This page lists past seminars and presentations by STAR
scientists and visiting scientists. These seminars include the STAR
Science Forum and similar events. Presentation materials for
seminars will be provided when available.
Title: Navigating the unknown: enhancing aquatic remote sensing products through uncertainty
Presenter(s): Mortimer Werther, EAWAG
Sponsor(s): NOAA Ocean Color Coordinating Group (NOCCG)
Seminar Contact(s): Veronica.Lance@NOAA.gov
Abstract: Uncertainty is a fundamental aspect of aquatic remote sensing, generated by sources such as sensor noise, atmospheric conditions, and human error. Despite scientific advancements, these uncertainties are often neglected in remote sensing studies, leading to misinterpretations and missed opportunities for innovation. This seminar recaps key findings from our recent paper, "Dive Into the Unknown: Embracing Uncertainty to Advance Aquatic Remote Sensing," and explores the sources of uncertainty in aquatic remote sensing, their theoretical background, and practical applications. We will provide examples in validation, model improvement, and decision-making. By embracing uncertainty, we aim to enhance the accuracy and reliability of remote sensing products, drive scientific progress, and support informed decision-making. Join us to discover why integrating uncertainty should be central to aquatic remote sensing efforts, leading to scientific advancement and more reliable Earth observation products.
Bio(s): Dr. Mortimer Werther holds a BSc in Geography from Kiel University, Germany, an MSc in Geo Information Science from Wageningen University, The Netherlands, and a PhD in Remote Sensing from the University of Stirling, Scotland (2022). He has worked at Brockmann Consult in Germany and has been a Postdoctoral researcher in Daniel Odermatt's group at the Swiss Federal Institute of Aquatic Science and Technology (EAWAG) since September 2022. His research focuses on aquatic optics, remote sensing, and calibration/validation activities. Dr. Werther specializes in applying remote sensing technologies to aquatic ecosystems, with a particular emphasis on understanding and incorporating uncertainty into remote sensing models.
Abstract: Global ocean phytoplankton growth and primary production are intimately linked to nutrient fluctuations from seasonal to millennial time scales. Rapid recycling and tight predator-prey coupling compromise the utility of nutrient or phytoplankton stocks for delineating the biogeography of global ocean nutrient stress. Here, field-measured omics biomarkers of nutritional status are coupled to a satellite remote sensing metric of cell physiology to mechanistically evaluate monthly to multi-decadal shifts in global phytoplankton nutrient stress. We observe a clear biogeography in nutrient stress aligned with variations in nutrient flux rate and distinctly elevated stress in nitrogen- compared to phosphate-limited waters. Temporal modes of stress are dominated by seasonal changes, but strong signatures of natural climate cycles are also apparent. However, a nearly ubiquitous surface ocean warming over the last twenty years is linked to a globally-consistent increase in nutrient stress. Thus, our integrated genomic and satellite remote sensing of phytoplankton physiology has uncovered a clear contemporary regulation of global ocean nutrient stress.
Title: A UN Ocean Decade Program, co-led by NOAA, Linking Air-Sea Interaction In Situ Observations, Satellites and Earth System Models for A Predicted, Safe, Healthy, Clean, and Productive Ocean
Presenter(s): Meghan Cronin (OAR)
Abstract: A look at NOAA activities related to the UN Ocean Decade program "Observing Air-Sea Interactions Strategy". Covering Ocean Climate Stations, OceanSITES longterm ocean surface mooring timeseries that can be used for assessing satellites, and also a new Tropical Pacific Observing System (TPOS) Equatorial Pacific Experiment (TEPEX) on the horizon. Discussion will cover how these observations might be used to help improve satellites and quantify uncertainties, how these are then linked to improved models and ultimately to the UN Decade goals of a Predicted Ocean, Safe Ocean, etc.
Title: Using a Generalized Additive Model to Compute Bias-corrected Near-surface Bulk Salinities from Satellite-derived Skin Salinities in the Arctic Ocean and Subarctic Seas
Abstract: This study addresses limitations in Arctic Ocean salinity measurements by utilizing in situ and satellite data, employing a machine-learning approach (Generalized Additive Model; GAM), to convert satellite-derived skin salinity to near-surface (0-5 m) bulk salinity. This research addresses satellite salinity high-latitude retrieval biases, enables the assimilation of those high-latitude satellite salinity observations into numerical modeling, and contributes to validating, verifying, and operationalizing the National Oceanographic and Atmospheric Administration's Unified Forecast System's global coupled model.
Abstract: Satellite constellations such as Sentinel-2A and -2B, Sentinel-3A and -3B, and Planet's PlanetScope constellation offer increased temporal resolution while maintaining spatial, spectral, and radiometric resolutions. For most satellite constellations currently in orbit, platforms are launched either in a group or individually, typically across several years. This increases sampling frequency throughout the satellite mission's lifespan and presents the opportunity to observe more extreme events. When assessing long-term trends or year-over-year change, increased sampling frequency can lead to observed changes that are incorrectly attributed to changes in environmental conditions. This study uses water quality data from the Copernicus Sentinel-3 satellite series to assess temporal aggregation methods for multi-platform satellite missions and their impact on resulting data distributions and change assessments. Temporal aggregation via the maximum data value and via the median data value were compared via the Wilcoxon signed-rank test for a simulation study and for water quality data produced by the Cyanobacteria Assessment Network (CyAN). Next, trends in water quality data were assessed for each temporal aggregation approach using the seasonal Mann-Kendall test for trend and associated Thiel-Sen slope. Results can inform large-scale, long-term water quality monitoring efforts and applications that combine multiple satellite missions, including sensor agnostic workflows.
