Google Earth Engine-Based Identification of Flood Extent and Flood-Affected Paddy Rice Fields Using Sentinel-2 MSI and Sentinel-1 SAR Data in Bihar State, India

Abstract

Flood is the major cause of fatalities associated with natural disasters in the world. In India especially in the state of Bihar, where about half of the area (North Bihar) gets flooded every year due to the overflow of major rivers during the rainy season. Which severely affects human lives, properties, agricultural production, farmers and their livelihood. Usually, the basins of the Kosi and Gandak rivers are known for their worst affects in Bihar. Synthetic aperture radar (SAR) is widely used for robust monitoring of flood events due to its ability to image the surface of the earth in all weather conditions. However, limited studies are available on flood patterns of Bihar and their impact on agriculture. Here, we investigated the flood extents and affected paddy rice fields for Bihar during the months of June–October (2020) using all accessible Sentinel-1 SAR and Sentinel-2 MSI images with additional supporting datasets available on the Google Earth Engine. The study showed that a large portion of Bihar (7019 km 2 ) was submerged during monsoon season. The floodwater remains in the agricultural fields for 50 to 65 days causing severe damage to the Kharif crops, mainly rice. The extreme effect of flood was seen in agricultural lands (11.23% of the total area) and populations (15.56% of the total population) in Bihar. Satellite-based identification of flood progression and affected rice fields can be helpful for decision-makers at the time of disaster to prioritize relief and rescue operations.

Flood Susceptibility Mapping to Identify the Vulnerable Areas in the Adayar River Basin at Chennai, Tamil Nadu

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Cartografía multitemporal y estrategias para la prevención de inundaciones en una cuenca con predominio agrícola de Argentina

“…In the case of flood inundation extent maps developed from satellite imagery, the creation of several algorithms has been produced by different institutions such as universities, space agencies, or companies directed to disaster recovery and response (DeVries et al, 2020;Hawker et al, 2020;Mehmood et al, 2021;Policelli et al, 2017). Specifically, for the Global South, most of the flood-related research including the use of GEE for flood extent identification is associated with South Asia (Kumar et al, 2022;Lal et al, 2020;Pandey et al, 2022;Vanama et al, 2020), with very few studies in LAC countries (Mora et al, 2021;Tellman et al, 2021).…”

Improved Lithological Map of Large Complex Semi-Arid Regions Using Spectral and Textural Datasets within Google Earth Engine and Fused Machine Learning Multi-Classifiers

“…It provides a variety of packages for image collection, analysis, processing, classification and export to ensure that users are no longer solely dependent on expensive commercial software [55][56][57]. Several researches use GEE to take advantage of its massive data catalogue to examine dynamic processes over long timeseries data and to generate large-scale thematic classifications for a variety of applications, including LU/LC mapping [58], cropland classifications [59], forest habitats mapping [60], surface water detection [61], urban and rural settlement [62], mine mapping [63], natural hazard mapping and snow and shoreline detection [64,65]. However, its application in geological mapping remains very limited [54].…”