Integration of optical and Synthetic Aperture Radar (SAR) imagery for delivering operational annual crop inventories |
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| Authors: | Heather McNairn Catherine Champagne Jiali Shang Delmar Holmstrom Gordon Reichert |
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| Institution: | 1. Faculty of Civil Engineering, Istanbul Technical University, Istanbul TR-34469, Turkey;2. Institute of Computing Research, University of Alicante, Alicante E-03080, Spain;3. Institute of Environmental Engineering, ETH Zurich, Zurich CH-8093, Switzerland;4. Microwaves and Radar Institute, German Aerospace Centre (DLR), Oberpfaffenhofen 82234, Germany;1. Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A0C6, Canada;2. Department of Geography, Nipissing University, North Bay, ON P1B 8L7, Canada |
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| Abstract: | Agriculture plays a critical role within Canada’s economy and, as such, sustainability of this sector is of high importance. Targeting and monitoring programs designed to promote economic and environmental sustainability are a vital component within Canada’s agricultural policy. A hierarchy of land information, including up to date information on cropping practices, is needed to measure the impacts of programs on land use decision-making and to gauge the environmental and economic benefits of these investments. A multi-year, multi-site research activity was completed to develop a robust methodology to inventory crops across Canada’s large and diverse agricultural landscapes. To move towards operational implementation the methodology must deliver accurate crop inventories, with consistency and reliability. In order to meet these operational requirements and to mitigate risk associated with reliance on a single data source, the methodology integrated both optical and Synthetic Aperture Radar (SAR) imagery. The results clearly demonstrated that multi-temporal satellite data can successfully classify crops for a variety of cropping systems present across Canada. Overall accuracies of at least 85% were achieved, and most major crops were also classified to this level of accuracy. Although multi-temporal optical data would be the preferred data source for crop classification, a SAR-optical dataset (two Envisat ASAR images and one optical image) provided acceptable accuracies and will mitigate risk associated with operational implementation. The preferred dual-polarization mode would be VV–VH. Not only were these promising classification results repeated year after year, but the target accuracies were met consistently for multiple sites across Canada, all with varying cropping systems. |
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