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Tipo da ReferênciaBook Section
Identificador8JMKD3MGP3W34P/3LRKB5B
Repositóriosid.inpe.br/mtc-m21b/2016/06.09.19.27   (acesso restrito)
Metadadossid.inpe.br/mtc-m21b/2016/06.09.19.27.16
Sitemtc-m21b.sid.inpe.br
DOI10.5772/61869
ISBN978-953-51-2444-3
978-953-51-2443-6
Chave SecundáriaINPE--/
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Chave de CitaçãoOgashawaraCurtAraúStec:2016:BiMoTr
Autor1 Ogashawara, Igor
2 Curtarelli, Marcelo Pedroso
3 Araújo, Carlos Alberto Sampaio de
4 Stech, José Luiz
Identificador de Curriculo1
2
3
4 8JMKD3MGP5W/3C9JHHN
Grupo1
2 DSR-OBT-INPE-MCTI-GOV-BR
3 DSR-OBT-INPE-MCTI-GOV-BR
4 DSR-OBT-INPE-MCTI-GOV-BR
Afiliação1 Indiana University
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 Instituto Nacional de Pesquisas Espaciais (INPE)
4 Instituto Nacional de Pesquisas Espaciais (INPE)
Endereço de e-Mail do Autor1
2 mpedroso@dsr.inpe.br
3 carlos@dsr.inpe.br
4 jose.stech@inpe.br
TítuloBio-optical modeling in a tropical hypersaline lagoon environment
Título do LivroEnvironmental applications of remote sensing
Ano2016
EditorMarghany, Maged
Editora (Publisher)InTech
Palavras-ChaveWater quality, chlorophyll-a, turbidity, bio-optical modeling.
ResumoIn this chapter, we attempted to present an overview of the use of remote sensing to mon‐ itor water quality parameters, mainly chlorophyll-a (chl-a) and turbidity. We summarized the main concepts of bio-optical modeling and presented a case study of the application of the Hyperspectral Imager for the Coastal Ocean (HICO) for the monitoring of water quality in a tropical hypersaline aquatic environment. Using HICO, we evaluated a set of different semi-empirical bio-optical algorithms for chl-a and turbidity estimation devel‐ oped for inland and oceanic waters in the Araruama Lagoon, RJ, Brazil, which is an ex‐ treme environment due to its high salinity values. We also developed an empirical algorithm for both water quality parameters and compared the performances. Results showed that for chl-a estimation all models have a low performance with a normalized root mean square error (NRMSE) varying from 24.13 to 30.46. For turbidity, the bio-opti‐ cal algorithms showed a better performance with the NRMSE between 15.49 and 28.04. Overall, these results highlight the importance of including extreme environments, such as the Araruama Lagoon, on the validation of bio-optical algorithms as well as the need for new orbital hyperspectral sensors which will improve the development of the field.
Páginas235-258
Idiomaen
Tipo SecundárioPRE LI
DivulgaçãoBNDEPOSITOLEGAL
AreaSRE
Tamanho1845 KiB
Número de Arquivos1
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Acervo Hospedeirosid.inpe.br/mtc-m21b/2013/09.26.14.25.20
Tipo do ConteudoExternal Contribution
Estágio do Documentonot transferred
Tipo de Versãopublisher
Permissão de Leituradeny from all and allow from 150.163
Unidades Imediatamente Superiores8JMKD3MGPCW/3ER446E
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Histórico2016-06-09 19:28:02 :: simone -> administrator :: 2016
2018-06-04 02:40:54 :: administrator -> simone :: 2016
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Data de Acesso19 out. 2020
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