# Operational Flood Mapping Using Multi-Temporal Sentinel-1 SAR Images: A Case Study from Bangladesh
## Materials and Methods
### Methods  
The methods used for this study are presented in Figure 3. Specifically, Sentinel-1 image classification for flood mapping, with initial pre-processing carried out to mitigate the SAR-typical speckle noise signatures from the images. During the pre-processing step, we rectified the radiometric and geometric distortions due to the characteristics of the imaging system and imaging conditions and performed radiometric corrections to improve visualization and interpretation for flood mapping. Figure 3: Overall methodological framework for the flood inundation mapping and damage assessment using multi-temporal Sentinel-1 and Landsat-8 satellite images. The pre-processing steps, including data import, radiometric calibration, speckle filtering, radiometric terrain correction [52; 53], linear-to-backscattering coefficient decibel scaling (dB) transformation, and data export, were implemented using ESA's Sentinel Application Platform (SNAP). The open-access SNAP toolbox is capable of reading, pre-processing, and visualizing Sentinel-1 SAR images. During the Sentinel-1 pre-processing, Level-1 images were first imported into the SNAP Desktop tool. Secondly, Sentinel-1 images were radiometrically corrected by applying annotated image calibration constants to arrive at physically meaningful radar backscatter pixel values. Thirdly, speckle filters were applied to reduce the granular noise characteristic to SAR data. Fourthly, multi-look processing was carried out to reduce the speckle further and improve image interpretability. Fifthly, geometric distortions present in the SAR images were corrected by transforming the coordinates to a standard reference frame. The ratio band of VH/VV was generated dividing the VH by the VV band. Finally, a radiometric conversion from a linear scale to a dB scale was conducted using the following expressions:  
\[\sigma_{\text{dB}}^{0}=10\cdot\log_{10}\sigma^{0} \tag{1}\]  
where, \(\sigma^{0}\) (dB)--backscattering image in dB, \(\sigma^{0}\)--Sigma nonught image. The preprocessed images were exported for classification. For automation, all processing steps were assembled and connected through Graph Builder, which is available in SNAP and was run in batch processing mode. The pre-processed stack of Sentinel-1 SAR images was imported into ERDAS Imagine for knowledge-based image analysis. According to [PERSON] and [PERSON] [54], knowledge-based classifications contain the following 5 characteristics: Aim, ancillary data, domain knowledge, knowledge presentation, and inference. The knowledge engineer provides the interface for an expert with first-hand knowledge of the data to develop an algorithm into a hierarchical decision tree using logic or rules [55]. While performing expert classification, the Sentinel-1 images were clustered to create a thematic raster layer by RGB clustering functions in ERDAS Imagine. In geospatial applications, the unsupervised classification used to be known as the iso-clustering or migrating means technique that helps to group the same type of features into homogeneous and diverse features into heterogeneous clusters [56; 57; 58]. The RGB clustering is the most common technique for data compression and iso-clustering works better on an optimal number of classes usually unknown [59; 60]. The RGB clustering functions are a simple classification algorithm that quickly compresses a three-band image into a single-band pseudo-colour image without necessarily classifying any particular features and without a signature file and decision rule. The RGB clustering provides greater control over the parameters used to partition the pixels into similar classes [61; 62]. During Sentinel 1 image clustering, the VH band is designated as a red band, the VV band as a green band, and the VH/VV band as the blue band. Secondly, the RGB clustered image was processed to generate a clamped image for converting thematic class values into uniquely numbered \"polygons\", representing contiguous groups of the original class values.