Object-oriented crop mapping and monitoring using multi-temporal polarimetric RADARSAT-2 data |
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| Institution: | 1. Eastern Cereal and Oilseed Research Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada;2. Natural Resource Canada, Ottawa, ON, Canada;3. Geography Department, Faculty of Arts and Science, Nipissing University, North Bay, ON, Canada;1. Universitat Politècnica de Catalunya, CommSensLab & IEEC/UPC, Jordi Girona 1-3, E-08034 Barcelona, Spain;2. Image Processing Lab (IPL), Universitat de València, 46980 Valencia, Spain;3. Department of Earth System Science, Stanford University, Stanford 94305, CA, United States;4. Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge 02139, MA, United States;1. Department of Geography, The University of Western Ontario, London, Ontario, Canada;2. Research Branch, Agriculture and Agri-Food Canada, Ottawa, Ontario, Canada |
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| Abstract: | The aim of this paper is to assess the accuracy of an object-oriented classification of polarimetric Synthetic Aperture Radar (PolSAR) data to map and monitor crops using 19 RADARSAT-2 fine beam polarimetric (FQ) images of an agricultural area in North-eastern Ontario, Canada. Polarimetric images and field data were acquired during the 2011 and 2012 growing seasons. The classification and field data collection focused on the main crop types grown in the region, which include: wheat, oat, soybean, canola and forage. The polarimetric parameters were extracted with PolSAR analysis using both the Cloude–Pottier and Freeman–Durden decompositions. The object-oriented classification, with a single date of PolSAR data, was able to classify all five crop types with an accuracy of 95% and Kappa of 0.93; a 6% improvement in comparison with linear-polarization only classification. However, the time of acquisition is crucial. The larger biomass crops of canola and soybean were most accurately mapped, whereas the identification of oat and wheat were more variable. The multi-temporal data using the Cloude–Pottier decomposition parameters provided the best classification accuracy compared to the linear polarizations and the Freeman–Durden decomposition parameters. In general, the object-oriented classifications were able to accurately map crop types by reducing the noise inherent in the SAR data. Furthermore, using the crop classification maps we were able to monitor crop growth stage based on a trend analysis of the radar response. Based on field data from canola crops, there was a strong relationship between the phenological growth stage based on the BBCH scale, and the HV backscatter and entropy. |
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| Keywords: | Crops Object-oriented classification PolSAR Polarimetric decomposition Crop mapping Phenological monitoring |
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