| Title | Comparison of compact and fully polarimetric SAR for multitemporal wetland monitoring |
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| Author | Dabboor, M ;
Banks, S; White, L; Brisco, B; Behnamian, A; Chen, Z; Murnaghan, K |
| Source | IEEE 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 |
| Year | 2019 |
| Alt Series | Natural Resources Canada, Contribution Series 20190616 |
| Document | serial |
| Lang. | English |
| Media | paper; on-line; digital |
| File format | pdf |
| Subjects | geophysics; remote sensing; wetlands; SAR; synthetic aperture radar surveys (SAR) |
| Program | Canada Centre for Remote Sensing Flood Mapping Guidelines |
| Released | 2019 04 26 |
| Abstract | A 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 ID | 321945 |
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