# Operational Flood Mapping Using Multi-Temporal Sentinel-1 SAR Images: A Case Study from Bangladesh
## Materials and Methods
### Methods
For land cover mapping, optical satellite images were used, e.g., Landsat are most common, and data were freely accessible and are often used to explore unique spectral characteristics of different land cover using image indices. The Google Earth Engine environment is a powerful computational fast analysis processing platform that can handle huge volumes of remote sensing imagery [67; 68]. GEE provides online access to archived pre-processed Landsat imagery [69]. In the GEE, a sequence of processing steps was followed (Figure 2) for image analysis and land use/land cover mapping. All the 2017 pre-flood Landsat data for the entirety of Bangladesh were processed to derive the pre-flood water extent ([[https://code.eartheengine.google.com/](https://code.eartheengine.google.com/)]([https://code.eartheengine.google.com/](https://code.eartheengine.google.com/))). First, as part of the atmospheric image correction, a cloud-free Landsat-8 image composites were prepared using partially cloudy images available for the period of January and April 2017. Using the derived cloud free image composites, the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), normalized difference moisture index (NDMI), bare soil index (BSI), normalized pigment chlorophyll ratio index (NPCRI), and land and water mask (LWM) index were created and used for land cover mapping. A supervised classification scheme was used to generate 2017 land cover maps from the set of Landsat-8-based index layers. To facilitate the 2017 land cover classification, training data were acquired using Google Earth High-resolution images as a basis. A total of 5484 training sets were collected for areas under tree coverage (Madhupur forest, hill forest, and mangrove forest, as well as rural settlement and homestead orchard), 8 were acquired for grassland, 346 for the barren area, 1039 for cropland, 3142 for waterbodies, and 86 for built-up areas. All the land cover legends were developed based on the land cover classification system (LCCS), which was developed by FAO to provide a consistent framework for the classification and mapping of land cover [70; 71]. All training data were imported into the Google Earth Engine environment in the form of a fusion table. Finally, a classification and regression tree (CART) land cover classifier was applied using the imported training sets and the Landsat-related raster layers. The CART is a decision tree (DT) based machine-learning method for constructing prediction models from a set of training data using the concept of information entropy that shows the strongly improved performance of classification [72; 73]. In addition to the Landsat bands \"B1\", \"B2\", \"B3\", \"B4\", \"B5\", \"B6\", \"B7\", \"B8\", and \"B9\", the indices \"NDVI\", \"NDWI\", \"NDMI\", \"BSI\", \"NPCRI\", \"LWM\", and the SRTM DEM were also used in the classification. As with any digitally classified land cover product, there can be reasons for misclassification related to the environmental conditions at the time of image acquisition (clouds, fog, etc. ), variations in local forest types or limitations in computational algorithms. Finally, the derived land cover maps were validated using another set of samples collected from field data and independent training data from high-resolution images of Google Earth. Publicly open earth observation data and online map tools like Google Map, Google Earth, Collect Earth Online and OpenStreetMap allow the accuracy assessment of a national level land cover based on very high-resolution satellite images [38; 39; 74; 75; 76]. The accuracy of the 2017 Landsat-derived land cover map was assessed using (10 km \(\times\) 10 km) 1400 reference points from Google Earth and 65 points from the ground. These were compared with the land cover map to calculate the error matrix, and an overall accuracy of 87.51% was found.