Satellite detected water extent over Rajshahi, Rangpur, Mymensingh and Khulna Divisions, Bangladesh as of 21 June 2022

**UNOSAT code: FL20240502BGD, GDACS ID: 1001064** This map illustrates satellite-detected surface waters in Barisal, Dhaka and Khulna Divisions, Bangladesh as observed from a Sentinel-1 images acquired on 1 June 2024 at 05:56 local time and using an automated analysis with machine learning method. Within the analyzed area of about 52,000 km2, about 1,000 km2 of land appears to be flooded. Based on Worldpop population data and the detected surface waters in the analyzed area, the potentially exposed population is mainly located in the district of Bagerhat, Khulna Division with ~96,000 people, and Barguna, Barisal Division with ~74,000 people. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the 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.

**UNOSAT code: FL20220525BGD** This map illustrates satellite-detected surface waters in Rangpur Division, 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 12,400 km2, about 1,200 km2 of lands appear to be flooded. Based on Worldpop population data and the detected surface waters in the analyzed area, the potentially exposed population is mainly located in the district of Kurigram with ~537,000 people, and Gaibandha with ~355,000 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.

**UNOSAT code: FL20220525BGD** This map illustrates satellite-detected surface waters in Sylhet, Mymensingh, Dhaka, and Chattogram Divisions, Bangladesh as observed from a Sentinel-1 images acquired on 26 May 2022 at 05:47 local time and using an automated analysis with machine learning method. Within the analyzed area of about 16,000 km2, about 6,800 km2 of lands appear to be flooded. In this area, about 5,400 km2 of croplands and 1,270 km2 of herbaceous wetland appear to be likely affected by the flood waters. Based on Worldpop population data and the detected surface waters in the analyzed area, the potentially exposed population is mainly located in the district of Sunamganj with ~1,700,000 people and Sylhet with ~1,400,000 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.

**UNOSAT code: FL20220525BGD** This map illustrates satellite-detected surface waters in Sylhet, Mymensingh, Dhaka, and Chattogram Divisions, Bangladesh as observed from a Sentinel-1 images acquired on 28 May 2022 at 18:04 local time and using an automated analysis with machine learning method. Within the analyzed area of about 12,000 km2, about 4,500 km2 of lands appear to be flooded. In this area, about 3,400 km2 of croplands and 1,000 km2 of herbaceous wetland appear to be likely affected by the flood waters. Based on Worldpop population data and the detected surface waters in the analyzed area, the potentially exposed population is mainly located in the district of Sunamganj with ~935,000 people and Kishoreganj with ~779,000 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.

**UNOSAT code: FL20220525BGD** This map illustrates satellite-detected surface waters in Sylhet, Mymensingh, Dhaka, and Chattogram Divisions, Bangladesh as observed from a RCM-1 images acquired on 19 Jun. 2022 at 18:02 local time. Within the analyzed area of about 22,300 km2, about 9,500 km2 of lands appear to be flooded. Water extent appears to have increased of about 2,150 km2 since the period between 25 to 28 May 2022. Based on Worldpop population data and the detected surface waters in the analyzed area, the potentially exposed population is mainly located in the district of Sunamganj with ~1,822,000 people and Sylhet with ~1,550,000 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.

**UNOSAT code: FL20240502BGD, GDACS ID: 1102700** This map illustrates the aggregated floods using Sentinel-1 images acquired between 22 to 29 June 2024 and between 4 to 11 July 2024 using an automated analysis with machine learning method. Within the analysed area of about 140,000 km², a total of about 8,700 km² of land appear to be affected by flood waters. Based on Worldpop spatial demographic data, about 7.7 million people are exposed or living close to flooded areas. Between these two periods, the floodwaters have increased about 2,900 km2. The increase has been mainly observed within Rangpur and Rajshahi divisions accordingly. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the 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 the backscattering properties of the radar signal.