| 1. Identificação | |
| Tipo de Referência | Artigo em Revista Científica (Journal Article) |
| Site | plutao.sid.inpe.br |
| Código do Detentor | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S |
| Identificador | 8JMKD3MGP3W/44SJTKE |
| Repositório | sid.inpe.br/plutao/2021/06.16.16.50 (acesso restrito) |
| Última Atualização | 2021:06.17.13.17.25 (UTC) lattes |
| Repositório de Metadados | sid.inpe.br/plutao/2021/06.16.16.50.57 |
| Última Atualização dos Metadados | 2024:04.17.08.12.13 (UTC) administrator |
| DOI | 10.3389/frsen.2020.623678 |
| ISSN | 2673-6187 |
| Rótulo | lattes: 1596449770636962 9 SmithPSREMGBBMFAK:2021:ChAlLa |
| Chave de Citação | SmithPSREMGBBMFAK:2021:ChAlLa |
| Título | A Chlorophyll-a Algorithm for Landsat-8 Based on Mixture Density Networks  |
| Ano | 2021 |
| Data de Acesso | 04 maio 2024 |
| Tipo de Trabalho | journal article |
| Tipo Secundário | PRE PI |
| Número de Arquivos | 1 |
| Tamanho | 4876 KiB |
| 2. Contextualização | |
| Autor | 1 Smith, Brandon 2 Pahlevan, Nima 3 Schalles, John 4 Ruberg, Steve 5 Errera, Reagan 6 Ma, Ronghua 7 Giardino, Claudia 8 Bresciani, Mariano 9 Barbosa, Cláudio Clemente Faria 10 Moore, Tim 11 Fernández, Virginia 12 Alikas, Krista 13 Kangaro, Kersti |
| Identificador de Curriculo | 1 2 3 4 5 6 7 8 9 8JMKD3MGP5W/3C9JGSB |
| Grupo | 1 2 3 4 5 6 7 8 9 DIOTG-CGCT-INPE-MCTI-GOV-BR |
| Afiliação | 1 NASA Goddard Space Flight Center 2 NASA Goddard Space Flight Center 3 Creighton University 4 NOAA 5 NOAA 6 Chinese Academy of Science 7 National Research Council of Italy 8 National Research Council of Italy 9 Instituto Nacional de Pesquisas Espaciais (INPE) 10 Florida Atlantic University 11 University of the Republic 12 University of Tartu 13 University of Tartu |
| Endereço de e-Mail do Autor | 1 2 3 4 5 6 7 8 9 claudio.barbosa@inpe.br |
| Revista | Frontiers in Remote Sensing |
| Volume | 1 |
| Páginas | e623678 |
| Histórico (UTC) | 2021-06-17 13:17:25 :: lattes -> administrator :: 2021 2024-04-17 08:12:13 :: administrator -> simone :: 2021 |
| 3. Conteúdo e estrutura | |
| É a matriz ou uma cópia? | é a matriz |
| Estágio do Conteúdo | concluido |
| Transferível | 1 |
| Tipo do Conteúdo | External Contribution |
| Tipo de Versão | publisher |
| Palavras-Chave | machine learning Landsat-8 Chlorophyll-a Inland Waters aquatic remote sensing |
| Resumo | Retrieval of aquatic biogeochemical variables, such as the near-surface concentration of chlorophyll-a (Chla) in inland and coastal waters via remote observations, has long been regarded as a challenging task. This manuscript applies Mixture Density Networks (MDN) that use the visible spectral bands available by the Operational Land Imager (OLI) aboard Landsat-8 to estimate Chla. We utilize a database of co-located in situ radiometric and Chla measurements (N 4,354), referred to as Type A data, to train and test an MDN model (MDNA). This algorithms performance, having been proven for other satellite missions, is further evaluated against other widely used machine learning models (e.g., support vector machines), as well as other domain-specific solutions (OC3), and shown to offer significant advancements in the field. Our performance assessment using a held-out test data set suggests that a 49% (median) accuracy with near-zero bias can be achieved via the MDNA model, offering improvements of 20 to 100% in retrievals with respect to other models. The sensitivity of the MDNA model and benchmarking methods to uncertainties from atmospheric correction (AC) methods, is further quantified through a semi-global matchup dataset (N 3,337), referred to as Type B data. To tackle the increased uncertainties, alternative MDN models (MDNB) are developed through various features of the Type B data (e.g., Rayleigh-corrected reflectance spectra ρs). Using heldout data, along with spatial and temporal analyses, we demonstrate that these alternative models show promise in enhancing the retrieval accuracy adversely influenced by the AC process. Results lend support for the adoption of MDNB models for regional and potentially global processing of OLI imagery, until a more robust AC method is developed. Index TermsChlorophyll-a, coastal water, inland water, Landsat-8, machine learning, ocean color, aquatic remote sensing. |
| Área | SRE |
| Arranjo 1 | urlib.net > BDMCI > Fonds > LabISA > A Chlorophyll-a Algorithm... |
| Arranjo 2 | urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGCT > A Chlorophyll-a Algorithm... |
| Conteúdo da Pasta doc | acessar |
| Conteúdo da Pasta source | não têm arquivos |
| Conteúdo da Pasta agreement | não têm arquivos |
| 4. Condições de acesso e uso | |
| Idioma | en |
| Arquivo Alvo | smith_chlorophyll.pdf |
| Grupo de Usuários | lattes |
| Grupo de Leitores | administrator lattes |
| Visibilidade | shown |
| Permissão de Leitura | deny from all and allow from 150.163 |
| Permissão de Atualização | não transferida |
| 5. Fontes relacionadas | |
| Unidades Imediatamente Superiores | 8JMKD3MGPCW/439EAFB 8JMKD3MGPCW/46KUATE |
| Lista de Itens Citando | sid.inpe.br/bibdigital/2020/09.18.00.06 2 sid.inpe.br/mtc-m21/2012/07.13.14.43.57 1 |
| Acervo Hospedeiro | dpi.inpe.br/plutao@80/2008/08.19.15.01 |
| 6. Notas | |
| Notas | Setores de Atividade: Pesquisa e desenvolvimento científico. |
| Campos Vazios | alternatejournal archivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel dissemination e-mailaddress format isbn lineage mark mirrorrepository month nextedition number orcid parameterlist parentrepositories previousedition previouslowerunit progress project rightsholder schedulinginformation secondarydate secondarykey secondarymark session shorttitle sponsor subject tertiarymark tertiarytype url |
| 7. Controle da descrição | |
| e-Mail (login) | simone |
| atualizar | |