Day 4

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Paper title Supporting data-intensive algorithm development approaches through a globally representative hyperspectral in situ dataset from inland and coastal waters: A community-initiative
  1. Daniela Gurlin Wisconsin Department of Natural Resources Speaker
  2. Moritz K. Lehmann Xerra Earth Observation Institute and the University of Waikato, New Zealand
  3. Nima Pahlevan NASA Goddard Space Flight Center
  4. Brandon Smith NASA Goddard Space Flight Center, Greenbelt, MD, USA and Science Systems and Applications Inc., Lanham, MD, USA
  5. Krista Alikas Tartu Observatory, University of Tartu, Estonia
  6. Janet Anstee CSIRO Oceans and Atmosphere
  7. Claudio Barbosa Brazilian National Institute for Space Research - INPE
  8. Caren Binding Environment and Climate Change Canada, Burlington, ON, Canada
  9. Mariano Bresciani CNR (National Research Council of Italy)
  10. Nathan Drayson CSIRO Oceans and Atmosphere Flagship
  11. Virginia Fernandez Universidad de la República de Uruguay
  12. Cédric G. Fichot Boston University
  13. Claudia Giardino National Research Council of Italy, Institute for Electromagnetic Sensing of the Environment, CNR-IREA
  14. Anatoly, A. Gitelson University of Nebraska-Lincoln
  15. Steven R. Greb GEO AquaWatch
  16. Cédric Jamet LOG/ULCO
  17. Dalin Jiang University of Stirling
  18. Kersti Kangro Tartu Observatory, University of Tartu
  19. Lin Li Department of Earth Sciences, Indiana University-Purdue University, Indianapolis, IN, USA
  20. Hubert Loisel Laboratoire d'Océanologie et de Géosciences
  21. Bunkei Matsushita University of Tsukuba
  22. Deepak R. Mishra University of Georgia
  23. Tim Moore Florida Atlantic University
  24. Wesley J. Moses U.S. Naval Research Laboratory
  25. Hà Nguyễn VNU University of Science
  26. Leif Olmanson University of Minnesota
  27. Michael Ondrusek NOAA Center for Satellite Applications and Research
  28. Natascha Oppelt Kiel University
  29. Antonio Ruiz-Verdú Image Processing Laboratory (IPL) - University of Valencia (Spain)
  30. John F. Schalles Creighton University
  31. Stefan Simis Plymouth Marine Laboratory
  32. Andrea VanderWoude Great Lakes Environmental Research Laboratory, NOAA, Ann Arbor, MI, USA
  33. Vincent Vantrepotte Université du Littoral Côte d'Opale, Université de Lille, and Laboratoire d'Océanologie et de Géosciences
Form of presentation Poster
  • A7. Hydrology and Water Cycle
    • A7.06 EO for monitoring water quality and ecological status in inland waters
Abstract text Large and globally representative in situ datasets are critical for the development of globally validated bio-optical algorithms to support comprehensive water quality monitoring and change detection using satellite Earth observation technologies. Such datasets are particularly scarce and geographically fragmented from inland and coastal waters. This is at odds with the importance of these waters for supporting human livelihoods, biodiversity, and cultural and recreational values. These shortcomings create two challenges. The first and major challenge is to collate these datasets and assess their compatibility concerning methodologies used and quality control procedures applied. The second challenge is to identify biases and gaps in the global dataset, in order to better direct future data collection efforts.

Our ongoing effort is to improve the availability of such datasets by providing open access to a large global collection of hyperspectral remote sensing reflectance spectra and concurrently measured Secchi depth, chlorophyll-a (Chla), total suspended solids (TSS), and absorption by colored dissolved organic matter (acdom). This dataset represents an expansion of data originally collated for a collaborative NASA-ESA-led exercise to assess the performance of atmospheric correction processors over inland and coastal waters (ACIX-Aqua). Its suitability for the development of globally applicable algorithms has been demonstrated by its use for developing novel approaches for the retrieval of Chla and TSS concentrations from a range of satellite sensors.

Our dataset contains relevant entries from the commonly used SeaWiFS Bio-optical Archive and Storage System (SeaBASS) and Lake Bio-optical Measurements and Matchup Data for Remote Sensing (LIMNADES) data archives and, in return, contributes thousands of new entries to these and other repositories. It encompasses data from inland and coastal waters distributed across five continents and a comprehensive range of optical water types. Our accompanying biogeographical data analysis contributes to a value-added dataset to aid in the identification of underrepresented geographical locations and optical water types, useful for targeting future data collection efforts.

To ensure the ease of use of this dataset and support the analysis of uncertainties and algorithm development, metadata covering the viewing geometry and environmental conditions were included in addition to hundreds of matched scene IDs for a number of multispectral satellite sensors (e.g. roughly 450 clear-sky match-ups for Landsat 8’s Operational Land Imager (OLI)), making it easier to validate algorithm performance in practical applications.

In curating this dataset, we had to overcome considerable challenges, including technical difficulties, such as variable measurement ranges of instruments, and others due to the fact that the data originated from a community-initiative of multinational researchers working on projects with a diverse range of objectives. Substantial data harmonization efforts to align different instrumentation, field methodologies, and processing routines were needed.

We conclude, our effort was a very worthwhile undertaking as demonstrated by a series of novel contributions and the publication of eight peer-reviewed research articles (at the time of writing). We expect that open access to this dataset will support the development of increasingly data-intensive algorithms for the retrieval of water quality indicators, including those for next-generation hyperspectral satellite sensors, e.g. sensors from the upcoming Surface Biology and Geology (SBG), Environmental Mapping and Analysis Program (EnMap), PRecursore IperSpettrale della Missione Applicativa (PRISMA) Second Generation (PSG), Copernicus Hyperspectral Imaging Mission for the Environment (CHIME), and FLuorescence EXplorer (FLEX) missions. We believe that this will stimulate the discussion of a framework for the future collection of fiducial reference data towards global representativeness.