Publications | MSU GCER lab

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Peer-reviewed publications

Every researcher knows the amount of work behind each research paper, and we would like to highlight each one with preview figures so we can remember they are unique in the data sources and analysis. Google Scholar

2025

35. Lima, T. M., Martins, V. S., Paulino, R. S., Caballero, C. B., Maciel, D. A., & Giardino, C. (2025). A General Spectral Bandpass Adjustment Function (SBAF) for Harmonizing Landsat-Sentinel over Inland and Coastal Waters. Science of Remote Sensing, 100225.

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34. Lima, T. M. A., Barbosa, C. C. F., Nordi, C. S. F., Begliomini, F. N., Martins, V. S., Watanabe, F. S. Y., Wanderley, R. L. N., & Paulino, R. S. (2025). A novel hybrid cyanobacteria mapping approach for inland reservoirs using Sentinel-3 imagery. Harmful Algae.

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33. Caballero, C. B., Martins, V. S., Paulino, R. S., Butler, E., Sparks, E., Lima, T. M., & Novo, E. M. L. M. (2025). The need for advancing algal bloom forecasting using remote sensing and modeling: Progress and future directions. Ecological Indicators, 172, 113244.

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32. Ferreira, L. B., Martins, V. S., Aires, U. R., Wijewardane, N., Zhang, X., & Samiappan, S. (2025). FieldSeg: A scalable agricultural field extraction framework based on the Segment Anything Model and 10-m Sentinel-2 imagery. Computers and Electronics in Agriculture, 232, 110086.

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31. Paulino, R. S., Martins, V. S., Novo, E. M., Barbosa, C. C., Maciel, D. A., Wanderley, R. L. D. N., ... & Lima, T. M. (2025). Generation of robust 10-m Sentinel-2/3 synthetic aquatic reflectance bands over inland waters. Remote Sensing of Environment, 318, 114593.

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2024

30. Schumacher, V., Setzer, A., Martins, V., & Victorino, P. (2024). Evaluation and comparison of MODIS and MISR aerosol products with ground-based monitoring stations in the Amazon Basin. Atmospheric Environment, 331, 120597..

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29. Portela, C. F., Martins, V. S., Novo, E. M., Paulino, R. S., & Barbosa, C. C. (2024). Recent advances in geostationary satellites for inland and coastal aquatic systems: scientific research and applications. International Journal of Remote Sensing, 45(5), 1574-1607.

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28. Gamagedara, Y., Wijewardane, N. K., Feng, G., Seybold, C., Williams, M., Tagert, M. L., & Martins, V. S. (2024). Can we use a mid-infrared fine-ground soil spectral library to predict non-fine-ground spectra?. Geoderma, 443, 116799.

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2023

27. Maciel, D.A., Pahlevan,N., Barbosa, C.C.F., Martins, V.S., Smith, B., O'Shea, R.E., Balasubramanian, S.V., Saranathan, Arun M., Novo,E.M.L.M.. (2023), Towards global long-term water transparency products from the Landsat archive. Remote Sensing of Environment, V. 299., 113889.

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26. Begliomini, F. N., Barbosa, C.C.F., Martins, V.S., Novo, E.M.L.M., Paulino, R.S., Maciel, D.A., Lima, T.M.A., O'Shea, R.E., Pahlevan, N., Lamparelli, M.C. (2023). Machine learning for cyanobacteria mapping on tropical urban reservoirs using PRISMA hyperspectral data. ISPRS Journal of Photogrammetry and Remote Sensing. Volume 204, 378-396.

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25. Paulino, R.S., Martins, V.S., Novo, E.M.L.M., Maciel,D.A., Correia-Lima,D.L., Barbosa, C.C.F., Bonnet, M.P., Uhde, A. (2023), A framework based on spectral similarity to estimate hydrological connectivity in Juruá River floodplain lakes using 3-m PlanetScope data. Journal of Hydrology. 130156.

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24. Maciel, D.A., Pahlevan, N., Barbosa, C.C.F., Novo, E.M.L.M., Paulino, R.S., Martins, V.S., Vermote, E. and Crawford, C.J. (2023), Validity of the Landsat surface reflectance archive for aquatic science: Implications for cloud-based analysis. Limnology and Oceanography Letters.

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23. Caballero, C. B., Guedes, H. A. S., Fraga, R. D. S., Mendes, K. G. P., Fonseca, E. H. D., Martins, V. S., & Mensch, M. D. S. (2023). Predictive model for monitoring water turbidity in a subtropical lagoon using Sentinel-2A/B MSI images. RBRH, 28, e5..

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2022

22. Caballero, C. B., Guedes, H. A. S., ANDRADE, A. C. F., Martins, V. S., Fraga, R. S., & Mendes, K. G. (2022). Empirical and semi-empirical chlorophyll-a modeling for water quality assessment through river-lake transition in extreme Southern Brazil. Anais da Academia Brasileira de Ciências, 94.

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21. Martins, V. S., Roy, D. P., Huang, H., Boschetti, L., Zhang, H. K., & Yan, L. (2022). Deep learning high resolution burned area mapping by transfer learning from Landsat-8 to PlanetScope. Remote Sensing of Environment, 280, 113203

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20. Paulino, R. S., Martins, V. S., Novo, E. M., Barbosa, C. C., de Carvalho, L. A., & Begliomini, F. N. (2022). Assessment of Adjacency Correction over Inland Waters Using Sentinel-2 MSI Images. Remote Sensing, 14(8), 1829.

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19. Zhai, Y., Roy, D. P., Martins, V. S., Zhang, H. K., Yan, L., & Li, Z. (2022). Conterminous United States Landsat-8 top of atmosphere and surface reflectance tasseled cap transformation coefficients. Remote Sensing of Environment, 274, 112992.

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18. Nagel, G. W., de Moraes Novo, E. M. L., Martins, V. S., Campos-Silva, J. V., Barbosa, C. C. F., & Bonnet, M. P. (2022). Impacts of meander migration on the Amazon riverine communities using Landsat time series and cloud computing. Science of The Total Environment, 806, 150449.

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2021

17. Lobo, F. D. L., Nagel, G. W., Maciel, D. A., Carvalho, L. A. S. D., Martins, V. S., Barbosa, C. C. F., & Novo, E. M. L. D. M. (2021). AlgaeMAp: algae bloom monitoring application for inland waters in latin america. Remote Sensing, 13(15), 2874.

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16. Roy, D. P., Huang, H., Houborg, R., & Martins, V. S. (2021). A global analysis of the temporal availability of PlanetScope high spatial resolution multi-spectral imagery. Remote Sensing of Environment, 264, 112586..

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15. Martins, V. S., Kaleita, A. L., & Gelder, B. K. (2021). Digital mapping of structural conservation practices in the Midwest US croplands: Implementation and preliminary analysis. Science of The Total Environment, 772, 145191.

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13. Damascena, A. S., Yamasoe, M. A., Martins, V. S., Rosas, J., Benavente, N. R., Sánchez, M. P., ... & Saldiva, P. H. N. (2021). Exploring the relationship between high-resolution aerosol optical depth values and ground-level particulate matter concentrations in the Metropolitan Area of São Paulo. Atmospheric Environment, 244, 117949.

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2020

14. Martins, V. S., Kaleita, A. L., Gelder, B. K., da Silveira, H. L., & Abe, C. A. (2020). Exploring multiscale object-based convolutional neural network (multi-OCNN) for remote sensing image classification at high spatial resolution. ISPRS Journal of Photogrammetry and Remote Sensing, 168, 56-73.

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12. Martins, V. S., Kaleita, A. L., Gelder, B. K., Nagel, G. W., & Maciel, D. A. (2020). Deep neural network for complex open-water wetland mapping using high-resolution WorldView-3 and airborne LiDAR data. International Journal of Applied Earth Observation and Geoinformation, 93, 102215.

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11. Maciel, D. A., Barbosa, C. C. F., de Moraes Novo, E. M. L., Cherukuru, N., Martins, V. S., Júnior, R. F., ... & Carlos, F. M. (2020). Mapping of diffuse attenuation coefficient in optically complex waters of amazon floodplain lakes. ISPRS Journal of Photogrammetry and Remote Sensing, 170, 72-87.

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10. Prudente, V. H. R., Martins, V. S., Vieira, D. C., e Silva, N. R. D. F., Adami, M., & Sanches, I. D. A. (2020). Limitations of cloud cover for optical remote sensing of agricultural areas across South America. Remote Sensing Applications: Society and Environment, 20, 100414.

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9. Fraga, R. S., Guedes, H. A., Martins, V. S., Caballero, C. B., Mendes, K. G., Monks, J. L., & Fassoni-Andrade, A. C. (2020). Empirical modelling of suspended solids in a subtropical lagoon (Brazil) using linear spectral mixing algorithm. Remote Sensing Applications: Society and Environment, 20, 100380.

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8. Maciel, D. A., Novo, E. M. L. D. M., Barbosa, C. C. F., Martins, V. S., Flores Júnior, R., Oliveira, A. H., ... & Lobo, F. D. L. (2020). Evaluating the potential of CubeSats for remote sensing reflectance retrieval over inland waters. International Journal of Remote Sensing, 41(7), 2807-2817.

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2019

7. Martins, V. S., Lyapustin, A., Wang, Y., Giles, D. M., Smirnov, A., Slutsker, I., & Korkin, S. (2019). Global validation of columnar water vapor derived from EOS MODIS-MAIAC algorithm against the ground-based AERONET observations. Atmospheric Research, 225, 181-192.

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6. Martins, V. S., Martins, V. S., Kaleita, A., Barbosa, C. C., Fassoni-Andrade, A. C., de Lucia Lobo, F., & Novo, E. M. (2019). Remote sensing of large reservoir in the drought years: Implications on surface water change and turbidity variability of Sobradinho reservoir (Northeast Brazil). Remote Sensing Applications: Society and Environment, 13, 275-288.

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5. Barbosa, C.C.F.; Novo, E.M.L.M.; Martins, V.S. (2019), Introduction to Remote Sensing for Aquatic Systems: Principles and Applications. National Institute for Space Research. (Book in Portuguese)

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2018

4. Martins, V. S., Soares, J. V., Novo, E. M., Barbosa, C. C., Pinto, C. T., Arcanjo, J. S., & Kaleita, A. (2018). Continental-scale surface reflectance product from CBERS-4 MUX data: Assessment of atmospheric correction method using coincident Landsat observations. Remote Sensing of Environment, 218, 55-68.

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3. Martins, V. S., Novo, E. M., Lyapustin, A., Aragão, L. E., Freitas, S. R., & Barbosa, C. C. (2018). Seasonal and interannual assessment of cloud cover and atmospheric constituents across the Amazon (2000–2015): Insights for remote sensing and climate analysis. ISPRS Journal of Photogrammetry and Remote Sensing, 145, 309-327.

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2017

2. Martins, V. S., Lyapustin, A., de Carvalho, L. A., Barbosa, C. C. F., & Novo, E. M. L. D. M. (2017). Validation of high‐resolution MAIAC aerosol product over South America. Journal of Geophysical Research: Atmospheres, 122(14), 7537-7559.

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1. Martins, V. S., Barbosa, C. C. F., De Carvalho, L. A. S., Jorge, D. S. F., Lobo, F. D. L., & Novo, E. M. L. D. M. (2017). Assessment of atmospheric correction methods for Sentinel-2 MSI images applied to Amazon floodplain lakes. Remote Sensing, 9(4), 322.

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