Cite
Long Term Global Surface Soil Moisture Fields Using an SMOS-Trained Neural Network Applied to AMSR-E Data.
MLA
Rodríguez-Fernández, Nemesio J., et al. “Long Term Global Surface Soil Moisture Fields Using an SMOS-Trained Neural Network Applied to AMSR-E Data.” Remote Sensing, vol. 8, no. 11, Nov. 2016, p. 959. EBSCOhost, https://doi.org/10.3390/rs8110959.
APA
Rodríguez-Fernández, N. J., Kerr, Y. H., Van Der Schalie, R., Al-Yaari, A., Wigneron, J.-P., De Jeu, R., Richaume, P., Dutra, E., Mialon, A., & Drusch, M. (2016). Long Term Global Surface Soil Moisture Fields Using an SMOS-Trained Neural Network Applied to AMSR-E Data. Remote Sensing, 8(11), 959. https://doi.org/10.3390/rs8110959
Chicago
Rodríguez-Fernández, Nemesio J., Yann H. Kerr, Robin Van Der Schalie, Amen Al-Yaari, Jean-Pierre Wigneron, Richard De Jeu, Philippe Richaume, Emanuel Dutra, Arnaud Mialon, and Matthias Drusch. 2016. “Long Term Global Surface Soil Moisture Fields Using an SMOS-Trained Neural Network Applied to AMSR-E Data.” Remote Sensing 8 (11): 959. doi:10.3390/rs8110959.