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| 2. |  | ANTUNES JUNIOR, M. Z.; FERRONATO, A.; FERRONATO, S. D.; RODRIGUES, K.; GALLON, M. M. P.; GOMES, N. M. G.; STIEVEN, A. C.; CAMPELO JUNIOR, J. H. Métodos não destrutivos para estimativa de densidade de área foliar em mangueira. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 44, n. 12, p. 1624-1630, dez. 2009 Título em inglês: Non-destructive methods for estimating leaf area density in mango.| Biblioteca(s): Embrapa Unidades Centrais. |
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| 3. | | RODIGHERI, G.; FONTANA, D. C.; SCHAPARINI, L. P.; DALMAGO, G. A.; SCHIRMBECK, J. Net primary productivity and dry matter in soybean cultivation utilizing datas of ndvi multi-sensors. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-3/W12-2020, IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS 2020), 22?26 March 2020, Santiago, Chile, 2020.| Biblioteca(s): Embrapa Trigo. |
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| Registros recuperados : 18 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Trigo. |
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Data corrente: |
11/02/2021 |
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Data da última atualização: |
11/02/2021 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
RODIGHERI, G.; FONTANA, D. C.; SCHAPARINI, L. P.; DALMAGO, G. A.; SCHIRMBECK, J. |
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Afiliação: |
GRAQZIELI RODIGHERI, CEPSRM, Post-Graduation Program of Remote Sensing, 91501970, Rio Grande do Sul, Brasil – grazielirodigheri@gmail.com; D. C. FONTANA, Dept. of Agronomy, Post-Graduation Program in Phytotechnics, 91540000, Rio Grande do Sul, Brasil – dfontana@ufrgs.br; laura_pigatto@yahoo.com.br; LAURA PIGATTO SCHAPARINI, Dept. of Agronomy, Post-Graduation Program in Phytotechnics, 91540000, Rio Grande do Sul, Brasil – dfontana@ufrgs.br; laura_pigatto@yahoo.com.br; GENEI ANTONIO DALMAGO, CNPT; J. SCHIRMBECK, UNIVATES, 95914014, Rio Grande do Sul, Brasil – schirmbeck.j@gmail.com. |
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Título: |
Net primary productivity and dry matter in soybean cultivation utilizing datas of ndvi multi-sensors. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-3/W12-2020, IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS 2020), 22?26 March 2020, Santiago, Chile, 2020. |
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Idioma: |
Inglês |
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Conteúdo: |
Net Primary Productivity (NPP) is an important indicator of vegetation growth status and ecosystems health. NPP can be estimated through remote sensing data, using vegetation indices such as NDVI. However, this index may show systematic differences when using several orbital sensors. Therefore, the objective of this paper was to compare the NDVI data obtained from different sensors and evaluate the impact over the soybean biomass and NPP estimates. NDVI data were recorded from 4 sensors, one on the field and others 3 orbitals sensors (Landsat 8/OLI, Sentinel 2/MSI and Terra/MODIS). Measured data on the field, Photosynthetically Active Radiation (PAR) and Dry Matter (DM), were used to modeling the total DM and also NPP. The NDVI data from different sensors showed differences throughout the cycle, but compared to the reference data there was a correlation greater than 0.84. The DM presented a correlation of 0.91 with the field measured MS data while the NPP presented differences of up to 240 gC/m²/month from in relation to the reference data. Therefore, NDVI obtained from multiple sensors can be used to estimate NPP for surface analysis. However, for more consistent evaluations, a function of adjustment between the NDVI sensor data and NDVI reference data is required, so that the NPP estimation be better correlated to the actual data. |
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Palavras-Chave: |
Google Earth Engine; PAR. |
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Thesaurus NAL: |
Agriculture; Remote sensing. |
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Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/221162/1/Rodigheri-2020-p22.pdf
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|
Marc: |
LEADER 02164nam a2200205 a 4500
001 2129996
005 2021-02-11
008 2020 bl uuuu u00u1 u #d
100 1 $aRODIGHERI, G.
245 $aNet primary productivity and dry matter in soybean cultivation utilizing datas of ndvi multi-sensors.$h[electronic resource]
260 $aIn: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-3/W12-2020, IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS 2020), 22?26 March 2020, Santiago, Chile$c2020
520 $aNet Primary Productivity (NPP) is an important indicator of vegetation growth status and ecosystems health. NPP can be estimated through remote sensing data, using vegetation indices such as NDVI. However, this index may show systematic differences when using several orbital sensors. Therefore, the objective of this paper was to compare the NDVI data obtained from different sensors and evaluate the impact over the soybean biomass and NPP estimates. NDVI data were recorded from 4 sensors, one on the field and others 3 orbitals sensors (Landsat 8/OLI, Sentinel 2/MSI and Terra/MODIS). Measured data on the field, Photosynthetically Active Radiation (PAR) and Dry Matter (DM), were used to modeling the total DM and also NPP. The NDVI data from different sensors showed differences throughout the cycle, but compared to the reference data there was a correlation greater than 0.84. The DM presented a correlation of 0.91 with the field measured MS data while the NPP presented differences of up to 240 gC/m²/month from in relation to the reference data. Therefore, NDVI obtained from multiple sensors can be used to estimate NPP for surface analysis. However, for more consistent evaluations, a function of adjustment between the NDVI sensor data and NDVI reference data is required, so that the NPP estimation be better correlated to the actual data.
650 $aAgriculture
650 $aRemote sensing
653 $aGoogle Earth Engine
653 $aPAR
700 1 $aFONTANA, D. C.
700 1 $aSCHAPARINI, L. P.
700 1 $aDALMAGO, G. A.
700 1 $aSCHIRMBECK, J.
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