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Using Growing-Season Time Series Coherence for Improved Peatland Mapping: Comparing the Contributions of Sentinel-1 and RADARSAT-2 Coherence in Full and Partial Time Series.

, , , , , and . Remote. Sens., 12 (15): 2465 (2020)

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Multitemporal monitoring of wetlands using simulated radarsat constellation mission compact polarimetric SAR data., , , , and . IGARSS, page 4586-4589. IEEE, (2017)Multi-Source EO for Dynamic Wetland Mapping and Monitoring in the Great Lakes Basin., , , , , , , , , and 4 other author(s). Remote. Sens., 13 (4): 599 (2021)Impact of beam mode, multi-temporal, and multi-angle combinations of RADARSAT-2 data on class separability for three coastal sites throughout Arctic Canada., , , and . IGARSS, page 1002-1004. IEEE, (2014)InSAR monitoring of marsh wetlands flow dynamics in Great Lakes., , , , , , and . IGARSS, page 6848-6851. IEEE, (2019)Wetland Classification with Multi-Angle/Temporal SAR Using Random Forests., , , , , , , and . Remote. Sens., 11 (6): 670 (2019)Using Growing-Season Time Series Coherence for Improved Peatland Mapping: Comparing the Contributions of Sentinel-1 and RADARSAT-2 Coherence in Full and Partial Time Series., , , , , and . Remote. Sens., 12 (15): 2465 (2020)Comparison of Compact and Fully Polarimetric SAR for Multitemporal Wetland Monitoring., , , , , , and . IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 12 (5): 1417-1430 (2019)Dimensionality Reduction in The Presence of Highly Correlated Variables for Random Forests: Wetland Case Study., , , , , , and . IGARSS, page 9839-9842. IEEE, (2019)A Systematic Approach for Variable Selection With Random Forests: Achieving Stable Variable Importance Values., , , , , and . IEEE Geosci. Remote. Sens. Lett., 14 (11): 1988-1992 (2017)Semi-Automated Surface Water Detection with Synthetic Aperture Radar Data: A Wetland Case Study., , , , , , , , , and . Remote. Sens., 9 (12): 1209 (2017)