Climate Change Studies through SWOT Phenomenology Research

Climate change is driving extreme spatial and temporal variability in surface water resources. This is particularly important for lake and wetland features, which have been under-characterized on the global scale. This under-characterization is largely due to the complex structural properties of these surfaces relative to available remote sensing data.  

The Surface Water and Ocean Topography Mission, as the first-of-its-kind 2D mapping and satellite interferometer using Ka-band SAR, was developed for mapping water surface extents and water surface elevations, providing a significant improvement in how we characterize and monitor surface water. Because of the novelty of the Ka-band SAR data for surface mapping, there have been limited studies of additional utilities SWOT can provide to complement water surface extent and elevation observations.  

First-look images from SWOT over Toulouse, France and Long Island, New York, USA, revealed strong signal returns over non-water surfaces, including agricultural fields and urban regions. Subsequent images highlighted by the SWOT Science Team also demonstrated wind-driven water surface signal variability, akin to NASA-JPL airborne AirSWOT investigations.  

This project provides early assessments of SWOT phenomenology for estimating characteristics that could contribute to novel datasets, such as wind speed, wind direction (for long wave formations), vegetation moisture, vegetation structure, and land surface moisture fraction. This work provides the foundation for a multi-year study to further develop the Ka-band Phenomenology Scattering Model (KaPS), and the wind model Ka-SWOT Model (Ka-SMOD), and will additionally discuss necessary reference datasets, models, and in-situ sampling necessary to complete this these assessments.

This project will increase the utility of the SWOT mission for studying diverse water and land features and significantly improve our understanding of fine-scale terrestrial hydrology. Given the relatively short temporal availability of the preliminary SWOT data, this work will focus on spatial variability across global sites, within the fast-sampling orbit, for observations taken for available dates in 2023. This preliminary analysis of the spatial and temporal variability of SWOT-derived phenomena aims to demonstrate how SWOT can be used in novel ways to study climate change.