Product ID: 3565 - English
Published: 22 Jun, 2022
GLIDE: FL20220525BGD

Product Links
PDF (10.5MB)     - Static viewing and printing
Shapefile          - Download a Shapefile of data
WebMap          - Dynamic viewing in a browser
Geodatabase   - Download data in the ESRI format
Table                 - Excel

This map illustrates satellite-detected surface waters in Rajshahi, Rangur, Mymensingh, Dhaka and Khulna Divisions, Bangladesh as observed from a Sentinel-1 images acquired on 21 Jun. 2022 at 18:05 local time and using an automated analysis with machine learning method. Within the analyzed area of about 20,400 km2, about 2,950 km2 of lands appear to be flooded. Water extent appears to have increased of about 2,360 km2 since 9 Jun. 2022.
Based on Worldpop population data and the detected surface waters in the analyzed area, the potentially exposed population is mainly located in the division of Rajshahi with ~1,335,000 people, Dhaka with 674,000 people,and Mymensingh with ~ 640,000 people.
This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT).
Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.
Satellite Data (1): Sentinel-1
Imagery Dates: 21 Jun. 2022 at 12:05 UTC
Satellite Data (2): Sentinel-1
Imagery Dates: 09 Jun. 2022 at 12:05 UTC
Resolution: 10 m
Copyright: Contains modified Copernicus Sentinel Data [2021]
Source: ESA
Administrative boundaries: : Bangladesh Bureau of Statistics (BBS)
Population data: WorldPop [2020]
Populated place: OpenStreetMap
Road data: OpenStreetMap
Waterways: OpenStreetMap
Permanent Water: ESA WorldCover and JRC
Copyright: © ESA WorldCover project 2020 / Contains modified Copernicus Sentinel data (2020) processed by ESA WorldCover consortium?
Background: ESRI World Imagery
Insets: Sentinel-2/21 May 2022 & 20 Jun. 2022
Analysis: United Nations Satellite Centre (UNOSAT) Machine learning method
Production: United Nations Satellite Centre (UNOSAT)