Google Earth Engine for Large-Scale Flood Mapping Using SAR Data and Impact Assessment on Agriculture and Population of Ganga-Brahmaputra Basin

Abstract

The Ganga-Brahmaputra basin is highly sensitive to the impacts of climate change and experiences recurrent flooding, which affects large agricultural areas and poses a high risk to the population. The present study is focused on the recent flood disaster in the Ganga-Brahmaputra basin, which mainly affected the regions of Bihar, West Bengal, and Assam in India and neighboring Bangladesh during July, August, and September 2020. Using the Sentinel-1A Synthetic Aperture Radar (SAR) data, the flood extent was derived in the Google Earth Engine (GEE) platform. The composite area under flood inundation for July–September was estimated to be 25,889.1 km2 for Bangladesh, followed by Bihar (20,837 km2), West Bengal (17,307.1 km2), and Assam (13,460.1 km2). The Copernicus Global Land Cover dataset was used to extract the affected agricultural area and flood-affected settlement. Floods have caused adverse impacts on agricultural lands and settlements, affecting 23.68–28.47% and 5.66–9.15% of these areas, respectively. The Gridded Population of the World (GPW) population density and Global Human Settlement Layer (GHSL) population dataset were also employed to evaluate flood impacts, which revealed that 23.29 million of the population was affected by floods in the Ganga-Brahmaputra basin. The highest impacts of floods can be seen from the Bihar state, as people reside in the lower valley and near to the riverbank due to their dependency on river water. Similarly, the highest impact was from Bangladesh because of the high population density as well as the settlement density. The study provided a holistic spatial assessment of flood inundation in the region due to the combined impact of the Ganga-Brahmaputra River basin. The identification of highly flood-prone areas with an estimated impact on cropland and build-up will provide necessary information to decision-makers for flood risk reduction, mitigation activities, and management.

Analysing frequent extreme flood incidences in Brahmaputra basin, South Asia

“…For the Brahmaputra basin, the backscatter intensity of VH varies between -15 to -20 dB during the pre-flood period, while the backscatter intensity was approximately -25 to -30 dB during the post-flood analysis. Backscatter ratio methods were adopted following Lal et al (2020) [ 17 ] and it’s one of the widely used techniques for flood inundation mapping [ 33 , 34 ]. The backscatter ratio method estimates the changes in due to flood inundation the during flood mosaic backscatter SAR data were divided from pre-flood mosaic SAR backscatter data to deduce the flood inundation.…”

A New Clustering Method to Generate Training Samples for Supervised Monitoring of Long-Term Water Surface Dynamics Using Landsat Data through Google Earth Engine

“…There are currently petabytes of remote sensing data available to scientists, researchers, engineers, and decision-makers that can be used for a wide range of applications. Especially for large-scale and long-term studies, these data require huge storage spaces and powerful hardware processing systems [19]. Today, with the development of cloud-based platforms such as Google Earth Engine (GEE), it has been possible to process remote sensing data online without downloading [20].…”

“…Today, with the development of cloud-based platforms such as Google Earth Engine (GEE), it has been possible to process remote sensing data online without downloading [20]. Various studies showed the effectiveness of GEE in different remote sensing applications such as landcover classification [21,22], wetland detection [23], water quality monitoring [24], flood mapping [19], and impact analysis of drought and floods on croplands [25].…”

Quantifying the Impacts of the 2020 Flood on Crop Production and Food Security in the Middle Reaches of the Yangtze River, China

“…In response to this scientific research development trend and the challenge of extensive data analysis, Google has developed and made public a cloud computing platform called "Google Earth Engine (GEE)" to provide global-scale geospatial analysis services. Google's substantial online computing power can effectively solve the need for a large area and long-term monitoring [8][9][10][11].…”