Standardized Analysis Ready (STAR) data cube for high-resolution Flood mapping using Sentinel-1 data
This provides a standardized tool for researchers to simplify flood mapping, but it is incremental as it builds on existing data cube and platform methods.
The study tackled the complexity of preprocessing Sentinel-1 data for flood mapping by proposing a workflow using a Standardized Analysis-Ready (STAR) data cube in Google Earth Engine, applied to the Nigeria Flood of 2022 to assess performance.
Floods are one of the most common disasters globally. Flood affects humans in many ways. Therefore, rapid assessment is needed to assess the effect of floods and to take early action to support the vulnerable community in time. Sentinel-1 is one such Earth Observation (EO) mission widely used for mapping the flooding conditions at a 10m scale. However, various preprocessing steps are involved before analyses of the Sentinel-1 data. Researchers sometimes avoid a few necessary corrections since it is time-consuming and complex. Standardization of the Sentinel-1 data is the need of the hour, specifically for supporting researchers to use the Standardized Analysis-Ready (STAR) data cube without experiencing the complexity of the Sentinel-1 data processing. In the present study, we proposed a workflow to use STAR in Google Earth Engine (GEE) environment. The Nigeria Flood of 2022 has been used as a case study for assessing the model performance.