ReP USP - Resultado da busca

Artigo de periodico
deadtrees.earth - An open-access and interactive database for centimeter-scale aerial imagery to uncover global tree mortality dynamics (2026)
Mosig, Clemens ; Vajna-Jehle, Janusch; Fabian Fassnacht; Ferreira, Matheus Pinheiro ; Fischer, Fabian Jörg
Source: Remote Sensing of Environment. Unidade: ESALQ
Subjects: AERONAVES NÃO TRIPULADAS, ÁRVORES FLORESTAIS, BANCO DE DADOS, FLORESTAS, IMAGEAMENTO DE SATÉLITE, PLATAFORMA DIGITAL, SENSORIAMENTO REMOTO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MOSIG, Clemens et al. deadtrees.earth - An open-access and interactive database for centimeter-scale aerial imagery to uncover global tree mortality dynamics. Remote Sensing of Environment, v. 332, p. 1-15, 2026Tradução . . Disponível em: https://doi.org/10.1016/j.rse.2025.115027. Acesso em: 17 abr. 2026.
APA
Mosig, C., Vajna-Jehle, J., Fabian Fassnacht,, Ferreira, M. P., & Fischer, F. J. (2026). deadtrees.earth - An open-access and interactive database for centimeter-scale aerial imagery to uncover global tree mortality dynamics. Remote Sensing of Environment, 332, 1-15. doi:10.1016/j.rse.2025.115027
NLM
Mosig C, Vajna-Jehle J, Fabian Fassnacht, Ferreira MP, Fischer FJ. deadtrees.earth - An open-access and interactive database for centimeter-scale aerial imagery to uncover global tree mortality dynamics [Internet]. Remote Sensing of Environment. 2026 ; 332 1-15.[citado 2026 abr. 17 ] Available from: https://doi.org/10.1016/j.rse.2025.115027
Vancouver
Mosig C, Vajna-Jehle J, Fabian Fassnacht, Ferreira MP, Fischer FJ. deadtrees.earth - An open-access and interactive database for centimeter-scale aerial imagery to uncover global tree mortality dynamics [Internet]. Remote Sensing of Environment. 2026 ; 332 1-15.[citado 2026 abr. 17 ] Available from: https://doi.org/10.1016/j.rse.2025.115027
Artigo de periodico
Soil laboratory and satellite spectral data filtering: a spectral quality protocol (SQuaP) (2026)
Bartsch, Bruno dos Anjos ; Demattê, José Alexandre Melo ; Tziolas, Nikolaos; Rosas, Jorge Tadeu Fim ; Sousa, Gabriel Pimenta Barbosa de; Rosin, Nicolas Augusto ; Gallios, Giannis; Melo, Borges Marfrann Dias
Source: Remote Sensing of Environment. Unidade: ESALQ
Subjects: BANCO DE DADOS, ESPECTROSCOPIA, IMAGEAMENTO DE SATÉLITE, SENSORIAMENTO REMOTO, SOLOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
BARTSCH, Bruno dos Anjos et al. Soil laboratory and satellite spectral data filtering: a spectral quality protocol (SQuaP). Remote Sensing of Environment, v. 333, p. 1-17, 2026Tradução . . Disponível em: https://doi.org/10.1016/j.rse.2025.115144. Acesso em: 17 abr. 2026.
APA
Bartsch, B. dos A., Demattê, J. A. M., Tziolas, N., Rosas, J. T. F., Sousa, G. P. B. de, Rosin, N. A., et al. (2026). Soil laboratory and satellite spectral data filtering: a spectral quality protocol (SQuaP). Remote Sensing of Environment, 333, 1-17. doi:10.1016/j.rse.2025.115144
NLM
Bartsch B dos A, Demattê JAM, Tziolas N, Rosas JTF, Sousa GPB de, Rosin NA, Gallios G, Melo BMD. Soil laboratory and satellite spectral data filtering: a spectral quality protocol (SQuaP) [Internet]. Remote Sensing of Environment. 2026 ; 333 1-17.[citado 2026 abr. 17 ] Available from: https://doi.org/10.1016/j.rse.2025.115144
Vancouver
Bartsch B dos A, Demattê JAM, Tziolas N, Rosas JTF, Sousa GPB de, Rosin NA, Gallios G, Melo BMD. Soil laboratory and satellite spectral data filtering: a spectral quality protocol (SQuaP) [Internet]. Remote Sensing of Environment. 2026 ; 333 1-17.[citado 2026 abr. 17 ] Available from: https://doi.org/10.1016/j.rse.2025.115144
Artigo de periodico
Predicting drought vulnerability with leaf reflectance spectra in Amazonian trees (2025)
Garcia, Maquelle N.; Tameirão, Lucas B. S.; Schietti, Juliana; Aleixo, Izabela; Domingues, Tomas Ferreira ; Huemmrich, K. Fred; Campell, Petya K. E.; Albert, Loren P.
Source: Remote Sensing of Environment. Unidade: FFCLRP
Subjects: ÁRVORES, SENSORIAMENTO REMOTO, SECA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
GARCIA, Maquelle N. et al. Predicting drought vulnerability with leaf reflectance spectra in Amazonian trees. Remote Sensing of Environment, v. 318, p. 1-16, 2025Tradução . . Disponível em: https://doi.org/10.1016/j.rse.2024.114562. Acesso em: 17 abr. 2026.
APA
Garcia, M. N., Tameirão, L. B. S., Schietti, J., Aleixo, I., Domingues, T. F., Huemmrich, K. F., et al. (2025). Predicting drought vulnerability with leaf reflectance spectra in Amazonian trees. Remote Sensing of Environment, 318, 1-16. doi:10.1016/j.rse.2024.114562
NLM
Garcia MN, Tameirão LBS, Schietti J, Aleixo I, Domingues TF, Huemmrich KF, Campell PKE, Albert LP. Predicting drought vulnerability with leaf reflectance spectra in Amazonian trees [Internet]. Remote Sensing of Environment. 2025 ; 318 1-16.[citado 2026 abr. 17 ] Available from: https://doi.org/10.1016/j.rse.2024.114562
Vancouver
Garcia MN, Tameirão LBS, Schietti J, Aleixo I, Domingues TF, Huemmrich KF, Campell PKE, Albert LP. Predicting drought vulnerability with leaf reflectance spectra in Amazonian trees [Internet]. Remote Sensing of Environment. 2025 ; 318 1-16.[citado 2026 abr. 17 ] Available from: https://doi.org/10.1016/j.rse.2024.114562
Artigo de periodico
Bridging the gap between laboratory VNIR-SWIR spectra and Landsat-8 bare soil composite image for soil organic carbon prediction (2025)
Hong, Yongsheng; Chen, Yiyun; Chen, Songchao; Wang, Yanyu; Hu, Wenyou; Ye, Su; Song, Xiaodong; Liu, Feng; Zhao, Yongcun; Demattê, José Alexandre Melo ; Shi, Liangsheng; Shen, Huanfeng; Shi, Zhou; Zhang, Ganlin; Liu, Yaolin
Source: Remote Sensing of Environment. Unidade: ESALQ
Subjects: APRENDIZADO COMPUTACIONAL, CARBONO, ESPECTROSCOPIA INFRAVERMELHA, IMAGEAMENTO DE SATÉLITE, MAPEAMENTO DO SOLO, MONITORAÇÃO DOS SOLOS, SENSORIAMENTO REMOTO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
HONG, Yongsheng et al. Bridging the gap between laboratory VNIR-SWIR spectra and Landsat-8 bare soil composite image for soil organic carbon prediction. Remote Sensing of Environment, v. 328, p. 1-24, 2025Tradução . . Disponível em: https://doi.org/10.1016/j.rse.2025.114874. Acesso em: 17 abr. 2026.
APA
Hong, Y., Chen, Y., Chen, S., Wang, Y., Hu, W., Ye, S., et al. (2025). Bridging the gap between laboratory VNIR-SWIR spectra and Landsat-8 bare soil composite image for soil organic carbon prediction. Remote Sensing of Environment, 328, 1-24. doi:10.1016/j.rse.2025.114874
NLM
Hong Y, Chen Y, Chen S, Wang Y, Hu W, Ye S, Song X, Liu F, Zhao Y, Demattê JAM, Shi L, Shen H, Shi Z, Zhang G, Liu Y. Bridging the gap between laboratory VNIR-SWIR spectra and Landsat-8 bare soil composite image for soil organic carbon prediction [Internet]. Remote Sensing of Environment. 2025 ; 328 1-24.[citado 2026 abr. 17 ] Available from: https://doi.org/10.1016/j.rse.2025.114874
Vancouver
Hong Y, Chen Y, Chen S, Wang Y, Hu W, Ye S, Song X, Liu F, Zhao Y, Demattê JAM, Shi L, Shen H, Shi Z, Zhang G, Liu Y. Bridging the gap between laboratory VNIR-SWIR spectra and Landsat-8 bare soil composite image for soil organic carbon prediction [Internet]. Remote Sensing of Environment. 2025 ; 328 1-24.[citado 2026 abr. 17 ] Available from: https://doi.org/10.1016/j.rse.2025.114874
Artigo de periodico
Remote sensing of the Earth’s soil color in space and time (2023)
Rizzo, Rodnei ; Wadoux, Alexandre M. J; Demattê, José Alexandre Melo ; Minasny, Budiman ; Barrón, Vidal; Ben-Dor, Eyal ; Francos, Nicolas ; Savin, Igor; Poppiel, Raul Roberto ; Silvero, Nelida Elizabet Quiñonez ; Terra, Fabrício da Silva; Rosin, Nícolas Augusto ; Rosas, Jorge Tadeu Fim ; Greschuk, Lucas Tadeu ; Ballester, Maria Victoria Ramos ; Gómez, Andrés Mauricio Rico ; Belllinaso, Henrique; Safanelli, José Lucas ; Chabrillat, Sabine ; Fiorio, Peterson Roberto ; Das, Bhabani Sankar ; Malone, Brendan P; Zalidis, George; Tziolas, Nikolaos ; Tsakiridis, Nikolaos; Karyotis, Konstantinos; Samarinas, Nikiforos; Kalopesa, Eleni; Gholizadeh, Asa ; Shepherd, Keith D; Milewski, Robert; Vaudour, Emmanuelle; Wang, Changkun; Salama, Elsayed Said Mohamed
Source: Remote Sensing of Environment. Unidades: ESALQ, CENA
Subjects: COR DO SOLO, ESPECTROSCOPIA, IMAGEAMENTO DE SATÉLITE, MONITORAÇÃO DOS SOLOS, SENSORIAMENTO REMOTO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
RIZZO, Rodnei et al. Remote sensing of the Earth’s soil color in space and time. Remote Sensing of Environment, v. 299, p. 1-15, 2023Tradução . . Disponível em: https://doi.org/10.1016/j.rse.2023.113845. Acesso em: 17 abr. 2026.
APA
Rizzo, R., Wadoux, A. M. J., Demattê, J. A. M., Minasny, B., Barrón, V., Ben-Dor, E., et al. (2023). Remote sensing of the Earth’s soil color in space and time. Remote Sensing of Environment, 299, 1-15. doi:10.1016/j.rse.2023.113845
NLM
Rizzo R, Wadoux AMJ, Demattê JAM, Minasny B, Barrón V, Ben-Dor E, Francos N, Savin I, Poppiel RR, Silvero NEQ, Terra F da S, Rosin NA, Rosas JTF, Greschuk LT, Ballester MVR, Gómez AMR, Belllinaso H, Safanelli JL, Chabrillat S, Fiorio PR, Das BS, Malone BP, Zalidis G, Tziolas N, Tsakiridis N, Karyotis K, Samarinas N, Kalopesa E, Gholizadeh A, Shepherd KD, Milewski R, Vaudour E, Wang C, Salama ESM. Remote sensing of the Earth’s soil color in space and time [Internet]. Remote Sensing of Environment. 2023 ; 299 1-15.[citado 2026 abr. 17 ] Available from: https://doi.org/10.1016/j.rse.2023.113845
Vancouver
Rizzo R, Wadoux AMJ, Demattê JAM, Minasny B, Barrón V, Ben-Dor E, Francos N, Savin I, Poppiel RR, Silvero NEQ, Terra F da S, Rosin NA, Rosas JTF, Greschuk LT, Ballester MVR, Gómez AMR, Belllinaso H, Safanelli JL, Chabrillat S, Fiorio PR, Das BS, Malone BP, Zalidis G, Tziolas N, Tsakiridis N, Karyotis K, Samarinas N, Kalopesa E, Gholizadeh A, Shepherd KD, Milewski R, Vaudour E, Wang C, Salama ESM. Remote sensing of the Earth’s soil color in space and time [Internet]. Remote Sensing of Environment. 2023 ; 299 1-15.[citado 2026 abr. 17 ] Available from: https://doi.org/10.1016/j.rse.2023.113845
Artigo de periodico
Distinguishing forest types in restored tropical landscapes with UAV-borne LIDAR (2023)
Scheeres, Janneke; Jong, Johan de; Brede, Benjamin; Brancalion, Pedro Henrique Santin ; Broadbent, Eben North ; Zambrano, Angelica Maria Almeyda; Gorgens, Eric Bastos; Silva, Carlos Alberto; Valbuena, Ruben; Molin, Paulo; Stark, Scott; Rodrigues, Ricardo Ribeiro ; Santoro, Giulio Brossi ; Resende, Angélica Faria ; Almeida, Catherine Torres de ; Almeida, Danilo Roberti Alves de
Source: Remote Sensing of Environment. Unidade: ESALQ
Subjects: AERONAVES NÃO TRIPULADAS, ECOLOGIA DA RESTAURAÇÃO, FLORESTAS TROPICAIS, PAISAGEM, SENSORIAMENTO REMOTO, TECNOLOGIA LIDAR, MATA ATLÂNTICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SCHEERES, Janneke et al. Distinguishing forest types in restored tropical landscapes with UAV-borne LIDAR. Remote Sensing of Environment, v. 290, p. 1-14, 2023Tradução . . Disponível em: https://doi.org/10.1016/j.rse.2023.113533. Acesso em: 17 abr. 2026.
APA
Scheeres, J., Jong, J. de, Brede, B., Brancalion, P. H. S., Broadbent, E. N., Zambrano, A. M. A., et al. (2023). Distinguishing forest types in restored tropical landscapes with UAV-borne LIDAR. Remote Sensing of Environment, 290, 1-14. doi:10.1016/j.rse.2023.113533
NLM
Scheeres J, Jong J de, Brede B, Brancalion PHS, Broadbent EN, Zambrano AMA, Gorgens EB, Silva CA, Valbuena R, Molin P, Stark S, Rodrigues RR, Santoro GB, Resende AF, Almeida CT de, Almeida DRA de. Distinguishing forest types in restored tropical landscapes with UAV-borne LIDAR [Internet]. Remote Sensing of Environment. 2023 ; 290 1-14.[citado 2026 abr. 17 ] Available from: https://doi.org/10.1016/j.rse.2023.113533
Vancouver
Scheeres J, Jong J de, Brede B, Brancalion PHS, Broadbent EN, Zambrano AMA, Gorgens EB, Silva CA, Valbuena R, Molin P, Stark S, Rodrigues RR, Santoro GB, Resende AF, Almeida CT de, Almeida DRA de. Distinguishing forest types in restored tropical landscapes with UAV-borne LIDAR [Internet]. Remote Sensing of Environment. 2023 ; 290 1-14.[citado 2026 abr. 17 ] Available from: https://doi.org/10.1016/j.rse.2023.113533
Artigo de periodico
How much inundation occurs in the Amazon River basin? (2022)
Fleischmann, Ayan Santos; Papa, Fabrice; Fassoni-Andrade, Alice; Melack, John M; Wongchuig, Sly; Paiva, Rodrigo Cauduro Dias; Hamilton, Stephen K; Fluet-Chouinard, Etienne; Barbedo, Rafael; Aires, Filipe; Al Bitar, Ahmad; Bonnet, Marie-Paule; Coe, Michael; Ferreira-Ferreira, Jefferson; Hess, Laura; Jensen, Katherine; McDonald, Kyle; Ovando, Alex; Park, Edward; Parrens, Marie; Pinel, Sébastien; Prigent, Catherine; Resende, Angélica Faria de ; Revel, Menaka; Rosenqvist, Ake; Rosenqvist, Jessica; Rudorff, Conrado; Silva, Thiago S. F; Yamazaki, Dai; Collischonn, Walter
Source: Remote Sensing of Environment. Unidade: ESALQ
Subjects: BACIA HIDROGRÁFICA, INUNDAÇÕES, SENSORIAMENTO REMOTO, RIO AMAZONAS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
FLEISCHMANN, Ayan Santos et al. How much inundation occurs in the Amazon River basin?. Remote Sensing of Environment, v. 278, p. 1-24, 2022Tradução . . Disponível em: https://doi.org/10.1016/j.rse.2022.113099. Acesso em: 17 abr. 2026.
APA
Fleischmann, A. S., Papa, F., Fassoni-Andrade, A., Melack, J. M., Wongchuig, S., Paiva, R. C. D., et al. (2022). How much inundation occurs in the Amazon River basin? Remote Sensing of Environment, 278, 1-24. doi:10.1016/j.rse.2022.113099
NLM
Fleischmann AS, Papa F, Fassoni-Andrade A, Melack JM, Wongchuig S, Paiva RCD, Hamilton SK, Fluet-Chouinard E, Barbedo R, Aires F, Al Bitar A, Bonnet M-P, Coe M, Ferreira-Ferreira J, Hess L, Jensen K, McDonald K, Ovando A, Park E, Parrens M, Pinel S, Prigent C, Resende AF de, Revel M, Rosenqvist A, Rosenqvist J, Rudorff C, Silva TSF, Yamazaki D, Collischonn W. How much inundation occurs in the Amazon River basin? [Internet]. Remote Sensing of Environment. 2022 ; 278 1-24.[citado 2026 abr. 17 ] Available from: https://doi.org/10.1016/j.rse.2022.113099
Vancouver
Fleischmann AS, Papa F, Fassoni-Andrade A, Melack JM, Wongchuig S, Paiva RCD, Hamilton SK, Fluet-Chouinard E, Barbedo R, Aires F, Al Bitar A, Bonnet M-P, Coe M, Ferreira-Ferreira J, Hess L, Jensen K, McDonald K, Ovando A, Park E, Parrens M, Pinel S, Prigent C, Resende AF de, Revel M, Rosenqvist A, Rosenqvist J, Rudorff C, Silva TSF, Yamazaki D, Collischonn W. How much inundation occurs in the Amazon River basin? [Internet]. Remote Sensing of Environment. 2022 ; 278 1-24.[citado 2026 abr. 17 ] Available from: https://doi.org/10.1016/j.rse.2022.113099
Artigo de periodico
Large scale multi-layer fuel load characterization in tropical savanna using GEDI spaceborne lidar data (2022)
Leite, Rodrigo Vieira; Silva, Carlos Alberto; Broadbent, Eben North ; Amaral, Cibele Hummel do ; Liesenberg, Veraldo; Almeida, Danilo Roberti Alves de ; Brancalion, Pedro Henrique Santin
Source: Remote Sensing of Environment. Unidade: ESALQ
Subjects: SAVANA, TECNOLOGIA LIDAR, COMBUSTÍVEIS, SENSORIAMENTO REMOTO, CERRADO, INCÊNDIOS FLORESTAIS, FOGO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
LEITE, Rodrigo Vieira et al. Large scale multi-layer fuel load characterization in tropical savanna using GEDI spaceborne lidar data. Remote Sensing of Environment, v. 268, p. 1-19, 2022Tradução . . Disponível em: https://doi.org/10.1016/j.rse.2021.112764. Acesso em: 17 abr. 2026.
APA
Leite, R. V., Silva, C. A., Broadbent, E. N., Amaral, C. H. do, Liesenberg, V., Almeida, D. R. A. de, & Brancalion, P. H. S. (2022). Large scale multi-layer fuel load characterization in tropical savanna using GEDI spaceborne lidar data. Remote Sensing of Environment, 268, 1-19. doi:10.1016/j.rse.2021.112764
NLM
Leite RV, Silva CA, Broadbent EN, Amaral CH do, Liesenberg V, Almeida DRA de, Brancalion PHS. Large scale multi-layer fuel load characterization in tropical savanna using GEDI spaceborne lidar data [Internet]. Remote Sensing of Environment. 2022 ; 268 1-19.[citado 2026 abr. 17 ] Available from: https://doi.org/10.1016/j.rse.2021.112764
Vancouver
Leite RV, Silva CA, Broadbent EN, Amaral CH do, Liesenberg V, Almeida DRA de, Brancalion PHS. Large scale multi-layer fuel load characterization in tropical savanna using GEDI spaceborne lidar data [Internet]. Remote Sensing of Environment. 2022 ; 268 1-19.[citado 2026 abr. 17 ] Available from: https://doi.org/10.1016/j.rse.2021.112764
Artigo de periodico
Monitoring restored tropical forest diversity and structure through UAV-borne hyperspectral and lidar fusion (2021)
Almeida, Danilo Roberti Alves de ; Broadbent, Eben North ; Ferreira, Matheus Pinheiro; Meli, Paula ; Zambrano, Angelica Maria Almeyda ; Gorgens, Eric Bastos ; Resende, Angelica Faria ; Almeida, Catherine Torres de ; Amaral, Cibele Hummel do ; Dalla Corte, Ana Paula; Silva, Carlos Alberto; Romanelli, João Paulo ; Prata, Gabriel Atticciati ; Papa, Daniel de Almeida ; Stark, Scott C; Valbuena, Ruben; Nelson, Bruce Walker; Guillemot, Joannes ; Féret, Jean-Baptiste; Chazdon, Robin; Brancalion, Pedro Henrique Santin
Source: Remote Sensing of Environment. Unidade: ESALQ
Subjects: AERONAVES NÃO TRIPULADAS, BIODIVERSIDADE, ECOLOGIA DA RESTAURAÇÃO, FLORESTAS TROPICAIS, MONITORAMENTO AMBIENTAL, SENSORIAMENTO REMOTO, TECNOLOGIA LIDAR
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ALMEIDA, Danilo Roberti Alves de et al. Monitoring restored tropical forest diversity and structure through UAV-borne hyperspectral and lidar fusion. Remote Sensing of Environment, v. 264, p. 1-13, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.rse.2021.112582. Acesso em: 17 abr. 2026.
APA
Almeida, D. R. A. de, Broadbent, E. N., Ferreira, M. P., Meli, P., Zambrano, A. M. A., Gorgens, E. B., et al. (2021). Monitoring restored tropical forest diversity and structure through UAV-borne hyperspectral and lidar fusion. Remote Sensing of Environment, 264, 1-13. doi:10.1016/j.rse.2021.112582
NLM
Almeida DRA de, Broadbent EN, Ferreira MP, Meli P, Zambrano AMA, Gorgens EB, Resende AF, Almeida CT de, Amaral CH do, Dalla Corte AP, Silva CA, Romanelli JP, Prata GA, Papa D de A, Stark SC, Valbuena R, Nelson BW, Guillemot J, Féret J-B, Chazdon R, Brancalion PHS. Monitoring restored tropical forest diversity and structure through UAV-borne hyperspectral and lidar fusion [Internet]. Remote Sensing of Environment. 2021 ; 264 1-13.[citado 2026 abr. 17 ] Available from: https://doi.org/10.1016/j.rse.2021.112582
Vancouver
Almeida DRA de, Broadbent EN, Ferreira MP, Meli P, Zambrano AMA, Gorgens EB, Resende AF, Almeida CT de, Amaral CH do, Dalla Corte AP, Silva CA, Romanelli JP, Prata GA, Papa D de A, Stark SC, Valbuena R, Nelson BW, Guillemot J, Féret J-B, Chazdon R, Brancalion PHS. Monitoring restored tropical forest diversity and structure through UAV-borne hyperspectral and lidar fusion [Internet]. Remote Sensing of Environment. 2021 ; 264 1-13.[citado 2026 abr. 17 ] Available from: https://doi.org/10.1016/j.rse.2021.112582
Artigo de periodico
Wind speed and mesoscale features drive net autotrophy in the South Atlantic Ocean (2021)
Ford, Daniel; Tilstone, Gavin H.; Shutler, Jamie D.; Kitidis, Vassilis; Lobanova, Polina; Schwarz, Jill; Poulton, Alex J.; Serret, Pablo; Lamont, Tarron ; Chuqui, Mateus Gustavo ; Barlow, Ray; Lozano, Jose; Kampel, Milton ; Brandini, Frederico Pereira
Source: Remote Sensing of Environment. Unidade: IO
Assunto: OCEANOGRAFIA FÍSICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
FORD, Daniel et al. Wind speed and mesoscale features drive net autotrophy in the South Atlantic Ocean. Remote Sensing of Environment, v. 260, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.rse.2021.112435. Acesso em: 17 abr. 2026.
APA
Ford, D., Tilstone, G. H., Shutler, J. D., Kitidis, V., Lobanova, P., Schwarz, J., et al. (2021). Wind speed and mesoscale features drive net autotrophy in the South Atlantic Ocean. Remote Sensing of Environment, 260. doi:10.1016/j.rse.2021.112435
NLM
Ford D, Tilstone GH, Shutler JD, Kitidis V, Lobanova P, Schwarz J, Poulton AJ, Serret P, Lamont T, Chuqui MG, Barlow R, Lozano J, Kampel M, Brandini FP. Wind speed and mesoscale features drive net autotrophy in the South Atlantic Ocean [Internet]. Remote Sensing of Environment. 2021 ; 260[citado 2026 abr. 17 ] Available from: https://doi.org/10.1016/j.rse.2021.112435
Vancouver
Ford D, Tilstone GH, Shutler JD, Kitidis V, Lobanova P, Schwarz J, Poulton AJ, Serret P, Lamont T, Chuqui MG, Barlow R, Lozano J, Kampel M, Brandini FP. Wind speed and mesoscale features drive net autotrophy in the South Atlantic Ocean [Internet]. Remote Sensing of Environment. 2021 ; 260[citado 2026 abr. 17 ] Available from: https://doi.org/10.1016/j.rse.2021.112435
Artigo de periodico
Soil variability and quantification based on Sentinel-2 and Landsat-8 bare soil images: A comparison (2021)
Silvero, Nélida Elizabet Quiñonez ; Demattê, José Alexandre Melo ; Amorim, Merilyn Taynara Accorsi; Santos, Natasha Valadares dos ; Rizzo, Rodnei ; Safanelli, José Lucas ; Poppiel, Raul Roberto ; Mendes, Wanderson de Sousa ; Bonfatti, Benito Roberto
Source: Remote Sensing of Environment. Unidade: ESALQ
Subjects: ANÁLISE DE SÉRIES TEMPORAIS, APRENDIZADO COMPUTACIONAL, IMAGEAMENTO DE SATÉLITE, MAPEAMENTO DO SOLO, SENSORIAMENTO REMOTO, VARIABILIDADE ESPACIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SILVERO, Nélida Elizabet Quiñonez et al. Soil variability and quantification based on Sentinel-2 and Landsat-8 bare soil images: A comparison. Remote Sensing of Environment, v. 252, n. art. 112117, p. 1-19, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.rse.2020.112117. Acesso em: 17 abr. 2026.
APA
Silvero, N. E. Q., Demattê, J. A. M., Amorim, M. T. A., Santos, N. V. dos, Rizzo, R., Safanelli, J. L., et al. (2021). Soil variability and quantification based on Sentinel-2 and Landsat-8 bare soil images: A comparison. Remote Sensing of Environment, 252( art. 112117), 1-19. doi:10.1016/j.rse.2020.112117
NLM
Silvero NEQ, Demattê JAM, Amorim MTA, Santos NV dos, Rizzo R, Safanelli JL, Poppiel RR, Mendes W de S, Bonfatti BR. Soil variability and quantification based on Sentinel-2 and Landsat-8 bare soil images: A comparison [Internet]. Remote Sensing of Environment. 2021 ; 252( art. 112117): 1-19.[citado 2026 abr. 17 ] Available from: https://doi.org/10.1016/j.rse.2020.112117
Vancouver
Silvero NEQ, Demattê JAM, Amorim MTA, Santos NV dos, Rizzo R, Safanelli JL, Poppiel RR, Mendes W de S, Bonfatti BR. Soil variability and quantification based on Sentinel-2 and Landsat-8 bare soil images: A comparison [Internet]. Remote Sensing of Environment. 2021 ; 252( art. 112117): 1-19.[citado 2026 abr. 17 ] Available from: https://doi.org/10.1016/j.rse.2020.112117
Artigo de periodico
Estimating leaf mass per area and equivalent water thickness based on leaf optical properties: potential and limitations of physical modeling and machine learning (2019)
Feret, J-B; Maire, G. le; Jay, S; Berveiller, D; Bendoula, R; Hmimina, G; Cheraiet, A; Oliveira, J. C; Ponzoni, F. J; Solanki, T; Boissieu, F. de; Chave, J; Nouvellon, Yann Pierre ; Porcar-Castell, A; Proisy, C; Soudani, K; Gastellu-Etchegorry, J-P; Lefevre-Fonollosa, M-J
Source: Remote Sensing of Environment. Unidade: ESALQ
Subjects: ESPECTROSCOPIA INFRAVERMELHA, FOLHAS (PLANTAS), SENSORIAMENTO REMOTO, VEGETAÇÃO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
FERET, J-B et al. Estimating leaf mass per area and equivalent water thickness based on leaf optical properties: potential and limitations of physical modeling and machine learning. Remote Sensing of Environment, v. 231, p. 1-14, 2019Tradução . . Disponível em: https://doi.org/10.1016/j.rse.2018.11.002. Acesso em: 17 abr. 2026.
APA
Feret, J. -B., Maire, G. le, Jay, S., Berveiller, D., Bendoula, R., Hmimina, G., et al. (2019). Estimating leaf mass per area and equivalent water thickness based on leaf optical properties: potential and limitations of physical modeling and machine learning. Remote Sensing of Environment, 231, 1-14. doi:10.1016/j.rse.2018.11.002
NLM
Feret J-B, Maire G le, Jay S, Berveiller D, Bendoula R, Hmimina G, Cheraiet A, Oliveira JC, Ponzoni FJ, Solanki T, Boissieu F de, Chave J, Nouvellon YP, Porcar-Castell A, Proisy C, Soudani K, Gastellu-Etchegorry J-P, Lefevre-Fonollosa M-J. Estimating leaf mass per area and equivalent water thickness based on leaf optical properties: potential and limitations of physical modeling and machine learning [Internet]. Remote Sensing of Environment. 2019 ; 231 1-14.[citado 2026 abr. 17 ] Available from: https://doi.org/10.1016/j.rse.2018.11.002
Vancouver
Feret J-B, Maire G le, Jay S, Berveiller D, Bendoula R, Hmimina G, Cheraiet A, Oliveira JC, Ponzoni FJ, Solanki T, Boissieu F de, Chave J, Nouvellon YP, Porcar-Castell A, Proisy C, Soudani K, Gastellu-Etchegorry J-P, Lefevre-Fonollosa M-J. Estimating leaf mass per area and equivalent water thickness based on leaf optical properties: potential and limitations of physical modeling and machine learning [Internet]. Remote Sensing of Environment. 2019 ; 231 1-14.[citado 2026 abr. 17 ] Available from: https://doi.org/10.1016/j.rse.2018.11.002
Artigo de periodico
On the relationship between sub-daily instantaneous and daily total gross primary production: Implications for interpreting satellite-based SIF retrievals (2018)
Zhang, Yao; Xiao, Xiangming; Zhang, Yongguang; Wolf, Sebastian; Zhou, Sha; Joiner, Joanna; Guanter, Luis; Verma, Manish; Sun, Ying; Yang, Xi; Paul-Limoges, Eugénie; Gough, Christopher M; Wohlfahrt, Georg; Gioli, Beniamino; Van der Tol, Christiaan; Nouvellon, Yann Pierre ; Lund, Magnus; Grandcourt, Agnès de
Source: Remote Sensing of Environment. Unidade: ESALQ
Subjects: CICLO DO CARBONO, CLOROFILA, ECOFISIOLOGIA VEGETAL, FENOLOGIA, FLUORESCÊNCIA, FOTOSSÍNTESE, IMAGEAMENTO DE SATÉLITE, RADIAÇÃO SOLAR E TERRESTRE
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ZHANG, Yao et al. On the relationship between sub-daily instantaneous and daily total gross primary production: Implications for interpreting satellite-based SIF retrievals. Remote Sensing of Environment, v. 205, p. 276-289, 2018Tradução . . Disponível em: https://doi.org/10.1016/j.rse.2017.12.009. Acesso em: 17 abr. 2026.
APA
Zhang, Y., Xiao, X., Zhang, Y., Wolf, S., Zhou, S., Joiner, J., et al. (2018). On the relationship between sub-daily instantaneous and daily total gross primary production: Implications for interpreting satellite-based SIF retrievals. Remote Sensing of Environment, 205, 276-289. doi:10.1016/j.rse.2017.12.009
NLM
Zhang Y, Xiao X, Zhang Y, Wolf S, Zhou S, Joiner J, Guanter L, Verma M, Sun Y, Yang X, Paul-Limoges E, Gough CM, Wohlfahrt G, Gioli B, Van der Tol C, Nouvellon YP, Lund M, Grandcourt A de. On the relationship between sub-daily instantaneous and daily total gross primary production: Implications for interpreting satellite-based SIF retrievals [Internet]. Remote Sensing of Environment. 2018 ; 205 276-289.[citado 2026 abr. 17 ] Available from: https://doi.org/10.1016/j.rse.2017.12.009
Vancouver
Zhang Y, Xiao X, Zhang Y, Wolf S, Zhou S, Joiner J, Guanter L, Verma M, Sun Y, Yang X, Paul-Limoges E, Gough CM, Wohlfahrt G, Gioli B, Van der Tol C, Nouvellon YP, Lund M, Grandcourt A de. On the relationship between sub-daily instantaneous and daily total gross primary production: Implications for interpreting satellite-based SIF retrievals [Internet]. Remote Sensing of Environment. 2018 ; 205 276-289.[citado 2026 abr. 17 ] Available from: https://doi.org/10.1016/j.rse.2017.12.009
Artigo de periodico
Characterization of indicator tree species in neotropical environments and implications for geological mapping (2018)
Amaral, Cibele Hummel do ; Almeida, Teodoro Isnard Ribeiro de ; Souza Filho, Carlos Roberto de ; Roberts, Dar A. ; Fraser, Stephen ; Alves, Marcos Nopper ; Botelho, Moreno
Source: Remote Sensing of Environment. Unidade: IGC
Subjects: FITOGEOGRAFIA, BIOMA, CERRADO, ESPECTROSCOPIA, MAPEAMENTO GEOLÓGICO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
AMARAL, Cibele Hummel do et al. Characterization of indicator tree species in neotropical environments and implications for geological mapping. Remote Sensing of Environment, v. 216, n. , p. 385-400, 2018Tradução . . Disponível em: https://doi.org/10.1016/j.rse.2018.07.009. Acesso em: 17 abr. 2026.
APA
Amaral, C. H. do, Almeida, T. I. R. de, Souza Filho, C. R. de, Roberts, D. A., Fraser, S., Alves, M. N., & Botelho, M. (2018). Characterization of indicator tree species in neotropical environments and implications for geological mapping. Remote Sensing of Environment, 216( ), 385-400. doi:10.1016/j.rse.2018.07.009
NLM
Amaral CH do, Almeida TIR de, Souza Filho CR de, Roberts DA, Fraser S, Alves MN, Botelho M. Characterization of indicator tree species in neotropical environments and implications for geological mapping [Internet]. Remote Sensing of Environment. 2018 ; 216( ): 385-400.[citado 2026 abr. 17 ] Available from: https://doi.org/10.1016/j.rse.2018.07.009
Vancouver
Amaral CH do, Almeida TIR de, Souza Filho CR de, Roberts DA, Fraser S, Alves MN, Botelho M. Characterization of indicator tree species in neotropical environments and implications for geological mapping [Internet]. Remote Sensing of Environment. 2018 ; 216( ): 385-400.[citado 2026 abr. 17 ] Available from: https://doi.org/10.1016/j.rse.2018.07.009
Artigo de periodico
Geospatial Soil Sensing System (GEOS3): a powerful data mining procedure to retrieve soil spectral reflectance from satellite images (2018)
Demattê, José Alexandre Melo; Fongaro, Caio Troula; Rizzo, Rodnei; Safanelli, José Lucas
Source: Remote Sensing of Environment. Unidade: ESALQ
Subjects: SENSORIAMENTO REMOTO, MAPEAMENTO DO SOLO, AGRICULTURA DE PRECISÃO, SOLOS, IMAGEAMENTO DE SATÉLITE
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
DEMATTÊ, José Alexandre Melo et al. Geospatial Soil Sensing System (GEOS3): a powerful data mining procedure to retrieve soil spectral reflectance from satellite images. Remote Sensing of Environment, v. 212, p. 161-175, 2018Tradução . . Disponível em: https://doi.org/10.1016/j.rse.2018.04.047. Acesso em: 17 abr. 2026.
APA
Demattê, J. A. M., Fongaro, C. T., Rizzo, R., & Safanelli, J. L. (2018). Geospatial Soil Sensing System (GEOS3): a powerful data mining procedure to retrieve soil spectral reflectance from satellite images. Remote Sensing of Environment, 212, 161-175. doi:10.1016/j.rse.2018.04.047
NLM
Demattê JAM, Fongaro CT, Rizzo R, Safanelli JL. Geospatial Soil Sensing System (GEOS3): a powerful data mining procedure to retrieve soil spectral reflectance from satellite images [Internet]. Remote Sensing of Environment. 2018 ; 212 161-175.[citado 2026 abr. 17 ] Available from: https://doi.org/10.1016/j.rse.2018.04.047
Vancouver
Demattê JAM, Fongaro CT, Rizzo R, Safanelli JL. Geospatial Soil Sensing System (GEOS3): a powerful data mining procedure to retrieve soil spectral reflectance from satellite images [Internet]. Remote Sensing of Environment. 2018 ; 212 161-175.[citado 2026 abr. 17 ] Available from: https://doi.org/10.1016/j.rse.2018.04.047
Artigo de periodico
Generalized space-time classifiers for monitoring sugarcane areas in Brazil (2018)
Luciano, Ana Cláudia dos Santos; Picoli, Michelle Cristina Araújo; Rocha, Jansle Vieira; Franco, Henrique Coutinho Junqueira; Sanches, Guilherme Martineli; Leal, Manoel Regis Lima Verde; le Maire, Guerric
Source: Remote Sensing of Environment. Unidade: ESALQ
Subjects: AGRICULTURA DE PRECISÃO, APRENDIZADO COMPUTACIONAL, CANA-DE-AÇÚCAR, IMAGEAMENTO DE SATÉLITE, MAPEAMENTO DO SOLO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
LUCIANO, Ana Cláudia dos Santos et al. Generalized space-time classifiers for monitoring sugarcane areas in Brazil. Remote Sensing of Environment, v. 215, p. 438-451, 2018Tradução . . Disponível em: https://doi.org/10.1016/j.rse.2018.06.017. Acesso em: 17 abr. 2026.
APA
Luciano, A. C. dos S., Picoli, M. C. A., Rocha, J. V., Franco, H. C. J., Sanches, G. M., Leal, M. R. L. V., & le Maire, G. (2018). Generalized space-time classifiers for monitoring sugarcane areas in Brazil. Remote Sensing of Environment, 215, 438-451. doi:10.1016/j.rse.2018.06.017
NLM
Luciano AC dos S, Picoli MCA, Rocha JV, Franco HCJ, Sanches GM, Leal MRLV, le Maire G. Generalized space-time classifiers for monitoring sugarcane areas in Brazil [Internet]. Remote Sensing of Environment. 2018 ; 215 438-451.[citado 2026 abr. 17 ] Available from: https://doi.org/10.1016/j.rse.2018.06.017
Vancouver
Luciano AC dos S, Picoli MCA, Rocha JV, Franco HCJ, Sanches GM, Leal MRLV, le Maire G. Generalized space-time classifiers for monitoring sugarcane areas in Brazil [Internet]. Remote Sensing of Environment. 2018 ; 215 438-451.[citado 2026 abr. 17 ] Available from: https://doi.org/10.1016/j.rse.2018.06.017
Artigo de periodico
The Evaporative Stress Index as an indicator of agricultural drought in Brazil: an assessment based on crop yield impacts (2016)
Anderson, Martha C; Zolin, Cornélio Alberto; Sentelhas, Paulo César; Hain, Christopher R; Semmens, Kathryn; Tugrul Yilmaz, M; Gao, Feng; Otkin, Jason A; Tetrault, Robert
Source: Remote Sensing of Environment. Unidade: ESALQ
Subjects: EVAPOTRANSPIRAÇÃO, BALANÇO HÍDRICO, SECA, PRODUÇÃO AGRÍCOLA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ANDERSON, Martha C et al. The Evaporative Stress Index as an indicator of agricultural drought in Brazil: an assessment based on crop yield impacts. Remote Sensing of Environment, v. 174, p. 82–99, 2016Tradução . . Disponível em: https://doi.org/10.1016/j.rse.2015.11.034. Acesso em: 17 abr. 2026.
APA
Anderson, M. C., Zolin, C. A., Sentelhas, P. C., Hain, C. R., Semmens, K., Tugrul Yilmaz, M., et al. (2016). The Evaporative Stress Index as an indicator of agricultural drought in Brazil: an assessment based on crop yield impacts. Remote Sensing of Environment, 174, 82–99. doi:10.1016/j.rse.2015.11.034
NLM
Anderson MC, Zolin CA, Sentelhas PC, Hain CR, Semmens K, Tugrul Yilmaz M, Gao F, Otkin JA, Tetrault R. The Evaporative Stress Index as an indicator of agricultural drought in Brazil: an assessment based on crop yield impacts [Internet]. Remote Sensing of Environment. 2016 ; 174 82–99.[citado 2026 abr. 17 ] Available from: https://doi.org/10.1016/j.rse.2015.11.034
Vancouver
Anderson MC, Zolin CA, Sentelhas PC, Hain CR, Semmens K, Tugrul Yilmaz M, Gao F, Otkin JA, Tetrault R. The Evaporative Stress Index as an indicator of agricultural drought in Brazil: an assessment based on crop yield impacts [Internet]. Remote Sensing of Environment. 2016 ; 174 82–99.[citado 2026 abr. 17 ] Available from: https://doi.org/10.1016/j.rse.2015.11.034
Artigo de periodico
Estimating determinism rates to detect patterns in geospatial datasets (2015)
Rios, Ricardo Araújo; Parrott, Lael; Lange, Holger; Mello, Rodrigo Fernandes de
Source: Remote Sensing of Environment. Unidade: ICMC
Subjects: SISTEMAS DISTRIBUÍDOS, PROGRAMAÇÃO CONCORRENTE, ANÁLISE DE SÉRIES TEMPORAIS, VEGETAÇÃO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
RIOS, Ricardo Araújo et al. Estimating determinism rates to detect patterns in geospatial datasets. Remote Sensing of Environment, v. 156, n. ja 2015, p. 11-20, 2015Tradução . . Disponível em: https://doi.org/10.1016/j.rse.2014.09.019. Acesso em: 17 abr. 2026.
APA
Rios, R. A., Parrott, L., Lange, H., & Mello, R. F. de. (2015). Estimating determinism rates to detect patterns in geospatial datasets. Remote Sensing of Environment, 156( ja 2015), 11-20. doi:10.1016/j.rse.2014.09.019
NLM
Rios RA, Parrott L, Lange H, Mello RF de. Estimating determinism rates to detect patterns in geospatial datasets [Internet]. Remote Sensing of Environment. 2015 ; 156( ja 2015): 11-20.[citado 2026 abr. 17 ] Available from: https://doi.org/10.1016/j.rse.2014.09.019
Vancouver
Rios RA, Parrott L, Lange H, Mello RF de. Estimating determinism rates to detect patterns in geospatial datasets [Internet]. Remote Sensing of Environment. 2015 ; 156( ja 2015): 11-20.[citado 2026 abr. 17 ] Available from: https://doi.org/10.1016/j.rse.2014.09.019
Artigo de periodico
Satellite-based hydrological dynamics of the world's largest continuous wetland (2015)
Penatti, Natasha Costa ; Almeida, Teodoro Isnard Ribeiro de ; Ferreira, Laerte Guimarães ; Arantes, Arielle Elias; Coe, Michael T.
Source: Remote Sensing of Environment. Unidade: IGC
Subjects: SENSORIAMENTO REMOTO, HIDROGEOLOGIA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
PENATTI, Natasha Costa et al. Satellite-based hydrological dynamics of the world's largest continuous wetland. Remote Sensing of Environment, v. 170, p. 1-13, 2015Tradução . . Disponível em: https://doi.org/10.1016/j.rse.2015.08.031. Acesso em: 17 abr. 2026.
APA
Penatti, N. C., Almeida, T. I. R. de, Ferreira, L. G., Arantes, A. E., & Coe, M. T. (2015). Satellite-based hydrological dynamics of the world's largest continuous wetland. Remote Sensing of Environment, 170, 1-13. doi:10.1016/j.rse.2015.08.031
NLM
Penatti NC, Almeida TIR de, Ferreira LG, Arantes AE, Coe MT. Satellite-based hydrological dynamics of the world's largest continuous wetland [Internet]. Remote Sensing of Environment. 2015 ; 170 1-13.[citado 2026 abr. 17 ] Available from: https://doi.org/10.1016/j.rse.2015.08.031
Vancouver
Penatti NC, Almeida TIR de, Ferreira LG, Arantes AE, Coe MT. Satellite-based hydrological dynamics of the world's largest continuous wetland [Internet]. Remote Sensing of Environment. 2015 ; 170 1-13.[citado 2026 abr. 17 ] Available from: https://doi.org/10.1016/j.rse.2015.08.031
Artigo de periodico
Mapping short-rotation plantations at regional scale using MODIS time series: Case of eucalypt plantations in Brazil (2014)
le Maire, Guerric; Dupuy, Stéphane; Nouvellon, Yann Pierre ; Loos, Rodolfo Araujo; Hakamada, Rodrigo
Source: Remote Sensing of Environment. Unidade: ESALQ
Subjects: ANÁLISE DE SÉRIES TEMPORAIS, EUCALIPTO, MAPEAMENTO DO SOLO, ROTAÇÃO DE CULTURAS, SENSORIAMENTO REMOTO, SILVICULTURA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
LE MAIRE, Guerric et al. Mapping short-rotation plantations at regional scale using MODIS time series: Case of eucalypt plantations in Brazil. Remote Sensing of Environment, v. 152, p. 136-149, 2014Tradução . . Disponível em: https://doi.org/10.1016/j.rse.2014.05.015. Acesso em: 17 abr. 2026.
APA
le Maire, G., Dupuy, S., Nouvellon, Y. P., Loos, R. A., & Hakamada, R. (2014). Mapping short-rotation plantations at regional scale using MODIS time series: Case of eucalypt plantations in Brazil. Remote Sensing of Environment, 152, 136-149. doi:10.1016/j.rse.2014.05.015
NLM
le Maire G, Dupuy S, Nouvellon YP, Loos RA, Hakamada R. Mapping short-rotation plantations at regional scale using MODIS time series: Case of eucalypt plantations in Brazil [Internet]. Remote Sensing of Environment. 2014 ; 152 136-149.[citado 2026 abr. 17 ] Available from: https://doi.org/10.1016/j.rse.2014.05.015
Vancouver
le Maire G, Dupuy S, Nouvellon YP, Loos RA, Hakamada R. Mapping short-rotation plantations at regional scale using MODIS time series: Case of eucalypt plantations in Brazil [Internet]. Remote Sensing of Environment. 2014 ; 152 136-149.[citado 2026 abr. 17 ] Available from: https://doi.org/10.1016/j.rse.2014.05.015

USP Schools

ReP

Filtros

USP Schools

Departament

Authors

USP affiliated authors

Date published

Subjects

Funding Agencies

Indexado em

Non normalized affiliation

Countries of institutions of collaboration