Ramata Magagi

Research topics

My main field of research is the microwave remote sensing of land surface parameters (soil, vegetation and snow). The environments studied are very diverse, ranging from semi-arid environments to boreal forest areas and snow-covered regions. I also work on the extraction of physical parameters and the characterization of these environments.  
 

Email

Ramata.Magagi@USherbrooke.ca  
 

Current projects

Validation of passive microwave data from the SMOS space mission
Estimation of soil moisture by airborne and satellite passive microwaves
Modelling of terrestrial physical parameters  
 

Publications

  • Wang, H., Magagi, R., Goïta, K., Colliander, A., Jackson, T., McNairn, H., & Powers, J. (2021). Soil moisture retrieval over a site of intensive agricultural production using airborne radiometer data. International Journal of Applied Earth Observation and Geoinformation, 97, 102287.
  • Bahrami, A., Goïta, K., Magagi, R., Davison, B., Razavi, S., Elshamy, M., & Princz, D. (2021). Data assimilation of satellite-based terrestrial water storage changes into a hydrology land-surface model. Journal of Hydrology, 597, 125744.
  • Wang, H., Magagi, R., Goïta, K., & Wang, K. (2020). Soil moisture retrievals using ALOS2-ScanSAR and MODIS synergy over Tibetan Plateau. Remote Sensing of Environment, 251, 112100.
  • Ziyad, J., Goïta, K., & Magagi, R. (2020). Incertitudes des niveaux d’eau dérivés de l’altimétrie satellitaire pour des étendues d’eau soumises à l’action de la glace. Canadian Journal of Remote Sensing, 46(4), 429-453.
  • Bahrami, A., Goïta, K., & Magagi, R. (2020). Analysing the contribution of snow water equivalent to the terrestrial water storage over Canada. Hydrological Processes, 34(2), 175-188.
  • Tong, C., Wang, H., Magagi, R., Goïta, K., Zhu, L., Yang, M., & Deng, J. (2020). Soil Moisture Retrievals by Combining Passive Microwave and Optical Data. Remote Sensing, 12(19), 3173.
  • Ziyad, J., Goïta, K., Magagi, R., Blarel, F., & Frappart, F. (2020). Improving the Estimation of Water Level over Freshwater Ice Cover using Altimetry Satellite Active and Passive Observations. Remote Sensing, 12(6), 967.
  • Monsiváis-Huertero, A., Hernández-Sánchez, J. C., Jiménez-Escalona, J. C., Galeana-Pizaña, J. M., Constantino-Recillas, D. E., Torres-Gómez, A. C., … & Couturier, S. (2020). Impact of temporal variations in vegetation optical depth and vegetation temperature on L-band passive soil moisture retrievals over a tropical forest using in-situ information. International Journal of Remote Sensing, 41(6), 2098-2139.
  • Wang, H., Magagi, R., Goïta, K.,Trudel, M., McNairn, H., Powers, J. (2019) Crop phenology retrieval via polarimetric SAR decomposition and random forest algorithm. Remote Sensing of Environment,https://doi.org/10.1016/j.rse.2019.111234.
  • Wang, H., Magagi, R., Goïta, K.,Jagdhuber, T. (2019) Refining a polarimetric decomposition of multi-angular UAVSAR time series of soil moisture retrieval over low and high vegetated agricultural fields. IEEE J. Selected Topics in Applied Earth Observations and Remote Sensing,12(5) : 1431-1450.
  • Colliander, A., Cosh, M. H., Misra, S., Jackson, T. J., Crow, W. T., Powers, J., … & Gao, Y. (2019). Comparison of high-resolution airborne soil moisture retrievals to SMAP soil moisture during the SMAP validation experiment 2016 (SMAPVEX16). Remote Sensing of Environment, 227, 137-150.
  • Abbes, A. B., Magagi, R., & Goita, K. (2019, July). Soil Moisture Estimation From Smap Observations Using Long Short-Term Memory (LSTM). In IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium (pp. 1590-1593). IEEE.
  • Wang, H., Magagi, R., Goïta, K. (2018) Potential of a two-component polarimetric decomposition at C-band for soil moisture retrieval over agricultural fields. Remote Sens. Environment, 217:38-51.
  • Bhuiyan, H. A., McNairn, H., Powers, J., Friesen, M., Pacheco, A., Jackson, T. J., … & Bullock, P. (2018). Assessing SMAP soil moisture scaling and retrieval in the Carman (Canada) study site. Vadose Zone Journal, 17(1), 1-14.
  • Constantino-Recillas, D. E., Monsiváis-Huertero, A., Jiménez-Escalona, J. C., Zempoaltecatl-Ramirez, E., Magagi, R., & Goïta, K. (2018, July). A Semi-Empirical Model to Estimate Biophysical Parameters in Southern Mexico. In IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium (pp. 5344-5347). IEEE.
  • Wang, H., Magagi, R., Goïta, K. (2017) Comparison of different polarimetric decompositions for soil moisture retrieval over vegetation covered agricultural area. Remote Sens.Environment, 199:120-136.
  • Gherboudj, I., Magagi, R., Berg, A. A., & Toth, B. (2017). Characterization of the spatial variability of in-situ soil moisture measurements for upscaling at the spatial resolution of RADARSAT-2. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(5), 1813-1823.
  • Magagi, R., Kerr, Y., & Wigneron, J. P. (2016). Estimation of Soil Water Conditions Using Passive Microwave Remote Sensing. In Land Surface Remote Sensing in Continental Hydrology (pp. 41-78). Elsevier.
  • Djamai, N., Magagi, R., Goïta, K., Merlin, O., Kerr, Y., Roy, A. (2016) A combination of DISPATCH downscaling algorithm with CLASS land surface scheme for soil moisture estimation at fine scale duringcloudy days. Remote Sens.Environment, 184:1-14.
  • Wang, H., Magagi, R., Goïta, K., Jagdhuber, T., Hajnsek, I. (2016b) Evaluation of Simplified Polarimetric Decomposition for Soil Moisture Retrieval over Vegetated Agricultural Fields. Remote Sensing, 2016, 8, 142; doi:10.3390/rs8020142.
  • Sun, L., Seidou, O., Nistor, I., Goïta, K., Magagi, R. (2016) Simultaneous assimilation of in situ soil moisture and streamflow in the SWAT model using the Extended Kalman Filter. Journal of Hydrology, 543:671-685.
  • Wang, H., Magagi, R., Goïta, K. (2016a) Polarimetric Decomposition for Monitoring Crop Growth Status. IEEE Geoscience and Remote Sensing Letters, 13:870-874.