| 1. Identificação | ||||
| Tipo de Referência | Artigo em Evento (Conference Proceedings) | |||
| Site | mtc-m21d.sid.inpe.br | |||
| Código do Detentor | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S | |||
| Identificador | 8JMKD3MGP3W34T/4APJ7L8 | |||
| Repositório | sid.inpe.br/mtc-m21d/2024/02.19.12.46 | |||
| Repositório de Metadados | sid.inpe.br/mtc-m21d/2024/02.19.12.46.21 | |||
| Última Atualização dos Metadados | 2024:02.28.10.36.28 (UTC) administrator | |||
| Chave Secundária | INPE--PRE/ | |||
| Chave de Citação | PahlevanAWSOSKMZ:2024:MaLeCe | |||
| Título | A machine learning centered atmospheric correction model for multi and hyperspectral satellite observations  | |||
| Ano | 2024 | |||
| Data de Acesso | 05 jun. 2024 | |||
| Tipo Secundário | PRE CI | |||
| 2. Contextualização | ||||
| Autor | 1 Pahlevan, Nima 2 Ashapure, Akash 3 Wainwright, William 4 Smith, Brandon 5 O'Shea, Ryan E. 6 Saranathan, Arun 7 Kabir, Sakib 8 Maciel, Daniel Andrade 9 Zhai, Pengwang | |||
| Grupo | 1 2 3 4 5 6 7 8 DIOTG-CGCT-INPE-MCTI-GOV-BR | |||
| Afiliação | 1 Science Systems and Applications 2 Science Systems and Applications 3 Science Systems and Applications 4 Science Systems and Applications 5 Science Systems and Applications 6 University of Massachusetts Amherst 7 Science Systems and Applications 8 Instituto Nacional de Pesquisas Espaciais (INPE) 9 University of Maryland Baltimore County | |||
| Endereço de e-Mail do Autor | 1 2 3 4 5 6 7 8 daniel.maciel@inpe.br | |||
| Nome do Evento | Ocean Sciences Meeting | |||
| Localização do Evento | New Orleans | |||
| Data | 18-23 Feb. 2024 | |||
| Editora (Publisher) | AGU | |||
| Título do Livro | Proceedings | |||
| Tipo Terciário | Poster Session | |||
| Histórico (UTC) | 2024-02-19 12:46:21 :: simone -> administrator :: 2024-02-28 10:36:28 :: administrator -> simone :: 2024 | |||
| 3. Conteúdo e estrutura | ||||
| É a matriz ou uma cópia? | é a matriz | |||
| Estágio do Conteúdo | concluido | |||
| Transferível | 1 | |||
| Tipo do Conteúdo | External Contribution | |||
| Resumo | 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. | |||
| Área | SRE | |||
| Arranjo 1 | urlib.net > BDMCI > Fonds > LabISA > A machine learning... | |||
| Arranjo 2 | urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGCT > A machine learning... | |||
| Conteúdo da Pasta doc | não têm arquivos | |||
| Conteúdo da Pasta source | não têm arquivos | |||
| Conteúdo da Pasta agreement |
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| 4. Condições de acesso e uso | ||||
| Grupo de Usuários | simone | |||
| Visibilidade | shown | |||
| Permissão de Atualização | não transferida | |||
| 5. Fontes relacionadas | ||||
| Repositório Espelho | urlib.net/www/2021/06.04.03.40.25 | |||
| Unidades Imediatamente Superiores | 8JMKD3MGPCW/439EAFB 8JMKD3MGPCW/46KUATE | |||
| Acervo Hospedeiro | urlib.net/www/2021/06.04.03.40 | |||
| 6. Notas | ||||
| Campos Vazios | archivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel dissemination doi e-mailaddress edition editor format isbn issn keywords label language lineage mark nextedition notes numberoffiles numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project publisheraddress readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarymark serieseditor session shorttitle size sponsor subject targetfile tertiarymark type url versiontype volume | |||
| 7. Controle da descrição | ||||
| e-Mail (login) | simone | |||
| atualizar | ||||