Standardized Analysis Ready (STAR) data cube for high-resolution Flood mapping using Sentinel-1 data

TL;DR

This paper introduces a standardized, analysis-ready Sentinel-1 data cube called STAR, facilitating rapid flood mapping in Google Earth Engine, demonstrated through the Nigeria 2022 flood case study.

Contribution

It presents a workflow for creating and using a standardized Sentinel-1 data cube in GEE, simplifying flood analysis for researchers.

Findings

Effective flood mapping using STAR in GEE

Reduced preprocessing complexity for Sentinel-1 data

Successful application to Nigeria 2022 flood case

Abstract

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.