@InProceedings{PahlevanAWSOSKMZ:2024:MaLeCe,
               author = "Pahlevan, Nima and Ashapure, Akash and Wainwright, William and 
                         Smith, Brandon and O'Shea, Ryan E. and Saranathan, Arun and Kabir, 
                         Sakib and Maciel, Daniel Andrade and Zhai, Pengwang",
          affiliation = "{Science Systems and Applications} and {Science Systems and 
                         Applications} and {Science Systems and Applications} and {Science 
                         Systems and Applications} and {Science Systems and Applications} 
                         and {University of Massachusetts Amherst} and {Science Systems and 
                         Applications} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {University of Maryland Baltimore County}",
                title = "A machine learning centered atmospheric correction model for multi 
                         and hyperspectral satellite observations",
            booktitle = "Proceedings...",
                 year = "2024",
         organization = "Ocean Sciences Meeting",
            publisher = "AGU",
             abstract = "A primary challenge in aquatic remote sensing is the development 
                         of a robust atmospheric correction (AC) method to estimate remote 
                         sensing reflectance (Rrs) products, defined as the ratio of 
                         water-leaving radiance to downwelling irradiance just above the 
                         water. Our AC method, based on a machine learning model referred 
                         to as Mixture Density Networks (MDNs), has been implemented and 
                         widely tested with 2000+ co-located Rrs matchups of Landsat-8/-9 
                         (Operational Land Imager; OLI) and Sentinel-2 (Multispectral 
                         Instrument; MSI) images over inland and nearshore coastal waters. 
                         Here, we describe an extension of this approach to the 
                         hyperspectral domain by generating products for the Hyperspectral 
                         Imager for the Coastal Ocean (HICO). Our processing system, termed 
                         Aquaverse, begins by leveraging a coupled ocean-atmosphere 
                         radiative transfer model to simulate Rayleigh-corrected 
                         reflectance ( ) for various imaging geometries and atmospheric 
                         conditions void of absorbing gases and Rayleigh effects, via in 
                         situ hyperspectral Rrs measurements. The in situ Rrs and simulated 
                         spectra are then resampled with HICO spectral response functions 
                         and subsequently used to train an ensemble of MDNs to retrieve Rrs 
                         from . Our trained ensemble is then applied to Rayleigh-corrected 
                         HICO imagery, which is validated with available in situ data in 
                         select lakes and coastal estuaries across the U.S. The performance 
                         of our models trained with hyperspectral data is expected to match 
                         that of Landsats OLI and Sentinel-2s MSI with median uncertainties 
                         ranging from ~ 16% (green bands) to ~ 35% (blue bands), indicating 
                         major improvements (i.e., ~2x in the blue bands) with respect to 
                         the performance of existing AC models for inland and coastal 
                         waters. Aquaverse-generated maps (Rrs and downstream products like 
                         chlorophyll-a, total suspended solids, and the absorption by 
                         colored dissolved organic matter) for several HICO images will be 
                         demonstrated and compared with those produced via other 
                         processors. Validation uncertainties of Aquaverse in fresh and 
                         coastal waters on HICO imagery will serve as a proxy for future 
                         global space-borne spectrometers.",
  conference-location = "New Orleans",
      conference-year = "18-23 Feb. 2024",
        urlaccessdate = "13 maio 2024"
}