Improving Earth Observations by correlating Multiple Satellite Data: A Comparative Analysis of Landsat, MODIS and Sentinel Satellite Data for Flood Mapping

Flooding is a recurring and severe calamity, aggravated by climate change and rapid urbanization, resulting in substantial loss of life, property, and economic disruption. To address this critical issue, a comprehensive study focuses on delineating flood-prone zones using satellite data from Landsat, MODIS, and Sentinel sources. Geographic Information System (GIS) technology with Google Earth Engine (GEE) platform, providing an environment which is based on the cloud for analysis of geospatial data. The methodology encompasses data collection, preprocessing, and the application of algorithms for flood mapping and depth estimation. By assessing flood extents and depths, it contributes to informed decision-making for flood management and disaster preparedness. Researchers have made prominent efforts to use these satellite data in the individual manner or using techniques like fusion to combine two different satellite data. The author has made an attempt to design a platform where all three-satellite data would be available together so that valuable insights are drawn in the field of flood mapping. This paper is prominently focused to corelate those satellite data with the help of difference mapping.