Djamai Najib

Image Najib Djamai
Remote Sensing Physical Scientist

Development and validation of methods for estimating vegetation biophysical variables from medium resolution multispectral satellite data.

Education and awards

  • Ph.D. in Remote Sensing, Université de Sherbrooke, Québec (2015)
  • M.Sc. in geomatics , Université Laval, Québec (2015)

  • B.Eng. in Hydrometeorology, National Engineering School of Tunis (Tunisia)


  • Postgraduate Diploma in Machine Learning and Artificial Intelligence (2020), EMERITUS Institute of Management - Columbia Engineering Executive Education
  • Applied Machine Learning Certificate (2019, EMERITUS Institute of Management - University of California Berkeley Extension 

Key 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. 




Research facility

580 Booth Street
Ottawa, ON K1A 0E8