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1. Identificação
Tipo de ReferênciaArtigo em Revista Científica (Journal Article)
Sitemtc-m21b.sid.inpe.br
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identificador8JMKD3MGP3W34P/3NNF96P
Repositóriosid.inpe.br/mtc-m21b/2017/04.18.13.56
Última Atualização2017:04.18.13.56.57 (UTC) administrator
Repositório de Metadadossid.inpe.br/mtc-m21b/2017/04.18.13.56.57
Última Atualização dos Metadados2018:06.04.02.27.24 (UTC) administrator
DOI10.3390/rs9010042
ISSN2072-4292
Chave de CitaçãoRochaNetoTeiLeãMorGal:2017:HyReSe
TítuloHyperspectral remote sensing for detecting soil salinization using ProSpecTIR-VS aerial imagery and sensor simulation
Ano2017
MêsJan.
Data de Acesso02 maio 2024
Tipo de Trabalhojournal article
Tipo SecundárioPRE PI
Número de Arquivos1
Tamanho2883 KiB
2. Contextualização
Autor1 Rocha Neto, Odílio Coimbra da
2 Teixeira, Adunias dos Santos
3 Leão, Raimundo Alípio de Oliveira
4 Moreira, Luis Clenio Jario
5 Galvão, Lênio Soares
Identificador de Curriculo1
2
3
4
5 8JMKD3MGP5W/3C9JHLF
Grupo1
2
3
4
5 DIDSR-CGOBT-INPE-MCTIC-GOV-BR
Afiliação1 Universidade Federal do Ceará (UFC)
2 Universidade Federal do Ceará (UFC)
3 Universidade Federal do Ceará (UFC)
4 Universidade Federal do Ceará (UFC)
5 Instituto Nacional de Pesquisas Espaciais (INPE)
Endereço de e-Mail do Autor1 odilioneto@gmail.com
2 adunias@ufc.br
3 alipioleao@yahoo.com.br
4 cleniojario@gmail.com
5 lenio.galvao@inpe.br
RevistaRemote Sensing
Volume9
Número1
PáginasUNSP 42
Nota SecundáriaB3_GEOGRAFIA B3_ENGENHARIAS_I B4_GEOCIÊNCIAS B4_CIÊNCIAS_AMBIENTAIS B5_CIÊNCIAS_AGRÁRIAS_I
Histórico (UTC)2017-04-18 13:56:57 :: simone -> administrator ::
2017-04-18 13:56:58 :: administrator -> simone :: 2017
2017-04-18 13:57:19 :: simone -> administrator :: 2017
2018-06-04 02:27:24 :: administrator -> simone :: 2017
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-Chavesoil salinization
electrical conductivity
reflectance spectroscopy
hyperspectral remote sensing
Extreme Learning Machine (ELM)
Ordinary Least Square Regression (OLS)
Multilayer Perceptron (MLP)
Partial Least Squares Regression (PLSR)
ResumoSoil salinization due to irrigation affects agricultural productivity in the semi-arid region of Brazil. In this study, the performance of four computational models to estimate electrical conductivity (EC) (soil salinization) was evaluated using laboratory reflectance spectroscopy. To investigate the influence of bandwidth and band positioning on the EC estimates, we simulated the spectral resolution of two hyperspectral sensors (airborne ProSpecTIR-VS and orbital Hyperspectral Infrared Imager (HyspIRI)) and three multispectral instruments (RapidEye/REIS, High Resolution Geometric (HRG)/SPOT-5, and Operational Land Imager (OLI)/Landsat-8)). Principal component analysis (PCA) and the first-order derivative analysis were applied to the data to generate metrics associated with soil brightness and spectral features, respectively. The three sets of data (reflectance, PCA, and derivative) were tested as input variable for Extreme Learning Machine (ELM), Ordinary Least Square regression (OLS), Partial Least Squares Regression (PLSR), and Multilayer Perceptron (MLP). Finally, the laboratory models were inverted to a ProSpecTIR-VS image (400-2500 nm) acquired with 1-m spatial resolution in the northeast of Brazil. The objective was to estimate EC over exposed soils detected using the Normalized Difference Vegetation Index (NDVI). The results showed that the predictive ability of the linear models and ELM was better than that of the MLP, as indicated by higher values of the coefficient of determination (R-2) and ratio of the performance to deviation (RPD), and lower values of the root mean square error (RMSE). Metrics associated with soil brightness (reflectance and PCA scores) were more efficient in detecting changes in the EC produced by soil salinization than metrics related to spectral features (derivative). When applied to the image, the PLSR model with reflectance had an RMSE of 1.22 dS.m(-1) and an RPD of 2.21, and was more suitable for detecting salinization (10-20 dS.m(-1)) in exposed soils (NDVI < 0.30) than the other models. For all computational models, lower values of RMSE and higher values of RPD were observed for the narrowband-simulated sensors compared to the broadband-simulated instruments. The soil EC estimates improved from the RapidEye to the HRG and OLI spectral resolutions, showing the importance of shortwave intervals (SWIR-1 and SWIR-2) in detecting soil salinization when the reflectance of selected bands is used in data modelling.
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4. Condições de acesso e uso
URL dos dadoshttp://urlib.net/ibi/8JMKD3MGP3W34P/3NNF96P
URL dos dados zipadoshttp://urlib.net/zip/8JMKD3MGP3W34P/3NNF96P
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Arquivo Alvoneto.pdf
Grupo de Usuáriossimone
Grupo de Leitoresadministrator
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Visibilidadeshown
Política de Arquivamentoallowpublisher allowfinaldraft
Permissão de Atualizaçãonão transferida
5. Fontes relacionadas
Unidades Imediatamente Superiores8JMKD3MGPCW/3ER446E
Lista de Itens Citandosid.inpe.br/mtc-m21/2012/07.13.14.53.28 1
DivulgaçãoWEBSCI; PORTALCAPES; MGA; COMPENDEX; SCOPUS.
Acervo Hospedeirosid.inpe.br/mtc-m21b/2013/09.26.14.25.20
6. Notas
Campos Vaziosalternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark mirrorrepository nextedition notes orcid parameterlist parentrepositories previousedition previouslowerunit progress project readpermission rightsholder schedulinginformation secondarydate secondarykey session shorttitle sponsor subject tertiarymark tertiarytype url
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