资源环境科技发展态势分析平台

This study develops a new, highly efficient method to produce accurate, high-resolution surface water maps. The "active-passive surface water classification" method leverages cloud-based computing resources and machine learning techniques to merge Sentinel 1 synthetic aperture radar and Landsat observations and generate monthly 10-m-resolution water body maps. The skill of the active-passive surface water classification method is demonstrated by mapping surface water change over the Awash River basin in Ethiopia during the 2015 East African regional drought and 2016 localized flood events. Errors of omission (water incorrectly classified as nonwater) and commission (nonwater incorrectly classified as water) in the case study area are 7.16% and 1.91%, respectively. The case study demonstrates the method's ability to generate accurate, high-resolution water body maps depicting surface water dynamics in data-sparse regions. The developed technique will facilitate better monitoring and understanding of the impact of environmental change and climate extremes on global freshwater ecosystems.

Plain Language Summary Freshwater is one of the world's most important natural resources. However, data scarcity in many parts of the world is a substantial barrier to understanding and predicting the impact of environmental changes on freshwater ecosystems. Satellites provide data on water resources in data-poor regions. The "active-passive surface water classification" method merges observations from two types of sensors, passive sensors from National Aeronautics and Space Administration's Landsat satellites and radar sensors from Sentinel 1 satellites, to generate accurate, high-resolution water body maps. The technique is demonstrated by mapping surface water over the Awash River basin, Ethiopia, during the 2015 East African drought and 2016 flood events. Our results show the severe impact of the drought on basin water bodies, basin recovery to predrought conditions after the 2016 summer rains, and localized flooding from these rains. Our study is one of the first to generate water body maps by merging Landsat and Sentinel 1 data and use them to analyze spatial and temporal water body changes for monthly periods at a 10-m resolution. High-resolution maps of temporal surface water change will help water managers better target humanitarian response efforts, inform water policy decisions, and improve understanding of the impact of environmental change and climate extremes on freshwater ecosystems.