Dr. Heather McNairn

Image Heather McNairn
Chercheuse

Mes recherches portent sur l’élaboration de méthodes, de modèles et d’algorithmes pour surveiller l’état de nos sols et de nos cultures à l’aide de données provenant de satellites équipés de capteurs multispectraux, de capteurs hyperspectraux et d’un radar à synthèse d’ouverture (RSO).

Recherche et / ou projets en cours

Je dirige actuellement deux projets de recherche :

« Comparaison internationale des méthodes basées sur l’imagerie de radar à synthèse d’ouverture (RSO) pour la surveillance des types de culture et de l’état des cultures : mise au point d’une capacité de surveillance opérationnelle pour le Canada et au-delà » 

« Quantifier la productivité des cultures depuis l’espace : Un nouveau système radar par satellite pour mesurer la réponse des cultures »

 

Principales publications

Pour une liste complète de publications, visitez : AAC en ligne

Last 3 years, only:

Colliander, A., Cosh, M.H., Misra, S., Jackson, T.J., Crow, W., Powers, J., McNairn, H., Bullock, P., Berg, A., Magagi, R., Gao, Y., Bindlish, R., O’Neill, P.O., and Yueh, S. (2019). Comparison of high-resolution airborne soil moisture retrievals to SMAP soil moisture over agricultural landscapes during SMAP Validation Experiment 2016 (SMAPVEX2016), Remote Sensing of Environment, 227: 137-150.

Djamai, N., Fernandes, R., Weiss, M., McNairn, H., and Goïta, 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.

Reisi-Gahrouei, O., Homayouni, S., McNairn, H., Hosseini, M., and Safari, A. (2019). Crop biomass estimation using multi regression analysis and neural networks for multitemporal L-band polarimetric synthetic aperture radar data, International Journal of Remote Sensing, published on-line, doi.org/10.1080/01431161.2019.1594436.

Dabboor, M., Sun L. Carrera, M.L., Friesen, M., Merzouki, A., McNairn, H., Powers, J., and Belair, S. (2019). Comparative analysis of high-resolution soil moisture simulations from the Soil, Vegetation, and Snow (SVS) land surface model using SAR imagery, Water, 11: 1-22. doi:10.3390/w11030542.

Cosh, M.H., White, W.A., Colliander, A., Jackson, T.J., Prueger, J.H., Hornbuckle, B.K., Hunt, E.R., McNairn, H., Powers, J., Walker, V.A., and Bullock, P. (2019). Estimating vegetation water content during the Soil Moisture Active Passive Validation Experiment 2016, Journal of Applied Remote Sensing, 13: doi:10.1117/1.JRS.13.014516.

Homayouni, S., McNairn, H., Hosseini, M., Jiao, X, and Powers, J. (2019). Quad and compact multitemporal C-band PolSAR observations for crop characterization and monitoring, International Journal of Applied Earth Observations and Geoinformation, 74: 78-87.

McNairn, H., Jiao, X., Pacheco, A., Sinha, A., Tan, W., and Li, Y. (2018). Estimating canola phenology using synthetic aperture radar, Remote Sensing of Environment, 219: 196-205.

Zwieback, S., Colliander, A., Cosh, M.H., Martínez-Fernández, J., McNairn, H., Starks, P.J., Thibeault, M., and Berg, A. (2018). Estimating time-dependent vegetation biases in the SMAP soil moisture product, Hydrology and Earth System Sciences, 22: 4473-4489.

Bhuiyan, H.A.K.M., McNairn, H., Powers, J., Friesen, M., Pacheco, A., Jackson, T.J., Cosh, M.H., Colliander, A., Berg, A., Rowlandson, T., Bullock, P., and Magagi, R. (2018). Assessing SMAP soil moisture scaling and retrieval in the Carman (Canada) study site, Vadose Zone Journal, doi:10.2136/vzj2018.07.0132.

Niazmardi, S., Beckett, K., Safari, A., McNairn, H., Shang, J., and Homayouni, S.  (2018). Histogram-based Spatio-Temporal Feature Classification of Vegetation Indices Time-Series for Crop Mapping, International Journal of Applied Earth Observations and Geoinformation, 72: 34-41.

Kolassa, J., Reichle, R.H., Liu, Q., Alemohammad, S.H., Gentine, P., Aida, K., Asanuma, J., Bircher, S., Caldwell, T., Colliander, A., Cosh, M., Holifield Collins, C., Jackson, T., Jensen, K.H., Martinez-Fernandez, J., McNairn, H., Pacheco, A., Thibeault, M., and Walker, J. (2018). Estimating surface soil moisture from SMAP observations using a Neural Network technique, Remote Sensing of Environment, 204: 43-59.

Bindlish, R., Jackson, T.J., Cosh, M.H., Koike, T., Fujji, X., de Jeu, R., Chan, S., Asanuma, J., Berg, A., Bosch, D., Caldwell, T., Holifield-Collins, C., McNairn, H., Martinez-Fernandez, J., Prueger, J., Seyfried, M., Starks, P., Su, Z., Thibeault, M., Walker, J., and Coopersmith, E. (2018). GCOM-W AMSR2 soil moisture product validation using core validation sites, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11:209-219.

Chan, S.K., Bindlish, R., O’Neill, P., Jackson, T., Njoku, E., Dunbar, S., Chaubell, J., Piepmeier, J., Yueh, S., Entekhabi, D., Colliander, A., Chen, F., Cosh, M., Caldwell, T., Walker, J., Berg, A., McNairn, H., Thibeault, M., Martínez-Fernández, J., Uldall, F., Seyfried, M., Bosch, D., Starks, P., Holifield-Collins, C., Prueger, J., van der Velde, R., Asanuma, J., Palecki, M., Small, E.E., Zreda, M., Calvet, J.-C.,  Crow, W.T., and Kerr, Y. (2018). Development and assessment of the SMAP enhanced passive soil moisture product, Remote Sensing of Environment, 204: 931-941.

Niazmardi, S., Homayouni, S., Safari, A., Shang, J., and McNairn, H.  (2018). Multiple Kernel Representation and Classification of Multivariate Satellite-Image Time-Series for Crop Mapping, International Journal of Remote Sensing, 39:149-168.

Colliander, A., Jackson, T.J., Chan, S.K., O’Neill, P., Bindlish, R., Cosh, M.H., Caldwell, T., Walker, J.P., Berg, A., McNairn, H., Thibeault, M., Martinez-Fernandez, J., Jensen, K.H., Asanuma, J., Seyfried, M.S., Dosch, D.D., Starks, P.J., Holifield Collins, C., Prueger, J.H., Su, Z., Lopez-Baeza, E., and Yueh, S.H. (2018). An assessment of the differences between spatial resolution and grid size for the SMAP enhanced soil moisture product over homogeneous sites, Remote Sensing of Environment, 207: 65-70.

Rowlandson, T.L., Berg, A.A., Bullock, P.R., Hanis, K., Ojo, E.R., Cosh, M.H., Powers, J., and McNairn, H. (2018). Temporal transferability of soil calibration equations, Journal of Hydrology, 556: 349-358.

Reichle, R.H. De Lannoy, G.J.M., Liu, Q., Ardizzone, J.V. Colliander, A., Conaty, A., Crow, W., Jackson, T.J., Jones, L.A., Kimball, J.S., Koster, R.D., Mahanama, S.P., Smith, E.B., Berg, A., Bircher, S., Bosch, D., Caldwell, T.G., Cosh, M., Gonález-Zamora, A., Holifield Collins, C.D., Jensen, K.H. Livingston, S., Lopez-Baeza, E., Martínez-Fernández, McNairn, H., Moghaddam, M., Pacheco, A., Pellarin, T., Prueger, J., Rowlandson, T., Seyfied, M., Starks, P., Su, Z., Thibeault, M., van der Velde, R., Walker, J., Wu, X,., and Zeng, Y. (2017). Assessment of the SMAP level-4 surface and root-zone soil moisture product using in situ measurements, Journal of Hydrometeorology, 18:2621-2645.

Manns, H., Berg, A., Bullock, P.R., McNairn, H., von Bertoldi, P., Groenevelt, P. and Yang, W.  (2017). Importance of soil organic carbon in near-surface soil water content estimation: A simple model comparison in dry-end Canadian Prairie soils.  Canadian Water Resources Journal, 42:364-377.

Khosravi, I., Abdolreza, S., Homayouni, S., and McNairn, H. (2017). Enhanced decision tree ensembles for land cover mapping from fully polarimetric SAR data, International Journal of Remote Sensing, 38:7138-7160.

Kumar, V., McNairn, H., Bhattacharya, and Rao, Y.S. (2017). Temporal response of scattering from crops for transmitted ellipticity variation in simulated compact-pol SAR data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 10:5163-5174.

Zhang, H., Li, Q., Liu, J., Shang, J., Du, X., McNairn, H., Champagne, C., Dong, T., and Liu, M. (2017). Image classification using Rapideye data: integration of spectral and textural features in a Random Forest classifier. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 10:5334-5349.

Zhang, H., Li, Q., Liu, J., Du, X., Dong, T., McNairn, H., Champagne, C., Liu, M., and Shang, J. (2017). Object-based crop classification using multi-temporal SPOT imagery and textural features with a Random Forest classifier. Geocarto International, doi: 10.1080/10106049.2017.1333533.                                                                                                                                                                                                                                                                Steele-Dunne, S.C., McNairn, H., Monsivais-Huertero, A., Judge, J., Liu, P-W., and Papathanassiou, K. (2017). Radar remote sensing of agricultural canopies: a review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10: 2249-2273.                                                                                                                                       

Tamiminia, H., Homayouni, S., McNairn, H., Safari, A. (2017). A particle swarm optimized kernel-based clustering method for agriculture crop mapping from multi-temporal polarimetric L-Band SAR observations. International Journal of Applied Earth Observation and Geoinformation, 58:201-212.

Colliander, A. Jackson, T.J., Bindlish, R. Chan, S, Das, N., Kim, S.B., Cosh, M.H., Dunbar, R.S., Dang, L, Pashaian, L., Asanuma, J., Aida, K., Berg, A., Rowlandson, T., Bosch, D., Caldwell, T., Caylor, K., Goodrich, D., al Jassar, H., Lopez-Baeza, E., Martínez-Fernández, J., González-Zamora, A., Livingston, S., McNairn, H., Pacheco, A., Moghaddam, M., Montzka, C., Notarnicola, C., Niedrist, G., Pellarin, T., Prueger, J., Pulliainen, J., Rautiainen, K., Ramos, J., Seyfried, M., Starks, P., Su, Z., Zeng, Y., van der Velde, R., Thibeault, M., Dorigo, W., Vreugdenhil, M., Walker, J.P., Wu, X., Monerris, A., O’Neill, P.E., Entekhabi, D., Njoku, E.G., and Yueh, S. (2017).  Validation of SMAP surface soil moisture products with core validation sites. Remote Sensing of Environment, 191:215-231.

Garnaud, C., Bélair, S., Carrera, M.L., McNairn, H., and Pacheco, A. (2017). Field scale spatial variability of soil moisture and L-band brightness temperature from land surface modeling. Journal of Hydrometeorology, 18:573-589. doi:10.1175/JHM-D-16-0131.1

Bhuiyan, H.A.K.M, McNairn, H., Powers, J., and Merzouki, A. (2017). Application of HEC-HMS in a cold regions watershed and use of RADARSAT-2 soil moisture in initializing the model. Hydrology, 9. doi:10.3390/hydrology4010009 (published on-line)

Hosseini, M., and McNairn, H. (2017). Using multi-polarization C- and L-band radar to estimate biomass and soil moisture for wheat fields. International Journal of Applied Earth Observation and Geoinformation, 58:50-64.

Langue

Anglais
Français