2 November 2017 Applying a particle filtering technique for canola crop growth stage estimation in Canada
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Accurate crop growth stage estimation is important in precision agriculture as it facilitates improved crop management, pest and disease mitigation and resource planning. Earth observation imagery, specifically Synthetic Aperture Radar (SAR) data, can provide field level growth estimates while covering regional scales. In this paper, RADARSAT-2 quad polarization and TerraSAR-X dual polarization SAR data and ground truth growth stage data are used to model the influence of canola growth stages on SAR imagery extracted parameters. The details of the growth stage modeling work are provided, including a) the development of a new crop growth stage indicator that is continuous and suitable as the state variable in the dynamic estimation procedure; b) a selection procedure for SAR polarimetric parameters that is sensitive to both linear and nonlinear dependency between variables; and c) procedures for compensation of SAR polarimetric parameters for different beam modes. The data was collected over three crop growth seasons in Manitoba, Canada, and the growth model provides the foundation of a novel dynamic filtering framework for real-time estimation of canola growth stages using the multi-sensor and multi-mode SAR data. A description of the dynamic filtering framework that uses particle filter as the estimator is also provided in this paper.
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Abhijit Sinha, Weikai Tan, Yifeng Li, Heather McNairn, Xianfeng Jiao, and Mehdi Hosseini "Applying a particle filtering technique for canola crop growth stage estimation in Canada ", Proc. SPIE 10421, Remote Sensing for Agriculture, Ecosystems, and Hydrology XIX, 104211F (2 November 2017); doi: 10.1117/12.2277968; https://doi.org/10.1117/12.2277968

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