Dr. Heather McNairn

Image Heather McNairn
Research Scientist

My research develops methods, models and algorithms to monitor the state and condition of our soils and crops using data from multi-spectral, hyperspectral and Synthetic Aperture Radar (SAR) satellites.

Agriculture and Agri-Food Canada
960 Carling Avenue Ottawa, ON

Current research and/or projects

I am currently leading two research projects:

“An International Comparison Of Synthetic Aperture Radar (SAR) Based Methods For Crop Type And Crop Condition Monitoring: Developing An Operational Monitoring Capability For Canada, And Beyond” 

"Quantifying Crop Productivity from Space: A New Radar Satellite-based​ Metric for Measuring Crop Response"

 

Research and/or project statements

Fields of Science

Learn more about my research and how it impacts you by visiting Fields of Science, a campaign featuring 11 Agriculture and Agri-Food Canada scientists from coast to coast.

Additional links

Reaching for the Stars: Advice from a Scientist in Satellite Research

Key publications

  1. Arcand, Y., Bittman, S., Britten, M., Champagne, C., Daneshfar, B., David, S., Davidson, A., Derdall, E. Drury, C., Eden, J., Handyside, P., Hung, Y., Javorek, S., Jiang, Y., Jingui, Y., Jordan, C., Joosse, P., Li, S., McAuley, E., McNairn, H., MacDonald, D., Pacheco, A., Powers, J., Reid, K., Smith, E. L., Topp, E., Vanrobaeys, J., Villeneuve, S., Wilson, H., Yang, J. Agricultural Land Use. Synthesis of Science. Canada Water Agency Report. Freshwater Science Assessment-Line of Inquiry. Nov. 20, 2020.

    2020 - View publication details

  2. Dey, S., Bhattacharya, A., Ratha, D., Mandal, D., McNairn, H., Lopez-Sanchez, J.M., Rao, Y.S. (2020). Novel clustering schemes for full and compact polarimetric SAR data: An application for rice phenology characterization. ISPRS Journal of Photogrammetry and Remote Sensing, [online] 169 135-151. http://dx.doi.org/10.1016/j.isprsjprs.2020.09.010

    2020 - View publication details

  3. Mandal, D., Ratha, D., Bhattacharya, A., Kumar, V., McNairn, H., Rao, Y.S., Frery, A.C. (2020). A Radar vegetation index for crop monitoring using compact polarimetric sar data. IEEE Transactions on Geoscience and Remote Sensing, [online] 58(9), 6321-6335. http://dx.doi.org/10.1109/TGRS.2020.2976661

    2020 - View publication details

  4. Mandal, D., Kumar, V., Lopez-Sanchez, J.M., Bhattacharya, A., McNairn, H., Rao, Y.S. (2020). Crop biophysical parameter retrieval from Sentinel-1 SAR data with a multi-target inversion of Water Cloud Model. International Journal of Remote Sensing, [online] 41(14), 5503-5524. http://dx.doi.org/10.1080/01431161.2020.1734261

    2020 - View publication details

  5. Laamrani, A., Joosse, P., McNairn, H., Berg, A.A., Hagerman, J., Powell, K., Berry, M. (2020). Assessing soil cover levels during the non-growing season using multitemporal satellite imagery and spectral unmixing techniques, 12(9), http://dx.doi.org/10.3390/rs12091397

    2020 - View publication details

  6. McNairn, H., Jiao, X., Pacheco, A., Sinha, A., Tan, W., Li, Y. (2018). Estimating canola phenology using synthetic aperture radar. Remote Sensing of Environment, [online] 219 196-205. http://dx.doi.org/10.1016/j.rse.2018.10.012

    2018 - View publication details

  7. Zhang, H., Li, Q., Liu, J., Du, X., Dong, T., McNairn, H., Champagne, C., Liu, M., Shang, J. (2018). Object-based crop classification using multi-temporal SPOT-5 imagery and textural features with a Random Forest classifier. Geocarto International, [online] 33(10), 1017-1035. http://dx.doi.org/10.1080/10106049.2017.1333533

    2018 - View publication details

  8. Zwieback, S., Colliander, A., Cosh, M.H., Martínez-Fernández, J., McNairn, H., Starks, P.J., Thibeault, M., Berg, A. (2018). Estimating time-dependent vegetation biases in the SMAP soil moisture product. Hydrology and Earth System Sciences, [online] 22(8), 4473-4489. http://dx.doi.org/10.5194/hess-22-4473-2018

    2018 - View publication details

  9. Niazmardi, S., Homayouni, S., Safari, A., Shang, J., McNairn, H. (2018). Multiple kernel representation and classification of multivariate satellite-image time-series for crop mapping. International Journal of Remote Sensing, [online] 39(1), 149-168. http://dx.doi.org/10.1080/01431161.2017.1381351

    2018 - View publication details

  10. Zhang, H., Li, Q., Liu, J., Shang, J., Du, X., McNairn, H., Champagne, C., Dong, T., Liu, M. (2017). Image Classification Using RapidEye Data: Integration of Spectral and Textual Features in a Random Forest Classifier. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, [online] 10(12), 5334-5349. http://dx.doi.org/10.1109/JSTARS.2017.2774807

    2017 - View publication details

  11. Manns, H.R., Berg, A.A., Bullock, P.R., McNairn, H., Groenevelt, P., 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, [online] 42(4), 364-377. http://dx.doi.org/10.1080/07011784.2017.1383188

    2017 - View publication details

  12. Global Strategy to improve Agricultural and Rural Statistics (GSARS). 2017. Handbook on Remote Sensing for Agricultural Statistics. GSARS Handbook: Rome.

    Davidson, A.M., Fisette, T., Mcnairn, H. & Daneshfar, B. 2017. Detailed crop mapping using remote sensing data (Crop Data Layers). In: J. Delincé (ed.), Handbook on Remote Sensing for Agricultural Statistics (Chapter 4). Handbook of the Global Strategy to improve Agricultural and Rural Statistics (GSARS): Rome.

    2017 - View publication details

  13. Hosseini, M., McNairn, H. (2017). Using multi-polarization C- and L-band synthetic aperture radar to estimate biomass and soil moisture of wheat fields. International Journal of Applied Earth Observation and Geoinformation, [online] 58 50-64. http://dx.doi.org/10.1016/j.jag.2017.01.006

    2017 - View publication details

  14. Tamiminia, H., Homayouni, S., McNairn, H., Safari, A. (2017). A particle swarm optimized kernel-based clustering method for crop mapping from multi-temporal polarimetric L-band SAR observations. International Journal of Applied Earth Observation and Geoinformation, [online] 58 201-212. http://dx.doi.org/10.1016/j.jag.2017.02.010

    2017 - View publication details

  15. 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., Yueh, S. (2017). Validation of SMAP surface soil moisture products with core validation sites. Remote Sensing of Environment, [online] 191 215-231. http://dx.doi.org/10.1016/j.rse.2017.01.021

    2017 - View publication details

  16. Haifa Tamiminia a, Saeid Homayouni b, Heather McNairn c, Abdoreza Safari a

    2017 - View publication details

  17. Bhuiyan, H.A.K.M., McNairn, H., Powers, J., Merzouki, A. (2017). Application of HEC-HMS in a cold region watershed and use of RADARSAT-2 soil moisture in initializing the model. Hydrology, [online] 4(1), http://dx.doi.org/10.3390/hydrology4010009

    2017 - View publication details

  18. Garnaud, C., Bélair, S., Carrera, M.L., McNairn, H., Pacheco, A. (2017). Field-scale spatial variability of soil moisture and L-band brightness temperature from land surface modeling. Journal of Hydrometeorology, [online] 18(3), 573-589. http://dx.doi.org/10.1175/JHM-D-16-0131.1

    2017 - View publication details

  19. Champagne, C., Rowlandson, T.L., Berg, A.A., Burns, T., L'Heureux, J., Tetlock, E., Adams, J.R., McNairn, H., Toth, B.M., and Itenfisu, D. (2016). "Satellite Surface Soil Moisture from SMOS and Aquarius: Assessment for Applications in Agricultural Landscapes.", International Journal of Applied Earth Observation and Geoinformation, 45(Part B), pp. 143-154. doi : 10.1016/j.jag.2015.09.004

    2016 - View publication details

  20. "From fields to regions: Improving crop model predictions, using remote sensing-derived biophysical descriptors and climate data." Pattey, E.,Jégo, G. ,A. Vanderzaag, J. Liu, B. Qian, X. Geng; Res. Team: S. Admiral, M. Mesbah, D. Dow, T. HotteF. Baret, M. Weiss, R. Lacaze, F. Camacho, N. Beaudoin, R. Desjardins, T. Huffman, H. McNairn 2016. 2016 Canadian JECAM Site Update: CFIA-Ottawa. JECAM Science Meeting Kyiv, 11-12 Oct 2016 (poster).

    2016 - View publication details

Contact

Research facility

Ottawa Research and Development Centre
960 Carling Avenue
Ottawa, ON K1A 0C6
Canada

Expertise

Agriculture›Remote sensing

Language

English
French