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Registro Completo |
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Biblioteca(s): |
Embrapa Solos. |
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Data corrente: |
22/02/2022 |
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Data da última atualização: |
24/02/2022 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
TAVARES, S. R. de L.; VASQUES, G. de M.; OLIVEIRA, R. P. de; DANTAS, M. M.; RODRIGUES, H. M. |
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Afiliação: |
SILVIO ROBERTO DE LUCENA TAVARES, CNPS; GUSTAVO DE MATTOS VASQUES, CNPS; RONALDO PEREIRA DE OLIVEIRA, CNPS; MARLON M. DANTAS, IFRN; HUGO MACHADO RODRIGUES, UFRRJ. |
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Título: |
Proximal and remote sensor data fusion for in-depth salinization mapping in the Brazilian semiarid via machine learning. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
In: PEDOMETRICS BRAZIL, 2., 2021, Rio de Janeiro. Annals [...]. Rio de Janeiro: Embrapa Solos, 2022. Não paginado. Evento online. |
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Idioma: |
Inglês |
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Conteúdo: |
Mapping the salinization in irrigated cropland is a challenging practice. As an alternative, data from proximal and remote sensors have been implemented together via datafusion and machine learning algorithms. The present work was carried out on a farm with 11 ha and used data from the proximal sensor EM38-MK2 associated with radar C-band data obtained by the Sentinel1 satellite. The salinization classes were created from electrical conductivity data measured at 35 points using a 50 x 50 m sampling grid and at three depths: 0 ? 10, 10 ? 30, and 30 ? 50 cm using conventional laboratory approach. The accuracy values of the class prediction models presented values between 0.66 and 0.74 and Kappa values between 0.43 and 0.59 using Random Forest. The salinization decreased in layers 0 - 10 and 10 - 30 cm due to implementing a surface drainage system but the depth 30 - 50 cm had the highest occurrence of Salic classes, with a potentially harmful effect on the roots. |
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Thesagro: |
Sensoriamento Remoto. |
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Thesaurus Nal: |
Remote sensing. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/231716/1/Proximal-and-remote-sensor-data-fusion-for-in-depth-salinization-mapping-2022.pdf
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Marc: |
LEADER 01663nam a2200181 a 4500
001 2140303
005 2022-02-24
008 2022 bl uuuu u00u1 u #d
100 1 $aTAVARES, S. R. de L.
245 $aProximal and remote sensor data fusion for in-depth salinization mapping in the Brazilian semiarid via machine learning.$h[electronic resource]
260 $aIn: PEDOMETRICS BRAZIL, 2., 2021, Rio de Janeiro. Annals [...]. Rio de Janeiro: Embrapa Solos, 2022. Não paginado. Evento online.$c2022
520 $aMapping the salinization in irrigated cropland is a challenging practice. As an alternative, data from proximal and remote sensors have been implemented together via datafusion and machine learning algorithms. The present work was carried out on a farm with 11 ha and used data from the proximal sensor EM38-MK2 associated with radar C-band data obtained by the Sentinel1 satellite. The salinization classes were created from electrical conductivity data measured at 35 points using a 50 x 50 m sampling grid and at three depths: 0 ? 10, 10 ? 30, and 30 ? 50 cm using conventional laboratory approach. The accuracy values of the class prediction models presented values between 0.66 and 0.74 and Kappa values between 0.43 and 0.59 using Random Forest. The salinization decreased in layers 0 - 10 and 10 - 30 cm due to implementing a surface drainage system but the depth 30 - 50 cm had the highest occurrence of Salic classes, with a potentially harmful effect on the roots.
650 $aRemote sensing
650 $aSensoriamento Remoto
700 1 $aVASQUES, G. de M.
700 1 $aOLIVEIRA, R. P. de
700 1 $aDANTAS, M. M.
700 1 $aRODRIGUES, H. M.
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Registro original: |
Embrapa Solos (CNPS) |
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Biblioteca |
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Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
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Registro Completo
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Biblioteca(s): |
Embrapa Trigo. |
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Data corrente: |
06/02/2019 |
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Data da última atualização: |
06/02/2019 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
C - 0 |
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Autoria: |
DALMAGO, G. A.; PINTO, D. G.; FONTANA, D. C.; GOUVEA, J. A. de; BERGAMASCHI, H.; FOCHESATTO, E.; SANTI, A. |
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Afiliação: |
GENEI ANTONIO DALMAGO, CNPT; DANIELE GUTTERRES PINTO, UFRGS; DENISE CYBIS FONTANA, UFRGS; JORGE ALBERTO DE GOUVEA, CNPT; HOMERO BERGAMASCHI, UFRGS; ELIZANDRO FOCHESATTO; ANDERSON SANTI, CNPT. |
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Título: |
Use of solar radiation in the improvement of spring canola (Brassica napus L., Brassicaceae) yield influenced by nitrogen topdressing fertilization. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Agrometeoros, v. 26, n. 1, p. 223-237, 2018. |
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ISSN: |
2526-7043 |
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DOI: |
10.31062/agrom.v26i1.26368 |
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Idioma: |
Inglês |
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Thesagro: |
Área Foliar; Brassica Napus; Matéria Seca; Radiação Solar. |
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Thesaurus NAL: |
Canola; Dry matter accumulation; Leaf area index; Photosynthetically active radiation; Solar radiation. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/192199/1/ID44530-2018v26n1p233Agrometeoros.pdf
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Marc: |
LEADER 00981naa a2200313 a 4500
001 2105604
005 2019-02-06
008 2018 bl uuuu u00u1 u #d
022 $a2526-7043
024 7 $a10.31062/agrom.v26i1.26368$2DOI
100 1 $aDALMAGO, G. A.
245 $aUse of solar radiation in the improvement of spring canola (Brassica napus L., Brassicaceae) yield influenced by nitrogen topdressing fertilization.$h[electronic resource]
260 $c2018
650 $aCanola
650 $aDry matter accumulation
650 $aLeaf area index
650 $aPhotosynthetically active radiation
650 $aSolar radiation
650 $aÁrea Foliar
650 $aBrassica Napus
650 $aMatéria Seca
650 $aRadiação Solar
700 1 $aPINTO, D. G.
700 1 $aFONTANA, D. C.
700 1 $aGOUVEA, J. A. de
700 1 $aBERGAMASCHI, H.
700 1 $aFOCHESATTO, E.
700 1 $aSANTI, A.
773 $tAgrometeoros$gv. 26, n. 1, p. 223-237, 2018.
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Registro original: |
Embrapa Trigo (CNPT) |
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