In-season crop inventory using multi-angle and multi-pass RADARTSAT-2 SAR data over the Canadian prairies.

Shang, J., McNairn, H., Deschamps, J., and Jiao, X. (2011). "In-season crop inventory using multi-angle and multi-pass RADARTSAT-2 SAR data over the Canadian prairies.", Proceedings of SPIE - The International Society for Optical Engineering, 8156(Article No. 815604). doi : 10.1117/12.894211  Access to full text

Abstract

In response to the increasing demand on in-season crop inventory, this study presents results of early season crop identification and acreage estimates based on a random forest classifier using RADARSAT-2 fine quad (FQ) SAR data. Thirty RADARSAT-2 FQ SAR scenes acquired over Indian Head, Canada, during the 2009 AgriSAR campaign led by the European Space Agency (ESA) were analyzed. Consistent with results from other researches, this study revealed that the highest classification accuracies are achieved in mid to late season (early July to mid August) when most of the crops experiencing vegetative growth and early reproduction. In addition by incorporating multi-beam images, an increase in classification accuracy of 2% to 12% can be achieved. For images acquired close in time, shallower incidence angles usually give better classification accuracy compared with steeper incidence angles. In order to achieve optimal classification performance, both multi-temporal and multi-beam acquisitions should be combined. For major crops such as canola, spring wheat, lentil, and field peas, over 85% accuracies can be reached early in the growing season (early July) when multi-temporal multi-beam RADARSAT-2 FQ data are used.

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