Ramata Magagi

Thématiques de recherche

Mon domaine principal de recherche est la télédétection par micro-onde des paramètres de la surface terrestre (sol, végétation et neige). Les milieux étudiés sont très variés, passant des milieux semi-arides aux zones de forêts boréales et aux régions enneigées. Je me consacre également à l’extraction de paramètres physiques et à la caractérisation de ces milieux.

 

Ramata.Magagi@USherbrooke.ca

 
Validation de données micro-ondes passives de la mission spatiale SMOS

Estimation de l’humidité du sol par micro-ondes passives aéroportées et satellitaires
Modélisation des paramètres physiques terrestres

 

  • 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.