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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
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, proper...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer India
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8767773/ http://dx.doi.org/10.1007/s12524-021-01487-3 |
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author | Kumar, Himanshu Karwariya, Sateesh Kumar Kumar, Rohan |
author_facet | Kumar, Himanshu Karwariya, Sateesh Kumar Kumar, Rohan |
author_sort | Kumar, Himanshu |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8767773 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer India |
record_format | MEDLINE/PubMed |
spelling | pubmed-87677732022-01-19 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 Kumar, Himanshu Karwariya, Sateesh Kumar Kumar, Rohan J Indian Soc Remote Sens Research Article 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. Springer India 2022-01-19 2022 /pmc/articles/PMC8767773/ http://dx.doi.org/10.1007/s12524-021-01487-3 Text en © Indian Society of Remote Sensing 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Research Article Kumar, Himanshu Karwariya, Sateesh Kumar Kumar, Rohan 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 |
title | 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 |
title_full | 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 |
title_fullStr | 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 |
title_full_unstemmed | 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 |
title_short | 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 |
title_sort | 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 |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8767773/ http://dx.doi.org/10.1007/s12524-021-01487-3 |
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