Cumulative Satellite detected waters extent overview between 13 & 26 March 2019 over Sofala province, Mozambique

This data illustrates the satellite detected surface waters in Manica, Sofala, and Tete Provinces in Mozambique, as observed from Sentinel-1 imagery acquired on 13, 14, 19 and 20 March 2019.

This map illustrates satellite-detected surface waters in Tete and Zambezia province, Mozambique as observed from Sentinel-1 imagery acquired on 7 April 2019. Within the analysed extent of about 7,800 sq km, a total about 100 sq km of lands appear to be flooded as of 7 April 2019. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT. Satellite Data (Post): Sentinel-1 Imagery Date (1): 7 April 2019 Imagery Date (2): 20 March 2019 Resolution: 10 m Copyright: Copernicus 2019 / ESA Source: ESA

**UNOSAT code TC20230221MOZ, GDACS Id: 1000961** This map illustrates satellite-detected surface waters in Sofala and Zambezia Provinces, Mozambique as observed from a Sentinel-1 image acquired on 17 Mar. 2023 at 03:09 local time. Within the analyzed area of about 27,000 km2, about 1,800 km2 of lands appear to be flooded. Water extent appears to have increased of about 900 km2 since the 13 March 2023. Based on Worldpop population data and the detected surface waters in the analyzed area, the potentially exposed population is mainly located in Zambezia province, including the district of Namacurra, with ~39,00 people, Nicoadala with ~32,000 people and Maganja Da Costa with ~ 23,000 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: TC20240313MOZ, GDACS ID: 1001053** This map illustrates satellite detected surface waters in Maputo province, Mozambique, as observed from a Sentinel-1 images acquired on 23 March 2024 at 05:19 local time (03:19 UTC). Within the analyzed areas of about 950 km², a total of about 10 km² of lands appear to be flooded. 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).

**UNOSAT code: TC20220124MOZ** This map illustrates satellite-detected surface waters in Malawi and Mozambique as observed from a Sentinel-1 image acquired on 02 February 2022 at 10:04 local time. The flood analysis were carried out by using an automated analysis with Artificial Intelligence based methods. Within the analyzed area of about 145,978 km2, about 859 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 districts of Mutarara, Cidade de Quelim and Morrumbala with respectively ~ 33,000 people, ~ 11,000 people, and with ~16,600 people. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-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: FL20251229MOZ** This map illustrates the flood extent derived from Sentinel-1 imagery acquired on 31 December 2025, using an automated machine-learning-based analysis. Within the analysed area of approximately 32,000 km², an estimated 930 km² of land appears to be affected by floodwaters. Based on WorldPop spatial demographic data, approximately 4,200 people are exposed to or living in close to the flooded areas. 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.