Spatiotemporal quantification of surface water and flooding is essential for research on hydrological cycles. Satellite remote sensing is the only means of monitoring these dynamics across vast…

This is a grant award from the Shared Services Center, a civilian U.S. government agency, to North Carolina State University (NC State) for a project titled "Spatiotemporal Quantification of Surface Water and Flooding". The $611,393 award will fund research to develop machine learning algorithms to map global flooding dynamics using multi-sensor optical and radar satellite data from Landsat 8, Sentinel 1, and Sentinel 2. The goal is to accurately capture the maximum extent of ephemeral and extreme flood events in near real-time, while addressing challenges like detecting water under vegetation and cloud cover. NC State, a prominent 1862 Land Grant institution, will leverage its expertise in environmental studies, remote sensing, and algorithm development to deliver this research. There is no set-aside designation, indicating NC State's competitive capabilities in federal research contracting. The award is not associated with a larger contract vehicle.

Spatiotemporal quantification of surface water and flooding is essential for research on hydrological cycles. Satellite remote sensing is the only means of monitoring these dynamics across vast areas and over time. Several regional to global surface water data sets have been developed using optical time-series, either from modis-type sensors with coarse spatial resolution but daily frequency, or based on the entire landsat archive. Despite its high spatial resolution, the 16-day repeat frequency of landsat means that short lived hazardous flooding and the maximum extent of large floods are likely missed. Meanwhile, spatially coarser modis-type sensors may miss small water bodies and floods entirely. In addition, two limitations when mapping inundation with optical data have been detecting water under vegetation and cloud obscuration, which often coincides with floods. Both issues can be overcome by fusing multiple optical with synthetic aperture radar (SAR) data, taking advantage of complementary observation properties including SAR s ability to penetrate through clouds. Thus, combining observations and spectral properties of the newly available sentinel 1 SAR (S1) and sentinel 2 (S2) series of satellites with landsat 8 (L8) holds promise for global surface water and flood mapping with improved spatial and temporal resolution and accuracy. To accurately capture maximum extent of all floods in near real time, the key objectives are to (1) map flooding dynamics globally, using machine learning applied to time-series of multi-sensor optical (L8, S2) and radar (S1) time series data, (2) assess the accuracy of the mapped flood extent, and (3) test the ability of our algorithms to map (a) ephemeral floods in a dynamic dryland river system (b) a complex delta including inundated vegetation in western canada (leveraging field validation data on extent of inundated vegetation collected during NASA s arctic boreal vulnerability experiment), (c) extreme flooding in north carolina (during hurricanes in 2016, 2018 and 2019), and (d) small water bodies (< 5HA) in irrigated areas (i.e. arkansas, the U.S. State with the 3RD largest irrigated area, where hundreds of small reservoirs have been constructed since this proposal is significant to this NASA solicitation as IT will enable improved quantification of flood extent dynamics and water quantity. The algorithms and maps produced can be used for better mapping of floods during hazardous conditions and assessment of how changes in land cover and land use and climate impact surface water and flood dynamics.2015).