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Artigo de periodico
Estimation of biochemical compounds in Tradescantia leaves using VIS-NIR-SWIR hyperspectral and chlorophyll a fluorescence sensors (2024)
Falcioni, Renan ; Oliveira, Roney Berti de ; Chicati, Marcelo Luiz ; Antunes, Werner Camargos ; Demattê, José Alexandre Melo ; Nanni, Marcos Rafael
Source: Remote Sensing. Unidade: ESALQ
Subjects: BIOQUÍMICA VEGETAL, CLOROFILA, COMMELINALES, COMPONENTES PRINCIPAIS, ESPECTROSCOPIA, FOLHAS (PLANTAS), FLUORESCÊNCIA, MÍNIMOS QUADRADOS, SENSORIAMENTO REMOTO
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FALCIONI, Renan et al. Estimation of biochemical compounds in Tradescantia leaves using VIS-NIR-SWIR hyperspectral and chlorophyll a fluorescence sensors. Remote Sensing, v. 16, p. 1-25, 2024Tradução . . Disponível em: https://doi.org/10.3390/rs16111910. Acesso em: 15 nov. 2024.
APA
Falcioni, R., Oliveira, R. B. de, Chicati, M. L., Antunes, W. C., Demattê, J. A. M., & Nanni, M. R. (2024). Estimation of biochemical compounds in Tradescantia leaves using VIS-NIR-SWIR hyperspectral and chlorophyll a fluorescence sensors. Remote Sensing, 16, 1-25. doi:10.3390/rs16111910
NLM
Falcioni R, Oliveira RB de, Chicati ML, Antunes WC, Demattê JAM, Nanni MR. Estimation of biochemical compounds in Tradescantia leaves using VIS-NIR-SWIR hyperspectral and chlorophyll a fluorescence sensors [Internet]. Remote Sensing. 2024 ; 16 1-25.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/rs16111910
Vancouver
Falcioni R, Oliveira RB de, Chicati ML, Antunes WC, Demattê JAM, Nanni MR. Estimation of biochemical compounds in Tradescantia leaves using VIS-NIR-SWIR hyperspectral and chlorophyll a fluorescence sensors [Internet]. Remote Sensing. 2024 ; 16 1-25.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/rs16111910
Artigo de periodico
Sugarcane yield estimation using satellite remote sensing data in empirical or mechanistic modeling: a systematic review (2024)
Silva, Nildson Rodrigues de França e; Chaves, Michel Eustáquio Dantas ; Luciano, Ana Cláudia dos Santos ; Sanches, Ieda Del’Arco ; Almeida, Cláudia Maria de; Adami, Marcos
Source: Remote Sensing. Unidade: ESALQ
Subjects: CANA-DE-AÇÚCAR, IMAGEAMENTO DE SATÉLITE, MODELAGEM DE DADOS, REVISÃO SISTEMÁTICA, SENSORIAMENTO REMOTO
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SILVA, Nildson Rodrigues de França e et al. Sugarcane yield estimation using satellite remote sensing data in empirical or mechanistic modeling: a systematic review. Remote Sensing, v. 16, p. 1-26, 2024Tradução . . Disponível em: https://doi.org/10.3390/rs16050863. Acesso em: 15 nov. 2024.
APA
Silva, N. R. de F. e, Chaves, M. E. D., Luciano, A. C. dos S., Sanches, I. D. ’A., Almeida, C. M. de, & Adami, M. (2024). Sugarcane yield estimation using satellite remote sensing data in empirical or mechanistic modeling: a systematic review. Remote Sensing, 16, 1-26. doi:10.3390/rs16050863
NLM
Silva NR de F e, Chaves MED, Luciano AC dos S, Sanches ID’A, Almeida CM de, Adami M. Sugarcane yield estimation using satellite remote sensing data in empirical or mechanistic modeling: a systematic review [Internet]. Remote Sensing. 2024 ; 16 1-26.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/rs16050863
Vancouver
Silva NR de F e, Chaves MED, Luciano AC dos S, Sanches ID’A, Almeida CM de, Adami M. Sugarcane yield estimation using satellite remote sensing data in empirical or mechanistic modeling: a systematic review [Internet]. Remote Sensing. 2024 ; 16 1-26.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/rs16050863
Artigo de periodico
Integrated use of synthetic aperture radar and optical data in mapping native vegetation: a study in a transitional brazilian Cerrado–Atlantic Forest interface (2024)
Santos, Allita Rezende dos ; Barbosa, Mariana Abibi Guimarâes Araujo; Anjinho, Phelipe da Silva; Parizotto, Denise; Mauad, Frederico Fábio
Source: Remote Sensing. Unidade: EESC
Subjects: VEGETAÇÃO, BIOMA, RADAR, ENGENHARIA HIDRÁULICA
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SANTOS, Allita Rezende dos et al. Integrated use of synthetic aperture radar and optical data in mapping native vegetation: a study in a transitional brazilian Cerrado–Atlantic Forest interface. Remote Sensing, v. 16, n. 14, p. 1-15, 2024Tradução . . Disponível em: https://dx.doi.org/10.3390/rs16142559. Acesso em: 15 nov. 2024.
APA
Santos, A. R. dos, Barbosa, M. A. G. A., Anjinho, P. da S., Parizotto, D., & Mauad, F. F. (2024). Integrated use of synthetic aperture radar and optical data in mapping native vegetation: a study in a transitional brazilian Cerrado–Atlantic Forest interface. Remote Sensing, 16( 14), 1-15. doi:10.3390/rs16142559
NLM
Santos AR dos, Barbosa MAGA, Anjinho P da S, Parizotto D, Mauad FF. Integrated use of synthetic aperture radar and optical data in mapping native vegetation: a study in a transitional brazilian Cerrado–Atlantic Forest interface [Internet]. Remote Sensing. 2024 ; 16( 14): 1-15.[citado 2024 nov. 15 ] Available from: https://dx.doi.org/10.3390/rs16142559
Vancouver
Santos AR dos, Barbosa MAGA, Anjinho P da S, Parizotto D, Mauad FF. Integrated use of synthetic aperture radar and optical data in mapping native vegetation: a study in a transitional brazilian Cerrado–Atlantic Forest interface [Internet]. Remote Sensing. 2024 ; 16( 14): 1-15.[citado 2024 nov. 15 ] Available from: https://dx.doi.org/10.3390/rs16142559
Artigo de periodico
The use of Vis-NIR-SWIR spectroscopy and X-ray fluorescence in the development of predictive models: a step forward in the quantification of nitrogen, total organic carbon and humic fractions in ferralsols (2024)
Lima, Bruna Coelho de ; Demattê, José Alexandre Melo ; Santos, Carlos H. dos; Tiritan, Carlos S; Poppiel, Raul Roberto ; Nanni, Marcos R. ; Falcioni, Renan ; Oliveira, Caio A. de; Vedana, Nicole G; Zimmermann, Guilherme; Reis, Amanda S
Source: Remote Sensing. Unidade: ESALQ
Subjects: APRENDIZADO COMPUTACIONAL, CARBONO, ESPECTROSCOPIA, FLUORESCÊNCIA, RAIOS X, MATÉRIA ORGÂNICA DO SOLO, NITROGÊNIO, SOLO ARENOSO
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ABNT
LIMA, Bruna Coelho de et al. The use of Vis-NIR-SWIR spectroscopy and X-ray fluorescence in the development of predictive models: a step forward in the quantification of nitrogen, total organic carbon and humic fractions in ferralsols. Remote Sensing, v. 16, p. 1-19, 2024Tradução . . Disponível em: https://doi.org/10.3390/rs16163009. Acesso em: 15 nov. 2024.
APA
Lima, B. C. de, Demattê, J. A. M., Santos, C. H. dos, Tiritan, C. S., Poppiel, R. R., Nanni, M. R., et al. (2024). The use of Vis-NIR-SWIR spectroscopy and X-ray fluorescence in the development of predictive models: a step forward in the quantification of nitrogen, total organic carbon and humic fractions in ferralsols. Remote Sensing, 16, 1-19. doi:10.3390/rs16163009
NLM
Lima BC de, Demattê JAM, Santos CH dos, Tiritan CS, Poppiel RR, Nanni MR, Falcioni R, Oliveira CA de, Vedana NG, Zimmermann G, Reis AS. The use of Vis-NIR-SWIR spectroscopy and X-ray fluorescence in the development of predictive models: a step forward in the quantification of nitrogen, total organic carbon and humic fractions in ferralsols [Internet]. Remote Sensing. 2024 ; 16 1-19.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/rs16163009
Vancouver
Lima BC de, Demattê JAM, Santos CH dos, Tiritan CS, Poppiel RR, Nanni MR, Falcioni R, Oliveira CA de, Vedana NG, Zimmermann G, Reis AS. The use of Vis-NIR-SWIR spectroscopy and X-ray fluorescence in the development of predictive models: a step forward in the quantification of nitrogen, total organic carbon and humic fractions in ferralsols [Internet]. Remote Sensing. 2024 ; 16 1-19.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/rs16163009
Artigo de periodico
Towards Unified Online-Coupled Aerosol Parameterization for the Brazilian Global Atmospheric Model (BAM): Aerosol-Cloud Microphysical-Radiation Interactions (2023)
Pendharkar, Jayant ; Nobre, Paulo ; Herdies, Dirceu Luis ; Figueroa, Silvio Nilo ; Vara-Vela, Angel ; Krishna, Ravuri Phani Murali ; Schuch, Daniel ; Kubota, Paulo Yoshio; Alvim, Débora Souza ; Vendrasco, Éder Paulo ; Gomes, Helber Barros
Source: Remote Sensing. Unidade: EEL
Assunto: AEROSSOL
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PENDHARKAR, Jayant et al. Towards Unified Online-Coupled Aerosol Parameterization for the Brazilian Global Atmospheric Model (BAM): Aerosol-Cloud Microphysical-Radiation Interactions. Remote Sensing, v. 15, n. 1, p. 1-25, 2023Tradução . . Disponível em: https://doi.org/10.3390/rs15010278. Acesso em: 15 nov. 2024.
APA
Pendharkar, J., Nobre, P., Herdies, D. L., Figueroa, S. N., Vara-Vela, A., Krishna, R. P. M., et al. (2023). Towards Unified Online-Coupled Aerosol Parameterization for the Brazilian Global Atmospheric Model (BAM): Aerosol-Cloud Microphysical-Radiation Interactions. Remote Sensing, 15( 1), 1-25. doi:10.3390/rs15010278
NLM
Pendharkar J, Nobre P, Herdies DL, Figueroa SN, Vara-Vela A, Krishna RPM, Schuch D, Kubota PY, Alvim DS, Vendrasco ÉP, Gomes HB. Towards Unified Online-Coupled Aerosol Parameterization for the Brazilian Global Atmospheric Model (BAM): Aerosol-Cloud Microphysical-Radiation Interactions [Internet]. Remote Sensing. 2023 ; 15( 1): 1-25.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/rs15010278
Vancouver
Pendharkar J, Nobre P, Herdies DL, Figueroa SN, Vara-Vela A, Krishna RPM, Schuch D, Kubota PY, Alvim DS, Vendrasco ÉP, Gomes HB. Towards Unified Online-Coupled Aerosol Parameterization for the Brazilian Global Atmospheric Model (BAM): Aerosol-Cloud Microphysical-Radiation Interactions [Internet]. Remote Sensing. 2023 ; 15( 1): 1-25.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/rs15010278
Artigo de periodico
Towards Unified Online-Coupled Aerosol Parameterization for the Brazilian Global Atmospheric Model (BAM): Aerosol-Cloud Microphysical-Radiation Interactions (2023)
Pendharkar, Jayant ; Nobre, Paulo ; Herdies, Dirceu Luis ; Figueroa, Silvio Nilo ; Vara-Vela, Angel ; Krishna, Ravuri Phani Murali ; Schuch, Daniel ; Kubota, Paulo Yoshio; Alvim, Débora Souza ; Vendrasco, Éder Paulo ; Gomes, Helber Barros
Source: Remote Sensing. Unidade: EEL
Assunto: AEROSSOL
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ABNT
PENDHARKAR, Jayant et al. Towards Unified Online-Coupled Aerosol Parameterization for the Brazilian Global Atmospheric Model (BAM): Aerosol-Cloud Microphysical-Radiation Interactions. Remote Sensing, v. 15, n. 1, p. 1-25, 2023Tradução . . Disponível em: https://doi.org/10.3390/rs15010278. Acesso em: 15 nov. 2024.
APA
Pendharkar, J., Nobre, P., Herdies, D. L., Figueroa, S. N., Vara-Vela, A., Krishna, R. P. M., et al. (2023). Towards Unified Online-Coupled Aerosol Parameterization for the Brazilian Global Atmospheric Model (BAM): Aerosol-Cloud Microphysical-Radiation Interactions. Remote Sensing, 15( 1), 1-25. doi:10.3390/rs15010278
NLM
Pendharkar J, Nobre P, Herdies DL, Figueroa SN, Vara-Vela A, Krishna RPM, Schuch D, Kubota PY, Alvim DS, Vendrasco ÉP, Gomes HB. Towards Unified Online-Coupled Aerosol Parameterization for the Brazilian Global Atmospheric Model (BAM): Aerosol-Cloud Microphysical-Radiation Interactions [Internet]. Remote Sensing. 2023 ;15( 1): 1-25.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/rs15010278
Vancouver
Pendharkar J, Nobre P, Herdies DL, Figueroa SN, Vara-Vela A, Krishna RPM, Schuch D, Kubota PY, Alvim DS, Vendrasco ÉP, Gomes HB. Towards Unified Online-Coupled Aerosol Parameterization for the Brazilian Global Atmospheric Model (BAM): Aerosol-Cloud Microphysical-Radiation Interactions [Internet]. Remote Sensing. 2023 ;15( 1): 1-25.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/rs15010278
Artigo de periodico
Estimating black oat biomass using digital surface models and a vegetation index derived from RGB-based aerial images (2023)
Trevisan, Lucas Renato ; Brichi, Lisiane ; Gomes, Tamara Maria ; Rossi, Fabricio
Source: Remote Sensing. Unidades: FZEA, ESALQ
Subjects: AEROFOTOGRAMETRIA, AERONAVES NÃO TRIPULADAS, AVEIA PRETA, BIOMASSA, CRESCIMENTO VEGETAL, SENSORIAMENTO REMOTO, VISÃO COMPUTACIONAL
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TREVISAN, Lucas Renato et al. Estimating black oat biomass using digital surface models and a vegetation index derived from RGB-based aerial images. Remote Sensing, v. 15, p. 1-19, 2023Tradução . . Disponível em: https://doi.org/10.3390/rs15051363. Acesso em: 15 nov. 2024.
APA
Trevisan, L. R., Brichi, L., Gomes, T. M., & Rossi, F. (2023). Estimating black oat biomass using digital surface models and a vegetation index derived from RGB-based aerial images. Remote Sensing, 15, 1-19. doi:10.3390/rs15051363
NLM
Trevisan LR, Brichi L, Gomes TM, Rossi F. Estimating black oat biomass using digital surface models and a vegetation index derived from RGB-based aerial images [Internet]. Remote Sensing. 2023 ; 15 1-19.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/rs15051363
Vancouver
Trevisan LR, Brichi L, Gomes TM, Rossi F. Estimating black oat biomass using digital surface models and a vegetation index derived from RGB-based aerial images [Internet]. Remote Sensing. 2023 ; 15 1-19.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/rs15051363
Artigo de periodico
Crown-level structure and fuel load characterization from airborne and terrestrial laser scanning in a longleaf pine (Pinus palustris Mill.) forest ecosystem (2023)
Rocha, Kleydson Diego; Silva, Carlos Alberto ; Cosenza, Diogo N ; Mohan, Midhun ; Klauberg, Carine ; Schlickmann, Monique Bohora ; Xia, Jinyi; Leite, Rodrigo V; Almeida, Danilo Roberti Alves de ; Atkins, Jeff W; Cardil, Adrian ; Rowell, Eric; Parsons, Russ ; Sánchez-López, Nuria ; Prichard, Susan J; Hudak, Andrew T
Source: Remote Sensing. Unidade: ESALQ
Subjects: APRENDIZADO COMPUTACIONAL, DOSSEL (BOTÂNICA), ECOSSISTEMAS FLORESTAIS, PINHEIRO, SENSORIAMENTO REMOTO, TECNOLOGIA LIDAR
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ROCHA, Kleydson Diego et al. Crown-level structure and fuel load characterization from airborne and terrestrial laser scanning in a longleaf pine (Pinus palustris Mill.) forest ecosystem. Remote Sensing, v. 15, p. 1-21, 2023Tradução . . Disponível em: https://doi.org/10.3390/rs15041002. Acesso em: 15 nov. 2024.
APA
Rocha, K. D., Silva, C. A., Cosenza, D. N., Mohan, M., Klauberg, C., Schlickmann, M. B., et al. (2023). Crown-level structure and fuel load characterization from airborne and terrestrial laser scanning in a longleaf pine (Pinus palustris Mill.) forest ecosystem. Remote Sensing, 15, 1-21. doi:10.3390/rs15041002
NLM
Rocha KD, Silva CA, Cosenza DN, Mohan M, Klauberg C, Schlickmann MB, Xia J, Leite RV, Almeida DRA de, Atkins JW, Cardil A, Rowell E, Parsons R, Sánchez-López N, Prichard SJ, Hudak AT. Crown-level structure and fuel load characterization from airborne and terrestrial laser scanning in a longleaf pine (Pinus palustris Mill.) forest ecosystem [Internet]. Remote Sensing. 2023 ; 15 1-21.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/rs15041002
Vancouver
Rocha KD, Silva CA, Cosenza DN, Mohan M, Klauberg C, Schlickmann MB, Xia J, Leite RV, Almeida DRA de, Atkins JW, Cardil A, Rowell E, Parsons R, Sánchez-López N, Prichard SJ, Hudak AT. Crown-level structure and fuel load characterization from airborne and terrestrial laser scanning in a longleaf pine (Pinus palustris Mill.) forest ecosystem [Internet]. Remote Sensing. 2023 ; 15 1-21.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/rs15041002
Artigo de periodico
COVID-19 Pandemic: Impacts on Air Quality during Partial Lockdown in the Metropolitan Area of São Paulo (2023)
Alvim, Débora Souza ; Figueroa, Silvio Nilo ; Herdies, Dirceu Luis ; Corrêa, Sérgio Machado ; Basso, Luana Santamaria; Khalid, Bushra; Silva, Gabriella Fernandes Prazeres; Oyerinde, Gabriel ; Carvalho, Nicolli Albuquerque de; Coelho, Simone Marilene Sievert da Costa
Source: Remote Sensing. Unidade: EEL
Subjects: POLUIÇÃO ATMOSFÉRICA, PANDEMIAS, COVID-19, SÃO PAULO (SP)
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ALVIM, Débora Souza et al. COVID-19 Pandemic: Impacts on Air Quality during Partial Lockdown in the Metropolitan Area of São Paulo. Remote Sensing, v. 15, n. 5, p. 1-30, 2023Tradução . . Disponível em: https://doi.org/10.3390/rs15051262. Acesso em: 15 nov. 2024.
APA
Alvim, D. S., Figueroa, S. N., Herdies, D. L., Corrêa, S. M., Basso, L. S., Khalid, B., et al. (2023). COVID-19 Pandemic: Impacts on Air Quality during Partial Lockdown in the Metropolitan Area of São Paulo. Remote Sensing, 15( 5), 1-30. doi:10.3390/rs15051262
NLM
Alvim DS, Figueroa SN, Herdies DL, Corrêa SM, Basso LS, Khalid B, Silva GFP, Oyerinde G, Carvalho NA de, Coelho SMS da C. COVID-19 Pandemic: Impacts on Air Quality during Partial Lockdown in the Metropolitan Area of São Paulo [Internet]. Remote Sensing. 2023 ;15( 5): 1-30.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/rs15051262
Vancouver
Alvim DS, Figueroa SN, Herdies DL, Corrêa SM, Basso LS, Khalid B, Silva GFP, Oyerinde G, Carvalho NA de, Coelho SMS da C. COVID-19 Pandemic: Impacts on Air Quality during Partial Lockdown in the Metropolitan Area of São Paulo [Internet]. Remote Sensing. 2023 ;15( 5): 1-30.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/rs15051262
Artigo de periodico
Assessment of combined reflectance, transmittance, and absorbance hyperspectral sensors for prediction of chlorophyll a fluorescence parameters (2023)
Falcioni, Renan ; Antunes, Werner Camargos ; Oliveira, Roney Berti de ; Chicati, Marcelo Luiz ; Demattê, José Alexandre Melo ; Nanni, Marcos Rafael
Source: Remote Sensing. Unidade: ESALQ
Subjects: ANÁLISE MULTIVARIADA, CLOROFILA, FLUORESCÊNCIA, ESPECTROSCOPIA, FOLHAS (PLANTAS), GERÂNIO, HIBISCO, MODELOS MATEMÁTICOS, SENSORIAMENTO REMOTO
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ABNT
FALCIONI, Renan et al. Assessment of combined reflectance, transmittance, and absorbance hyperspectral sensors for prediction of chlorophyll a fluorescence parameters. Remote Sensing, v. 15, p. 1-23, 2023Tradução . . Disponível em: https://doi.org/10.3390/rs15205067. Acesso em: 15 nov. 2024.
APA
Falcioni, R., Antunes, W. C., Oliveira, R. B. de, Chicati, M. L., Demattê, J. A. M., & Nanni, M. R. (2023). Assessment of combined reflectance, transmittance, and absorbance hyperspectral sensors for prediction of chlorophyll a fluorescence parameters. Remote Sensing, 15, 1-23. doi:10.3390/rs15205067
NLM
Falcioni R, Antunes WC, Oliveira RB de, Chicati ML, Demattê JAM, Nanni MR. Assessment of combined reflectance, transmittance, and absorbance hyperspectral sensors for prediction of chlorophyll a fluorescence parameters [Internet]. Remote Sensing. 2023 ; 15 1-23.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/rs15205067
Vancouver
Falcioni R, Antunes WC, Oliveira RB de, Chicati ML, Demattê JAM, Nanni MR. Assessment of combined reflectance, transmittance, and absorbance hyperspectral sensors for prediction of chlorophyll a fluorescence parameters [Internet]. Remote Sensing. 2023 ; 15 1-23.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/rs15205067
Artigo de periodico
Soil erosion satellite-based estimation in cropland for soil conservation (2023)
Gallo, Bruna Cristina ; Magalhães, Paulo Sérgio Graziano ; Demattê, José Alexandre Melo ; Cervi, Walter Rossi; Carvalho, João Luís Nunes ; Barbosa, Leandro Carneiro ; Bellinaso, Henrique ; Mello, Danilo César de ; Veloso, Gustavo Vieira ; Alves, Marcelo Rodrigo; Fernandes-Filho, Elpídio Inácio ; Francelino, Márcio Rocha ; Schaefer, Carlos Ernesto Gonçalves Reynaud
Source: Remote Sensing. Unidade: ESALQ
Subjects: BIOENERGIA, CONSERVAÇÃO DO SOLO, DEGRADAÇÃO DO SOLO, EROSÃO, IMAGEAMENTO DE SATÉLITE, RESÍDUOS AGRÍCOLAS, SENSORIAMENTO REMOTO, USO DO SOLO
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GALLO, Bruna Cristina et al. Soil erosion satellite-based estimation in cropland for soil conservation. Remote Sensing, v. 15, p. 11-24, 2023Tradução . . Disponível em: https://doi.org/10.3390/rs15010020. Acesso em: 15 nov. 2024.
APA
Gallo, B. C., Magalhães, P. S. G., Demattê, J. A. M., Cervi, W. R., Carvalho, J. L. N., Barbosa, L. C., et al. (2023). Soil erosion satellite-based estimation in cropland for soil conservation. Remote Sensing, 15, 11-24. doi:10.3390/rs15010020
NLM
Gallo BC, Magalhães PSG, Demattê JAM, Cervi WR, Carvalho JLN, Barbosa LC, Bellinaso H, Mello DC de, Veloso GV, Alves MR, Fernandes-Filho EI, Francelino MR, Schaefer CEGR. Soil erosion satellite-based estimation in cropland for soil conservation [Internet]. Remote Sensing. 2023 ; 15 11-24.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/rs15010020
Vancouver
Gallo BC, Magalhães PSG, Demattê JAM, Cervi WR, Carvalho JLN, Barbosa LC, Bellinaso H, Mello DC de, Veloso GV, Alves MR, Fernandes-Filho EI, Francelino MR, Schaefer CEGR. Soil erosion satellite-based estimation in cropland for soil conservation [Internet]. Remote Sensing. 2023 ; 15 11-24.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/rs15010020
Artigo de periodico
Sentinel-1 imagery used for estimation of soil organic carbon by dual-polarization SAR vegetation indices (2023)
Santos, Erli Pinto dos ; Moreira, Michel Castro; Fernandes-Filho, Elpídio Inácio ; Demattê, José Alexandre Melo ; Dionizio, Emily Ane ; Silva, Demetrius David da ; Cruz, Renata Ranielly Pedroza; Moura-Bueno, Jean Michel; Santos, Uemeson José dos ; Costa, Marcos Heil
Source: Remote Sensing. Unidade: ESALQ
Subjects: APRENDIZADO COMPUTACIONAL, CARBONO, IMAGEAMENTO DE RADAR, MAPEAMENTO DO SOLO, SENSORIAMENTO REMOTO, SOLO DE CERRADO
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ABNT
SANTOS, Erli Pinto dos et al. Sentinel-1 imagery used for estimation of soil organic carbon by dual-polarization SAR vegetation indices. Remote Sensing, v. 15, p. 1-20, 2023Tradução . . Disponível em: https://doi.org/10.3390/rs15235464. Acesso em: 15 nov. 2024.
APA
Santos, E. P. dos, Moreira, M. C., Fernandes-Filho, E. I., Demattê, J. A. M., Dionizio, E. A., Silva, D. D. da, et al. (2023). Sentinel-1 imagery used for estimation of soil organic carbon by dual-polarization SAR vegetation indices. Remote Sensing, 15, 1-20. doi:10.3390/rs15235464
NLM
Santos EP dos, Moreira MC, Fernandes-Filho EI, Demattê JAM, Dionizio EA, Silva DD da, Cruz RRP, Moura-Bueno JM, Santos UJ dos, Costa MH. Sentinel-1 imagery used for estimation of soil organic carbon by dual-polarization SAR vegetation indices [Internet]. Remote Sensing. 2023 ; 15 1-20.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/rs15235464
Vancouver
Santos EP dos, Moreira MC, Fernandes-Filho EI, Demattê JAM, Dionizio EA, Silva DD da, Cruz RRP, Moura-Bueno JM, Santos UJ dos, Costa MH. Sentinel-1 imagery used for estimation of soil organic carbon by dual-polarization SAR vegetation indices [Internet]. Remote Sensing. 2023 ; 15 1-20.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/rs15235464
Artigo de periodico
First Lidar Campaign in the Industrial Sites of Volta Redonda-RJ and Lorena-SP, Brazil (2022)
Lopes, Fábio Juliano da Silva ; Peixoto, Carlos Jose Todero ; Carvalho, Silvânia A. ; Catalani, Fernando ; Silva, Jonatan João da; Almeida, Rogério Menezes de ; Ribeiro, Fábio de Jesus; Fellows, Carlos. Eduardo; Landulfo, Eduardo ; Menegatti, Carlos Renato
Source: Remote Sensing. Unidade: EEL
Subjects: POLUIÇÃO INDUSTRIAL, AEROSSOL, CALIPSO
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ABNT
LOPES, Fábio Juliano da Silva et al. First Lidar Campaign in the Industrial Sites of Volta Redonda-RJ and Lorena-SP, Brazil. Remote Sensing, v. 14, n. 7, p. 1675-, 2022Tradução . . Disponível em: https://doi.org/10.3390/rs14071675. Acesso em: 15 nov. 2024.
APA
Lopes, F. J. da S., Peixoto, C. J. T., Carvalho, S. A., Catalani, F., Silva, J. J. da, Almeida, R. M. de, et al. (2022). First Lidar Campaign in the Industrial Sites of Volta Redonda-RJ and Lorena-SP, Brazil. Remote Sensing, 14( 7), 1675-. doi:10.3390/rs14071675
NLM
Lopes FJ da S, Peixoto CJT, Carvalho SA, Catalani F, Silva JJ da, Almeida RM de, Ribeiro F de J, Fellows CE, Landulfo E, Menegatti CR. First Lidar Campaign in the Industrial Sites of Volta Redonda-RJ and Lorena-SP, Brazil [Internet]. Remote Sensing. 2022 ;14( 7): 1675-.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/rs14071675
Vancouver
Lopes FJ da S, Peixoto CJT, Carvalho SA, Catalani F, Silva JJ da, Almeida RM de, Ribeiro F de J, Fellows CE, Landulfo E, Menegatti CR. First Lidar Campaign in the Industrial Sites of Volta Redonda-RJ and Lorena-SP, Brazil [Internet]. Remote Sensing. 2022 ;14( 7): 1675-.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/rs14071675
Artigo de periodico
Intercomparison of Planetary Boundary Layer Heights Using Remote Sensing Retrievals and ERA5 Reanalysis over Central Amazonia (2022)
Dias Júnior, Cléo Quaresma ; Carneiro, Rayonil Gomes ; Fisch, Gilberto ; D\2019Oliveira, Flavio Augusto Farias ; Sörgel, Matthias ; Botía, Santiago ; Machado, Luiz Augusto Toledo ; Wolff, Stefan ; Santos, Rosa Maria Nascimento dos ; Pöhlker, Christopher
Source: Remote Sensing. Unidade: IF
Assunto: FLORESTAS
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ABNT
DIAS JÚNIOR, Cléo Quaresma et al. Intercomparison of Planetary Boundary Layer Heights Using Remote Sensing Retrievals and ERA5 Reanalysis over Central Amazonia. Remote Sensing, v. 14, n. 18, 2022Tradução . . Disponível em: https://doi.org/10.3390/rs14184561. Acesso em: 15 nov. 2024.
APA
Dias Júnior, C. Q., Carneiro, R. G., Fisch, G., D\2019Oliveira, F. A. F., Sörgel, M., Botía, S., et al. (2022). Intercomparison of Planetary Boundary Layer Heights Using Remote Sensing Retrievals and ERA5 Reanalysis over Central Amazonia. Remote Sensing, 14( 18). doi:10.3390/rs14184561
NLM
Dias Júnior CQ, Carneiro RG, Fisch G, D\2019Oliveira FAF, Sörgel M, Botía S, Machado LAT, Wolff S, Santos RMN dos, Pöhlker C. Intercomparison of Planetary Boundary Layer Heights Using Remote Sensing Retrievals and ERA5 Reanalysis over Central Amazonia [Internet]. Remote Sensing. 2022 ; 14( 18):[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/rs14184561
Vancouver
Dias Júnior CQ, Carneiro RG, Fisch G, D\2019Oliveira FAF, Sörgel M, Botía S, Machado LAT, Wolff S, Santos RMN dos, Pöhlker C. Intercomparison of Planetary Boundary Layer Heights Using Remote Sensing Retrievals and ERA5 Reanalysis over Central Amazonia [Internet]. Remote Sensing. 2022 ; 14( 18):[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/rs14184561
Artigo de periodico
Canopy height mapping by Sentinel 1 and 2 Satellite Images, Airborne LiDAR Data, and Machine Learning (2022)
Almeida, Catherine Torres de ; Gerente, Jéssica; Campos, Jamerson Rodrigo dos Prazeres; Gomes Junior, Francisco Caruso; Providelo, Lucas Antonio; Marchiori, Guilherme; Chen, Xinjian
Source: Remote Sensing. Unidade: ESALQ
Subjects: APRENDIZADO COMPUTACIONAL, DOSSEL (BOTÂNICA), IMAGEAMENTO DE SATÉLITE, SENSORIAMENTO REMOTO, TECNOLOGIA LIDAR
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ALMEIDA, Catherine Torres de et al. Canopy height mapping by Sentinel 1 and 2 Satellite Images, Airborne LiDAR Data, and Machine Learning. Remote Sensing, v. 14, 2022Tradução . . Disponível em: https://doi.org/10.3390/rs14164112. Acesso em: 15 nov. 2024.
APA
Almeida, C. T. de, Gerente, J., Campos, J. R. dos P., Gomes Junior, F. C., Providelo, L. A., Marchiori, G., & Chen, X. (2022). Canopy height mapping by Sentinel 1 and 2 Satellite Images, Airborne LiDAR Data, and Machine Learning. Remote Sensing, 14. doi:10.3390/rs14164112
NLM
Almeida CT de, Gerente J, Campos JR dos P, Gomes Junior FC, Providelo LA, Marchiori G, Chen X. Canopy height mapping by Sentinel 1 and 2 Satellite Images, Airborne LiDAR Data, and Machine Learning [Internet]. Remote Sensing. 2022 ; 14[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/rs14164112
Vancouver
Almeida CT de, Gerente J, Campos JR dos P, Gomes Junior FC, Providelo LA, Marchiori G, Chen X. Canopy height mapping by Sentinel 1 and 2 Satellite Images, Airborne LiDAR Data, and Machine Learning [Internet]. Remote Sensing. 2022 ; 14[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/rs14164112
Artigo de periodico
Landslide Segmentation with Deep Learning: evaluating model generalization in rainfall-induced landslides in Brazil (2022)
Soares, Lucas Pedrosa ; Dias, Helen Cristina ; Garcia, Guilherme Pereira Bento ; Grohmann, Carlos Henrique
Source: Remote Sensing. Unidades: IEE, IGC
Assunto: DESLIZAMENTO DE TERRA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SOARES, Lucas Pedrosa et al. Landslide Segmentation with Deep Learning: evaluating model generalization in rainfall-induced landslides in Brazil. Remote Sensing, v. 14, n. 9, p. e2237/1-17, 2022Tradução . . Disponível em: https://doi.org/10.3390/rs14092237. Acesso em: 15 nov. 2024.
APA
Soares, L. P., Dias, H. C., Garcia, G. P. B., & Grohmann, C. H. (2022). Landslide Segmentation with Deep Learning: evaluating model generalization in rainfall-induced landslides in Brazil. Remote Sensing, 14( 9), e2237/1-17. doi:10.3390/rs14092237
NLM
Soares LP, Dias HC, Garcia GPB, Grohmann CH. Landslide Segmentation with Deep Learning: evaluating model generalization in rainfall-induced landslides in Brazil [Internet]. Remote Sensing. 2022 ; 14( 9):e2237/1-17.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/rs14092237
Vancouver
Soares LP, Dias HC, Garcia GPB, Grohmann CH. Landslide Segmentation with Deep Learning: evaluating model generalization in rainfall-induced landslides in Brazil [Internet]. Remote Sensing. 2022 ; 14( 9):e2237/1-17.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/rs14092237
Artigo de periodico
The Brazilian Soil Spectral Service (BraSpecS): a User-Friendly System for Global Soil Spectra Communication (2022)
Demattê, José Alexandre Melo ; Paiva, Ariane Francine da Silveira ; Poppiel, Raul Roberto ; Rosin, Nícolas Augusto ; Ruiz, Luis Fernando Chimelo ; Mello, Fellipe Alcantara de Oliveira ; Minasny, Budiman ; Grunwald, Sabine ; Ge, Yufeng; Ben Dor, Eyal ; Gholizadeh, Asa ; Gomez, Cecile ; Chabrillat, Sabine; Francos, Nicolas ; Ayoubi, Shamsollah ; Fiantis, Dian; Biney, James Kobina Mensah ; Wang, Changkun; Belal, Abdelaziz ; Naimi, Salman ; Hafshejani, Najmeh Asgari; Bellinaso, Henrique ; Moura-Bueno, Jean Michel; Silvero, Nélida E. Q
Source: Remote Sensing. Unidade: ESALQ
Subjects: AGRICULTURA DE PRECISÃO, ANÁLISE DO SOLO, ESPECTROSCOPIA INFRAVERMELHA, SOLOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
DEMATTÊ, José Alexandre Melo et al. The Brazilian Soil Spectral Service (BraSpecS): a User-Friendly System for Global Soil Spectra Communication. Remote Sensing, v. 14, p. 1-27, 2022Tradução . . Disponível em: https://doi.org/10.3390/rs14030740. Acesso em: 15 nov. 2024.
APA
Demattê, J. A. M., Paiva, A. F. da S., Poppiel, R. R., Rosin, N. A., Ruiz, L. F. C., Mello, F. A. de O., et al. (2022). The Brazilian Soil Spectral Service (BraSpecS): a User-Friendly System for Global Soil Spectra Communication. Remote Sensing, 14, 1-27. doi:10.3390/rs14030740
NLM
Demattê JAM, Paiva AF da S, Poppiel RR, Rosin NA, Ruiz LFC, Mello FA de O, Minasny B, Grunwald S, Ge Y, Ben Dor E, Gholizadeh A, Gomez C, Chabrillat S, Francos N, Ayoubi S, Fiantis D, Biney JKM, Wang C, Belal A, Naimi S, Hafshejani NA, Bellinaso H, Moura-Bueno JM, Silvero NEQ. The Brazilian Soil Spectral Service (BraSpecS): a User-Friendly System for Global Soil Spectra Communication [Internet]. Remote Sensing. 2022 ; 14 1-27.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/rs14030740
Vancouver
Demattê JAM, Paiva AF da S, Poppiel RR, Rosin NA, Ruiz LFC, Mello FA de O, Minasny B, Grunwald S, Ge Y, Ben Dor E, Gholizadeh A, Gomez C, Chabrillat S, Francos N, Ayoubi S, Fiantis D, Biney JKM, Wang C, Belal A, Naimi S, Hafshejani NA, Bellinaso H, Moura-Bueno JM, Silvero NEQ. The Brazilian Soil Spectral Service (BraSpecS): a User-Friendly System for Global Soil Spectra Communication [Internet]. Remote Sensing. 2022 ; 14 1-27.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/rs14030740
Artigo de periodico
Characterizing and Modeling Tropical Sandy Soils through VisNIR-SWIR, MIR Spectroscopy, and X-ray Fluorescence (2022)
Di Raimo, Luis Augusto Di Loreto ; Couto, Eduardo Guimarães ; Mello, Danilo César de ; Demattê, José Alexandre Melo ; Amorim, Ricardo Santos Silva ; Torres, Gilmar Nunes ; Bocuti, Edwaldo Dias; Veloso, Gustavo Vieira ; Poppiel, Raul Roberto ; Francelino, Márcio Rocha ; Fernandes-Filho, Elpídio Inácio
Source: Remote Sensing. Unidade: ESALQ
Subjects: AREIA, CLASSIFICAÇÃO DO SOLO, ESPECTROSCOPIA INFRAVERMELHA, FLUORESCÊNCIA, RAIOS X, SENSORIAMENTO REMOTO, SOLO ARENOSO, SOLO TROPICAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
DI RAIMO, Luis Augusto Di Loreto et al. Characterizing and Modeling Tropical Sandy Soils through VisNIR-SWIR, MIR Spectroscopy, and X-ray Fluorescence. Remote Sensing, v. 14, p. 1-24, 2022Tradução . . Disponível em: https://doi.org/10.3390/rs14194823. Acesso em: 15 nov. 2024.
APA
Di Raimo, L. A. D. L., Couto, E. G., Mello, D. C. de, Demattê, J. A. M., Amorim, R. S. S., Torres, G. N., et al. (2022). Characterizing and Modeling Tropical Sandy Soils through VisNIR-SWIR, MIR Spectroscopy, and X-ray Fluorescence. Remote Sensing, 14, 1-24. doi:10.3390/rs14194823
NLM
Di Raimo LADL, Couto EG, Mello DC de, Demattê JAM, Amorim RSS, Torres GN, Bocuti ED, Veloso GV, Poppiel RR, Francelino MR, Fernandes-Filho EI. Characterizing and Modeling Tropical Sandy Soils through VisNIR-SWIR, MIR Spectroscopy, and X-ray Fluorescence [Internet]. Remote Sensing. 2022 ; 14 1-24.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/rs14194823
Vancouver
Di Raimo LADL, Couto EG, Mello DC de, Demattê JAM, Amorim RSS, Torres GN, Bocuti ED, Veloso GV, Poppiel RR, Francelino MR, Fernandes-Filho EI. Characterizing and Modeling Tropical Sandy Soils through VisNIR-SWIR, MIR Spectroscopy, and X-ray Fluorescence [Internet]. Remote Sensing. 2022 ; 14 1-24.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/rs14194823
Artigo de periodico
Correction: Demattê et al.: The Brazilian soil spectral service (BraSpecS): a user-friendly system for global soil spectra communication. Remote Sens. 2022, 14, 740 (2022)
Demattê, José Alexandre Melo ; Paiva, Ariane Francine da Silveira ; Poppiel, Raul Roberto ; Rosin, Nícolas Augusto ; Ruiz, Luis Fernando Chimelo ; Mello, Fellipe Alcantara de Oliveira ; Minasny, Budiman ; Grunwald, Sabine ; Ge, Yufeng; Ben Dor, Eyal ; Gholizadeh, Asa ; Gomez, Cecile ; Chabrillat, Sabine; Francos, Nicolas ; Ayoubi, Shamsollah ; Fiantis, Dian; Biney, James Kobina Mensah ; Wang, Changkun; Belal, Abdelaziz ; Naimi, Salman ; Hafshejani, Najmeh Asgari; Bellinaso, Henrique ; Moura-Bueno, Jean Michel; Silvero, Nélida Elizabet Quiñonez
Source: Remote Sensing. Unidade: ESALQ
Subjects: AGRICULTURA DE PRECISÃO, ANÁLISE DO SOLO, ESPECTROSCOPIA INFRAVERMELHA, QUALIDADE DO SOLO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
DEMATTÊ, José Alexandre Melo et al. Correction: Demattê et al.: The Brazilian soil spectral service (BraSpecS): a user-friendly system for global soil spectra communication. Remote Sens. 2022, 14, 740. Remote Sensing, v. 14, p. 1-2, 2022Tradução . . Disponível em: https://doi.org/10.3390/rs14061459. Acesso em: 15 nov. 2024.
APA
Demattê, J. A. M., Paiva, A. F. da S., Poppiel, R. R., Rosin, N. A., Ruiz, L. F. C., Mello, F. A. de O., et al. (2022). Correction: Demattê et al.: The Brazilian soil spectral service (BraSpecS): a user-friendly system for global soil spectra communication. Remote Sens. 2022, 14, 740. Remote Sensing, 14, 1-2. doi:10.3390/rs14061459
NLM
Demattê JAM, Paiva AF da S, Poppiel RR, Rosin NA, Ruiz LFC, Mello FA de O, Minasny B, Grunwald S, Ge Y, Ben Dor E, Gholizadeh A, Gomez C, Chabrillat S, Francos N, Ayoubi S, Fiantis D, Biney JKM, Wang C, Belal A, Naimi S, Hafshejani NA, Bellinaso H, Moura-Bueno JM, Silvero NEQ. Correction: Demattê et al.: The Brazilian soil spectral service (BraSpecS): a user-friendly system for global soil spectra communication. Remote Sens. 2022, 14, 740 [Internet]. Remote Sensing. 2022 ; 14 1-2.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/rs14061459
Vancouver
Demattê JAM, Paiva AF da S, Poppiel RR, Rosin NA, Ruiz LFC, Mello FA de O, Minasny B, Grunwald S, Ge Y, Ben Dor E, Gholizadeh A, Gomez C, Chabrillat S, Francos N, Ayoubi S, Fiantis D, Biney JKM, Wang C, Belal A, Naimi S, Hafshejani NA, Bellinaso H, Moura-Bueno JM, Silvero NEQ. Correction: Demattê et al.: The Brazilian soil spectral service (BraSpecS): a user-friendly system for global soil spectra communication. Remote Sens. 2022, 14, 740 [Internet]. Remote Sensing. 2022 ; 14 1-2.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/rs14061459
Artigo de periodico
Mapping key indicators of forest restoration in the Amazon using a low-cost drone and artificial intelligence (2022)
Albuquerque, Rafael Walter ; Vieira, Daniel Luis Mascia; Ferreira, Manuel Eduardo; Soares, Lucas Pedrosa; Olsen, Søren Ingvor; Araujo, Luciana Spinelli; Vicente, Luiz Eduardo; Tymus, Julio Ricardo Caetano; Balieiro, Cintia Palheta; Matsumoto, Marcelo Hiromiti ; Grohmann, Carlos Henrique
Source: Remote Sensing. Unidades: ESALQ, IEE, IGC
Subjects: AEROFOTOGRAMETRIA, AERONAVES NÃO TRIPULADAS, APRENDIZADO COMPUTACIONAL, ÁRVORES FLORESTAIS, DOSSEL (BOTÂNICA), ECOLOGIA DA RESTAURAÇÃO, HETEROGENEIDADE
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ABNT
ALBUQUERQUE, Rafael Walter et al. Mapping key indicators of forest restoration in the Amazon using a low-cost drone and artificial intelligence. Remote Sensing, v. 14, p. 1-28, 2022Tradução . . Disponível em: https://doi.org/10.3390/rs14040830. Acesso em: 15 nov. 2024.
APA
Albuquerque, R. W., Vieira, D. L. M., Ferreira, M. E., Soares, L. P., Olsen, S. I., Araujo, L. S., et al. (2022). Mapping key indicators of forest restoration in the Amazon using a low-cost drone and artificial intelligence. Remote Sensing, 14, 1-28. doi:10.3390/rs14040830
NLM
Albuquerque RW, Vieira DLM, Ferreira ME, Soares LP, Olsen SI, Araujo LS, Vicente LE, Tymus JRC, Balieiro CP, Matsumoto MH, Grohmann CH. Mapping key indicators of forest restoration in the Amazon using a low-cost drone and artificial intelligence [Internet]. Remote Sensing. 2022 ; 14 1-28.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/rs14040830
Vancouver
Albuquerque RW, Vieira DLM, Ferreira ME, Soares LP, Olsen SI, Araujo LS, Vicente LE, Tymus JRC, Balieiro CP, Matsumoto MH, Grohmann CH. Mapping key indicators of forest restoration in the Amazon using a low-cost drone and artificial intelligence [Internet]. Remote Sensing. 2022 ; 14 1-28.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/rs14040830

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