Djamai Najib
Développement et validation de méthodes d'estimation des variables biophysiques de la végétation à partir de données satellites multispectrales à résolution moyenne.
Prix et études
- Doctorat en Télédétection (2015), Université de Sherbrooke, Sherbrooke (QC), Canada
- Mastère en sciences géomatiques (2010), Université Laval, Québec-City (QC), Canada
- Diplôme national d’ingénieur en hydrométéorologie (2008), École National d’Ingénieurs de Tunis, Tunisie
- Diplôme d'études supérieures en apprentissage automatique et intelligence artificielle (2020), EMERITUS Institute of Management - Columbia Engineering Executive Education
- Certificat d'apprentissage automatique appliqué (2019), EMERITUS Institute of Management - University of California Berkeley Extension
Principales publications
Fernandes, R., Djamai, N., Harvey, K., Hong, G., MacDougall, C., Shah, H., Sun, L. (2024). Evidence of a bias-variance trade off when correcting for bias in Sentinel 2 forest LAI retrievals using radiative transfer models. Remote Sensing of Environment, 305, 114060.
Brown, L., Fernandes, R., Djamai, N., Meier, C., Gobron, N., Morris, H., Canisius, F., Bai, G., Lerebourg, C., Lanconelli, C., Clerici, M., Dash, J., (2021). Validation of baseline and modified Sentinel-2 Level 2 Prototype Processor leaf area index retrievals over the United States. ISPRS Journal of Photogrammetry and Remote Sensing, 175, 71 -87.
Djamai, N., Fernandes, R., (2021). Active learning regularization increases clear sky retrieval rates for biophysical variables using Sentinel-2 data. Remote Sensing of Environment, 254, 112241.
Djamai, N., Zhong. D., Fernandes, R., Zhou, F. (2019). Evaluation of vegetation biophysical variables time-series derived from synthetic Sentinel-2 images. Remote Sensing, 11(13), 1547.
Djamai, N., Fernandes, R., Weiss, M., McNairn, H., Goita, K., (2019). Validation of the Sentinel Simplified Level 2 Product Prototype Processor (SL2P) for mapping cropland biophysical variables using Sentinel-2/MSI and Landsat-8/OLI data. Remote Sensing of Environment, 225, 416-430.
Djamai, N., Fernandes, R. (2018). A comparison of SNAP-derived Sentinel-2A L2A product to ESA provided product over Europe. Remote Sensing, 10(6), 926.
Hamdi, M., Zagrarni, M.F., Djamai, N., Jerbi, H., Goita, K., Tarhouni, J. (2018). Journal of African Earth Sciences 143, 178-186.
Djamai, N., Magagi, R., Goita, K., Merlin, O., Kerr, H.Y., Roy, A. (2016). A combination of DISPATCH downscaling algorithm with CLASS land surface scheme for soil moisture estimation at fine scale during cloudy days. Remote Sensing of Environment, 184, 1–14.
Djamai, N., Magagi, R., Goita, K., Merlin, O., Kerr, H.Y., Walker, A. (2015). Disaggregation of SMOS Soil Moisture over the Canadian Prairies. Remote Sensing of Environment, 170, 255–268.
Djamai, N., Magagi, R., Goita, K., Housseini, M., Cosh, M.H, Berg, A., Toth, B. (2015). Evaluation of SMOS soil moisture products over the CanEx-SM10 area. Journal of Hydrology, 520, 254-267.
Magagi, R., Berg, A., Goïta, K., Belair, S., Jackson, T., Toth, B., Walker, A., McNairn, H., Peggy, O., Moghaddam, M., Gherboudj, I., Colliander, A., Cosh, M., Burgin, M., Fisher, J. B., Kim, S.B., Mladenova, I., Djamai N., Rousseau, L.P., Belanger, J., Shang, J., and Merzouki, A. (2013). CanEX-SM 10 (Canadian Experiment for Soil Moisture in 2010): Overview and Preliminary results. IEEE Transactions on Geoscience and Remote Sensing, 51, 347-363.
Djamai N., Gond V., Cocard, M. (2012). Exploitation des images satellitaires MODIS-Terra pour la caractérisation des états de surface en Tunisie. Sécheresse, 23, 113 – 120.
ORCID