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1. Identificação
Tipo de ReferênciaArtigo em Revista Científica (Journal Article)
Sitemtc-m21d.sid.inpe.br
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identificador8JMKD3MGP3W34T/466DE92
Repositóriosid.inpe.br/mtc-m21d/2022/01.10.13.57   (acesso restrito)
Última Atualização2022:01.10.13.57.37 (UTC) simone
Repositório de Metadadossid.inpe.br/mtc-m21d/2022/01.10.13.57.37
Última Atualização dos Metadados2023:01.03.16.46.00 (UTC) administrator
DOI10.1016/j.rse.2021.112860
ISSN0034-4257
Chave de CitaçãoPahlevanSAABBBCGGFJKLLMHOPSVVR:2022:SiReSe
TítuloSimultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3
Ano2022
MêsMar.
Data de Acesso28 mar. 2024
Tipo de Trabalhojournal article
Tipo SecundárioPRE PI
Número de Arquivos1
Tamanho13703 KiB
2. Contextualização
Autor 1 Pahlevan, Nima
 2 Smith, Brandon
 3 Alikas, Krista
 4 Anstee, Janet
 5 Barbosa, Cláudio Clemente Faria
 6 Binding, Caren
 7 Bresciani, Mariano
 8 Cremelha, Bruno
 9 Giardino, Claudia
10 Gurlin, Daniela
11 Fernandez, Virginia
12 Jamet, Cédric
13 Kangro, Kersti
14 Lehmann, Moritz K.
15 Loisel, Hubert
16 Matshushita, Bunkei
17 Hà, Nguyên
18 Olmanson, Leif
19 Potvin, Geneviève
20 Simis, Stefan G. H.
21 VanderWoude, Andrea
22 Vantrepotte, Vincent
23 Ruiz-Verdù, Antonio
Identificador de Curriculo 1
 2
 3
 4
 5 8JMKD3MGP5W/3C9JGSB
Grupo 1
 2
 3
 4
 5 DIOTG-CGCT-INPE-MCTI-GOV-BR
Afiliação 1 NASA Goddard Space Flight Center
 2 NASA Goddard Space Flight Center
 3 University of Tartu
 4 Commonwealth Scientific and Industrial Research Organization (CSIRO)
 5 Instituto Nacional de Pesquisas Espaciais (INPE)
 6 Environment and Climate Change Canada
 7 National Research Council of Italy
 8 University of Sherbrooke
 9 National Research Council of Italy
10 Wisconsin Department of Natural Resources
11 Universidad la Republica
12 Univ. Littoral Côte d’Opale
13 University of Tartu
14 Xerra Earth Observation Institute and the University of Waikato
15 Univ. Littoral Côte d’Opale
16 University of Tsukuba
17 Vietnam National University
18 University of Minnesota
19 University of Sherbrooke
20 Plymouth Marine Laboratory
21 National Oceanic and Atmospheric Administration
22 Univ. Littoral Côte d’Opale
23 University of Valencia
Endereço de e-Mail do Autor 1 nima.pahlevan@nasa.gov
 2
 3
 4
 5 claudio.barbosa@inpe.br
RevistaRemote Sensing of Environment
Volume270
Páginase112860
Nota SecundáriaA1_INTERDISCIPLINAR A1_GEOCIÊNCIAS A1_ENGENHARIAS_I A1_CIÊNCIAS_BIOLÓGICAS_I A1_CIÊNCIAS_AMBIENTAIS A1_CIÊNCIAS_AGRÁRIAS_I A1_BIODIVERSIDADE
Histórico (UTC)2022-01-10 13:57:37 :: simone -> administrator ::
2022-01-10 13:57:37 :: administrator -> simone :: 2022
2022-01-10 14:02:01 :: simone -> administrator :: 2022
2022-07-08 16:51:11 :: administrator -> simone :: 2022
2022-12-20 13:32:11 :: simone -> administrator :: 2022
2023-01-03 16:46:00 :: administrator -> simone :: 2022
3. Conteúdo e estrutura
É a matriz ou uma cópia?é a matriz
Estágio do Conteúdoconcluido
Transferível1
Tipo do ConteúdoExternal Contribution
Tipo de Versãopublisher
Palavras-ChaveInland and coastal waters
Machine learning
MSI
OLCI
OLI
Water quality
ResumoConstructing multi-source satellite-derived water quality (WQ) products in inland and nearshore coastal waters from the past, present, and future missions is a long-standing challenge. Despite inherent differences in sensors spectral capability, spatial sampling, and radiometric performance, research efforts focused on formulating, implementing, and validating universal WQ algorithms continue to evolve. This research extends a recently developed machine-learning (ML) model, i.e., Mixture Density Networks (MDNs) (Pahlevan et al., 2020; Smith et al., 2021), to the inverse problem of simultaneously retrieving WQ indicators, including chlorophyll-a (Chla), Total Suspended Solids (TSS), and the absorption by Colored Dissolved Organic Matter at 440 nm (acdom(440)), across a wide array of aquatic ecosystems. We use a database of in situ measurements to train and optimize MDN models developed for the relevant spectral measurements (400800 nm) of the Operational Land Imager (OLI), MultiSpectral Instrument (MSI), and Ocean and Land Color Instrument (OLCI) aboard the Landsat-8, Sentinel-2, and Sentinel-3 missions, respectively. Our two performance assessment approaches, namely hold-out and leave-one-out, suggest significant, albeit varying degrees of improvements with respect to second-best algorithms, depending on the sensor and WQ indicator (e.g., 68%, 75%, 117% improvements based on the hold-out method for Chla, TSS, and acdom(440), respectively from MSI-like spectra). Using these two assessment methods, we provide theoretical upper and lower bounds on model performance when evaluating similar and/or out-of-sample datasets. To evaluate multi-mission product consistency across broad spatial scales, map products are demonstrated for three near-concurrent OLI, MSI, and OLCI acquisitions. Overall, estimated TSS and acdom(440) from these three missions are consistent within the uncertainty of the model, but Chla maps from MSI and OLCI achieve greater accuracy than those from OLI. By applying two different atmospheric correction processors to OLI and MSI images, we also conduct matchup analyses to quantify the sensitivity of the MDN model and best-practice algorithms to uncertainties in reflectance products. Our model is less or equally sensitive to these uncertainties compared to other algorithms. Recognizing their uncertainties, MDN models can be applied as a global algorithm to enable harmonized retrievals of Chla, TSS, and acdom(440) in various aquatic ecosystems from multi-source satellite imagery. Local and/or regional ML models tuned with an apt data distribution (e.g., a subset of our dataset) should nevertheless be expected to outperform our global model.
ÁreaSRE
ArranjoSimultaneous retrieval of...
Conteúdo da Pasta docacessar
Conteúdo da Pasta sourcenão têm arquivos
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4. Condições de acesso e uso
Idiomaen
Arquivo Alvopahlevan_2022.pdf
Grupo de Usuáriossimone
Grupo de Leitoresadministrator
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Visibilidadeshown
Política de Arquivamentodenypublisher allowfinaldraft24
Permissão de Leituradeny from all and allow from 150.163
Permissão de Atualizaçãonão transferida
5. Fontes relacionadas
Unidades Imediatamente Superiores8JMKD3MGPCW/46KUATE
Lista de Itens Citandosid.inpe.br/bibdigital/2022/04.03.22.23 1
sid.inpe.br/mtc-m21/2012/07.13.14.43.57 1
DivulgaçãoWEBSCI; PORTALCAPES; MGA; COMPENDEX; SCOPUS.
Acervo Hospedeirourlib.net/www/2021/06.04.03.40
6. Notas
Campos Vaziosalternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark mirrorrepository nextedition notes number orcid parameterlist parentrepositories previousedition previouslowerunit progress project rightsholder schedulinginformation secondarydate secondarykey session shorttitle sponsor subject tertiarymark tertiarytype url
7. Controle da descrição
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