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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/4BSCDB5
Repositóriosid.inpe.br/mtc-m21d/2024/08.20.17.47
Última Atualização2024:08.20.17.47.32 (UTC) simone
Repositório de Metadadossid.inpe.br/mtc-m21d/2024/08.20.17.47.32
Última Atualização dos Metadados2024:08.22.05.07.43 (UTC) administrator
DOI10.3390/rs16152686
ISSN2072-4292
Chave de CitaçãoBreunigDGBLBGLS:2024:MoCoCr
TítuloMonitoring Cover Crop Biomass in Southern Brazil Using Combined PlanetScope and Sentinel-1 SAR Data
Ano2024
MêsAug.
Data de Acesso09 maio 2025
Tipo de Trabalhojournal article
Tipo SecundárioPRE PI
Número de Arquivos1
Tamanho14551 KiB
2. Contextualização
Autor1 Breunig, Fábio Marcelo
2 Dalagnol, Ricardo
3 Galvão, Lênio Soares
4 Bispo, Polyanna da Conceição
5 Liu, Qing
6 Berra, Elias Fernando
7 Gaida, William
8 Liesenberg, Veraldo
9 Sampaio, Tony Vinicius Moreira
Identificador de Curriculo1
2
3 8JMKD3MGP5W/3C9JHLF
ORCID1 0000-0002-0405-9603
2 0000-0002-7151-8697
3 0000-0002-8313-0497
4 0000-0003-0247-8449
5
6
7
8 0000-0003-0564-7818
Grupo1
2
3 DIOTG-CGCT-INPE-MCTI-GOV-BR
Afiliação1 Universidade Federal do Paraná (UFPR)
2 University of California
3 Instituto Nacional de Pesquisas Espaciais (INPE)
4 University of Manchester
5 University of Manchester
6 Universidade Federal do Paraná (UFPR)
7 Universidade Federal de Santa Maria (UFSM)
8 Universidade do Estado de Santa Catarina (UDESC)
9 Universidade Federal do Paraná (UFPR)
Endereço de e-Mail do Autor1 fabiobreunig@ufpr.br
2 dalagnol@ucla.edu
3 lenio.galvao@inpe.br
4 polyanna.bispo@manchester.ac.uk
5 qing.liu-8@postgrad.manchester.ac.uk
6 eliasberra@ufpr.br
7 ufsm.william@gmail.com
8 veraldo.liesenberg@udesc.br
9 tonysampaio@ufpr.br
RevistaRemote Sensing
Volume16
Número15
Páginase2686
Nota SecundáriaB3_GEOGRAFIA B3_ENGENHARIAS_I B4_GEOCIÊNCIAS B4_CIÊNCIAS_AMBIENTAIS B5_CIÊNCIAS_AGRÁRIAS_I
Histórico (UTC)2024-08-20 17:47:32 :: simone -> administrator ::
2024-08-20 17:47:38 :: administrator -> simone :: 2024
2024-08-20 17:49:05 :: simone -> administrator :: 2024
2024-08-22 05:07:43 :: administrator -> simone :: 2024
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-ChaveAGB
agriculture
multi sensors
regression
remote sensing
ResumoPrecision agriculture integrates multiple sensors and data types to support farmers with informed decision-making tools throughout crop cycles. This study evaluated Aboveground Biomass (AGB) estimates of Rye using attributes derived from PlanetScope (PS) optical, Sentinel-1 Synthetic Aperture Radar (SAR), and hybrid (optical plus SAR) datasets. Optical attributes encompassed surface reflectance from PSs blue, green, red, and near-infrared (NIR) bands, alongside the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). Sentinel-1 SAR attributes included the C-band Synthetic Aperture Radar Ground Range Detected, VV and HH polarizations, and both Ratio and Polarization (Pol) indices. Ground reference AGB data for Rye (Secale cereal L.) were collected from 50 samples and four dates at a farm located in southern Brazil, aligning with image acquisition dates. Multiple linear regression models were trained and validated. AGB was estimated based on individual (optical PS or Sentinel-1 SAR) and combined datasets (optical plus SAR). This process was repeated 100 times, and variable importance was extracted. Results revealed improved Rye AGB estimates with integrated optical and SAR data. Optical vegetation indices displayed higher correlation coefficients (r) for AGB estimation (r = +0.67 for both EVI and NDVI) compared to SAR attributes like VV, Ratio, and polarization (r ranging from −0.52 to −0.58). However, the hybrid regression model enhanced AGB estimation (R2 = 0.62, p < 0.01), reducing RMSE to 579 kg·ha−1. Using only optical or SAR data yielded R2 values of 0.51 and 0.42, respectively (p < 0.01). In the hybrid model, the most important predictors were VV, NIR, blue, and EVI. Spatial distribution analysis of predicted Rye AGB unveiled agricultural zones associated with varying biomass throughout the cover crop development. Our findings underscored the complementarity of optical with SAR data to enhance AGB estimates of cover crops, offering valuable insights for agricultural zoning to support soil and cash crop management.
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4. Condições de acesso e uso
URL dos dadoshttp://urlib.net/ibi/8JMKD3MGP3W34T/4BSCDB5
URL dos dados zipadoshttp://urlib.net/zip/8JMKD3MGP3W34T/4BSCDB5
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Arquivo Alvoremotesensing-16-02686.pdf
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5. Fontes relacionadas
Repositório Espelhourlib.net/www/2021/06.04.03.40.25
Unidades Imediatamente Superiores8JMKD3MGPCW/46KUATE
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 nextedition notes parameterlist parentrepositories previousedition previouslowerunit progress project readpermission rightsholder schedulinginformation secondarydate secondarykey session shorttitle sponsor subject tertiarymark tertiarytype url
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