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TitleComparison of compact and fully polarimetric SAR for multitemporal wetland monitoring
 
AuthorDabboor, M; Banks, S; White, L; Brisco, B; Behnamian, A; Chen, Z; Murnaghan, K
SourceIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing vol. 12, 5, 8700211, 2019 p. 1417-1430, https://doi.org/10.1109/JSTARS.2019.2909437 Open Access logo Open Access
Year2019
Alt SeriesNatural Resources Canada, Contribution Series 20190616
Documentserial
Lang.English
Mediapaper; on-line; digital
File formatpdf
Subjectsgeophysics; remote sensing; wetlands; SAR; synthetic aperture radar surveys (SAR)
ProgramCanada Centre for Remote Sensing Flood Mapping Guidelines
Released2019 04 26
AbstractA key challenge in developing models for the fusion of surface reflectance data across multiple satellite sensors is ensuring that they apply to both gradual vegetation phenological dynamics and abrupt land surface changes. To better model land cover spatial and temporal changes, we proposed previously a Prediction Smooth Reflectance Fusion Model (PSRFM) that combines a dynamic prediction model based on the linear spectral mixing model with a smoothing filter corresponding to the weighted average of forward and backward temporal predictions. One of the significant advantages of PSRFM is that PSRFM can model abrupt land surface changes either through optimized clusters or the residuals of the predicted gradual changes. In this paper, we expanded our approach and developed more efficient methods for clustering. We applied the new methods for dramatic land surface changes caused by a flood and a forest fire. Comparison of the model outputs showed that the new methods can capture the land surface changes more effectively. We also compared the improved PSRFM to two most popular reflectance fusion algorithms: Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and Enhanced version of STARFM (ESTARFM). The results showed that the improved PSRFM is more effective and outperforms STARFM and ESTARFM both visually and quantitatively. © 2019 by the authors.
GEOSCAN ID321945

 
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