Citation:

Vanderhoof, M., L. Alexander, J. Christensen, K. Solvik, P. Nieuwlandt, AND M. Sagehorn. High-frequency time series comparison of Sentinel-1 and Sentinel-2 satellites for mapping open and vegetated water across the United States (2017–2021). REMOTE SENSING OF ENVIRONMENT. Elsevier Science Ltd, New York, NY, 288:113498, (2023). https://doi.org/10.1016/j.rse.2023.113498

Impact/Purpose:

This Product was motivated by OW’s request for additional data sources to supplement current desktop- and field-based methods for jurisdictional and water management decisions by states, tribes, and federal agencies. Headwater streamflow and wetland inundation dynamics have important implications for ecosystem functioning and downstream water quality. However, many high-functioning headwaters and wetlands remain prone to disturbance, due in part to scarcity of geospatial data representing surface water dynamics at scales needed to fully implement Clean Water Act policies and programs. Additional information that can be gained through remote sensing and modeling approaches can help to fill existing data gaps and would aid regulatory and management decisions. The launch of high-resolution (<= 20m) satellites with short (5-12 day) effective return intervals has greatly increased opportunities for data acquisition in dynamic surface water systems. Prior studies have demonstrated the potential of these sensors for water applications, but most have focused on open water features and been too limited in spatial extent to scale efforts or understand how classification accuracy for an algorithm developed at one site might change in other ecosystems, climates, and hydrological systems. o   Partners: Office of Water’s Office of Wetlands, Oceans, and Watersheds (OWOW) is the lead partner which requested the research. We expect that OWOW will be the primary user of the research but other federal and state agencies should find the information useful. Rose Kwok (OWOW) has been an integrated partner in this work.

Description:

Frequent observations of surface water at fine spatial scales will provide critical data to support the management of aquatic habitat, flood risk and water quality. Sentinel-1 and Sentinel-2 satellites can provide such observations, but algorithms are still needed that perform well across diverse climate and vegetation conditions. We developed surface inundation algorithms for Sentinel-1 and Sentinel-2, respectively, at 12 sites across the conterminous United States (CONUS), covering a total of >536,000 km2 and representing diverse hydrologic and vegetation landscapes. Each scene in the 5-year (2017–2021) time series was classified into open water, vegetated water, and non-water at 20 m resolution using variables from Sentinel-1 and Sentinel-2, as well as variables derived from topographic and weather datasets. The Sentinel-1 algorithm was developed distinct from the Sentinel-2 model to explore if and where the two time series could potentially be integrated into a single high-frequency time series. Within each model, open water and vegetated water (vegetated palustrine, lacustrine, and riverine wetlands) classes were mapped. The models were validated using imagery from WorldView and PlanetScope. Classification accuracy for open water was high across the 5-year period, with an omission and commission error of only 3.1% and 0.9% for the Sentinel-1 algorithm and 3.1% and 0.5% for the Sentinel-2 algorithm, respectively. Vegetated water accuracy was lower, as expected given that the class represents mixed pixels. The Sentinel-2 algorithm showed higher accuracy (10.7% omission and 7.9% commission error) relative to the Sentinel-1 algorithm (28.4% omission and 16.0% commission error). Patterns over time in the proportion of area mapped as open or vegetated water by the Sentinel-1 and Sentinel-2 algorithms were charted and correlated for a subset of all 12 sites. Our results showed that the Sentinel-1 and Sentinel-2 algorithm open water time series can be integrated at all 12 sites to improve the temporal resolution, but sensor-specific differences, such as sensitivity to vegetation structure versus pixel color, complicate the data integration for mixed-pixel, vegetated water. The methods developed here provide inundation at 5-day (Sentinel-2 algorithm) and 12-day (Sentinel-1 algorithm) time steps to improve our understanding of the short- and long-term response of surface water to climate and land use drivers in different ecoregions.

URLs/Downloads:

Record Details: