmentation method to extract varying levels of flood boundaries. In the second step, SRTM Digital Elevation Model (DEM) with 30m horizontal resolution is used on each boundary for statistical analysis based on which water surface levels are extracted. In the final step, depth levels are calculated from the water surface elevation and DEM. Floodwater Depth Estimation tool (FwDET) derived depth measurements are used to calibrate the statistical thresholds for derived flood depths. The study carried out on 2016 Assam flood event shows maximum flood depth of 1.56 meters in the selected study area and the results are verified with evidence-based ground truth collections which showed RMSE error of 0.25 meters from the measured values.

Flood Depth Estimation in Agricultural Lands From L and C-Band Synthetic Aperture Radar Images and Digital Elevation Model