One of the most frequent natural perils affecting the world today is flooding, and over the years, flooding has caused a large loss of life and damage to property. Remote sensing technology and satellite imagery derived data are useful in mapping the inundated area, which is useful for flood risk management. In the current paper, commonly used satellite imagery from the public domain for flood inundated extent capturing are studied considering Can Tho City as a study area. The differences in the flood inundated areas from different satellite sensors and the possible reasons are explored. An effective and relatively advanced method to address the uncertainties—inundated area capture from different remote sensing sensors—was implemented while establishing the inundated area pattern between the years 2000 and 2018. This solution involves the usage of a machine learning technique, Support Vector Machine Regression (SVR) which further helps in filling the gaps whenever there is lack of data from a single satellite data source. This useful method could be extended to establish the inundated area patterns over the years in data-sparse regions and in areas where access is difficult. Furthermore, the method is economical, as freely available data are used for the purpose.
An Object Model for Integrating Diverse Remote Sensing Satellite Sensors: A Case Study of Union Operation