Real-time flood inundation monitoring in Capital of India using Google Earth Engine and Sentinel database

Abstract

This study focuses on researching flood inundation and vulnerable areas in Delhi NCT using Remote Sensing (RS) and GIS techniques during flood from July 8 to July 15, 2023, with the entire analysis conducted through satellite and cloud-based processing methods, specifically employing the Google Earth Engine (GEE). Leveraging high-temporal satellites that gather data enables the identification of flooded zones in real-time, during floods, and in the aftermath. Analyzing data collected at different stages of a flood provides valuable insights for pinpointing affected areas. Water naturally flows from high to low elevation areas, and based on elevation data, lowest elevation regions, particularly along the Yamuna riverbank under Delhi NCT, are highly susceptible to flooding, are considered flood-prone areas and flood water inundated. A thorough comprehension and flood-prone area mapping, along with a map illustrating highly inundated zones. After obtaining flood inundation maps and overlaying them with Land Use and Land Cover (LULC) classified maps, the study identified specific areas that experienced flood inundation. The analysis generated a flood zone map, indicating that the flooded area encompasses approximately 110 km², within a total study area of 1488.4 km². The affected areas have elevations ranging from 200 to 210 meters, whereas the maximum elevation in the study area is approximately 326 meters. The GEE platform is employed for processing, utilizing a Supervised classification algorithm for LULC mapping, and an Inverse Distance Weight method for mapping temperature and rainfall. This study utilized the GEE platform to create pre- and post-flood maps based on Sentinel 1 satellite datasets. Generated DEM, and employed it to create various surface estimation maps, including a stream order map. The GIS is employed to enhance the efficiency of monitoring and managing flood disasters, with the high temporal and spatial resolution data playing a pivotal role in flood monitoring.