DOI: 10.57760/sciencedb.20097
title: Seamless surface water dynamic database (15-days and 10m) for East Africa in 2017-2023
title: Seamless surface water dynamic database (15-days and 10m) for East Africa in 2017-2023

description: Citation: Wang, Z., Hao, Z., Yang, Q., Mapfumo, P., Nyakudya, E., Du, Y., Yan, X. & Ling, F. (2025). Mapping seamless surface water dynamics over East Africa semimonthly at a 10-meter resolution in 2017–2023 by integrating Sentinel-1/2 data. ISPRS Journal of Photogrammetry and Remote Sensing, 225, 440-460.This dataset provides a seamless, semimonthly 10-meter resolution surface water dataset over East Africa (EA), covering countries such as Tanzania, Uganda, Ethiopia, and others. It was generated using a novel surface water mapping framework specifically designed for Sentinel-1 and Sentinel-2 data.The dataset is organized based on a 1-degree grid system (refer to the GridForData file) and subsequently aggregated within national boundaries. The files are named according to the following convention:eaYear_Month_Day_idYear: Year of acquisition, ranging from 2017 to 2023.Month: Month of acquisition, from 1 to 12.Day: Indicates whether the data was acquired in the first or second half of the month. "01" denotes the first half, and "15" denotes the second half.id: Refers to the grid ID specified in the GridForData file.This dataset offers accurate surface water delineation across East Africa. However, some uncertainties remain:Speckle noise from Sentinel-1 imagery can lead to misclassifications, even after applying filters. These misclassifications are more frequent in the northern parts of EA.A very small amount of data loss occurred during the complex processing workflow.To address these issues, we manually inspected the dataset. Surface water maps with excessive misclassifications or significant data loss were removed. Nevertheless, we recommend using this dataset primarily for analyzing local or national-scale surface water dynamics, rather than as a single-phase surface water reference map.P.S. The Eritrea data will be uploaded later, as we have splited these data into two parts.Don't forget to cite the paper if using this dataset!
description: Citation: Wang, Z., Hao, Z., Yang, Q., Mapfumo, P., Nyakudya, E., Du, Y., Yan, X. & Ling, F. (2025). Mapping seamless surface water dynamics over East Africa semimonthly at a 10-meter resolution in 2017–2023 by integrating Sentinel-1/2 data. ISPRS Journal of Photogrammetry and Remote Sensing, 225, 440-460.This dataset provides a seamless, semimonthly 10-meter resolution surface water dataset over East Africa (EA), covering countries such as Tanzania, Uganda, Ethiopia, and others. It was generated using a novel surface water mapping framework specifically designed for Sentinel-1 and Sentinel-2 data.The dataset is organized based on a 1-degree grid system (refer to the GridForData file) and subsequently aggregated within national boundaries. The files are named according to the following convention:eaYear_Month_Day_idYear: Year of acquisition, ranging from 2017 to 2023.Month: Month of acquisition, from 1 to 12.Day: Indicates whether the data was acquired in the first or second half of the month. "01" denotes the first half, and "15" denotes the second half.id: Refers to the grid ID specified in the GridForData file.This dataset offers accurate surface water delineation across East Africa. However, some uncertainties remain:Speckle noise from Sentinel-1 imagery can lead to misclassifications, even after applying filters. These misclassifications are more frequent in the northern parts of EA.A very small amount of data loss occurred during the complex processing workflow.To address these issues, we manually inspected the dataset. Surface water maps with excessive misclassifications or significant data loss were removed. Nevertheless, we recommend using this dataset primarily for analyzing local or national-scale surface water dynamics, rather than as a single-phase surface water reference map.P.S. The Eritrea data will be uploaded later, as we have splited these data into two parts.Don't forget to cite the paper if using this dataset!