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[ Author(Desc)] Title Type Year
S
Soleymani, A. , & K Scott, A. . (2023). Arctic marginal ice zone interannual variability and change point detection using two definitions. Environmental Research Letters, 18(12), 124002. Retrieved from https://iopscience.iop.org/article/10.1088/1748-9326/ad0609
Soleymani, A. , & K. Scott, A. . (2023). Passive microwave sea ice edge displacement error over the eastern Canadian Arctic 2013-2021. Canadian Journal of Remote Sensing.
Stonebridge, G. , K Scott, A. , & Buehner, M. . (2018). The impact of assuming a diagonal obervation error covariance matrix on sea ice analyses: Experiments using a 1D toy model. Tellus A, 70(1).
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Tamber, M. , Scott, K. A. , & Pedersen, L. T. . (2022). Accounting for label errors when training a convolutional neural network to estimate sea ice concentration using operational ice charts. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. Retrieved from https://ieeexplore.ieee.org/document/9674865
Tan, W. , K Scott, A. , & LeDrew, E. . (2014). Enhanced Arctic Ice Concentration Estimation Merging MODIS Ice Surface Temperature and SSM/I Sea-Ice Concentration. Atmosphere-Ocean, 52(2), 115-124. Retrieved from https://doi.org/10.1080/07055900.2014.883491
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Valencia, R. , Chen, X. , & K. Scott, A. . (2023). Predicting Sea Ice Concentration with Calibrated Uncertainty Quantification using Passive Microwave and Reanalysis Data: A Case Study in Baffin Bay. IEEE Transcations on Geoscience and Remote Sensing. sic_uncertainty_tgrs_paper_revision.pdf
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Wang, L. , K Scott, A. , & Clausi, D. A. . (2017). Sea ice concentration estimation during freeze-up from SAR imagery using a convolutional neural network. Remote Sensing, 9(5).
Wang, L. , K Scott, A. , & Clausi, D. A. . (2016). Improved sea ice concentration estimation through fusing classified SAR imagery and AMSR-E data. Canadian Journal of Remote Sensing, 42(1), 41-52.
Wang, L. , K Scott, A. , Xu, L. , & Clausi, D. A. . (2016). Sea ice concentration estimation during melt from dual-pol SAR scenes using deep convolutional neural networks: A case study. IEEE Transactions on Geoscience and Remote Sensing, 54(8), 4524-4533. Retrieved from doi:10.1109/TGRS.2016.2543660
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Xu, Y. , & K Scott, A. . (2019). Impact of intermediate ice concentration training data on sea ice concentration estimates from convolutional neural networks. International Journal of Remote Sensing, 40(15). Retrieved from doi:10.1080/01431161.2019.1582113
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Ziada, S. , K Scott, A. , & Arthurs, D. . (2007). Acoustic excitation by flow in T-junctions. Journal of Pressure Vessel Technology, 129(129), 14-20.

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