| Title | Estimation of paddy rice variables with a modified water cloud model and improved polarimetric decomposition using multi-temporal RADARSAT-2 images |
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| Author | Yang, Z; Li, K; Shao, Y ; Brisco, B; Liu, L |
| Source | Remote Sensing vol. 8, no. 10, 878, 2016., https://doi.org/10.3390/rs8100878  Open Access |
| Year | 2016 |
| Alt Series | Natural Resources Canada, Contribution Series 20181064 |
| Publisher | MDPI AG |
| Document | serial |
| Lang. | English |
| Media | paper; on-line; digital |
| File format | pdf |
| Subjects | geophysics; remote sensing |
| Program | Climate Change
Geoscience |
| Released | 2016 10 23 |
| Abstract | Rice growth monitoring is very important as rice is one of the staple crops of the world. Rice variables as quantitative indicators of rice growth are critical for farming management and yield
estimation, and synthetic aperture radar (SAR) has great advantages for monitoring rice variables due to its all-weather observation capability. In this study, eight temporal RADARSAT-2 full-polarimetric SAR images were acquired during rice growth
cycle and a modified water cloud model (MWCM) was proposed, in which the heterogeneity of the rice canopy in the horizontal direction and its phenological changes were considered when the double-bounce scattering between the rice canopy and the
underlying surface was firstly considered as well. Then, three scattering components from an improved polarimetric decomposition were coupled with the MWCM, instead of the backscattering coefficients. Using a genetic algorithm, eight rice variables
were estimated, such as the leaf area index (LAI), rice height (h), and the fresh and dry biomass of ears (Fe and De). The accuracy validation showed the MWCM was suitable for the estimation of rice variables during the whole growth season. The
validation results showed that the MWCM could predict the temporal behaviors of the rice variables well during the growth cycle (R2 > 0.8). Compared with the original water cloud model (WCM), the relative errors of rice variables with the MWCM
were much smaller, especially in the vegetation phase (approximately 15% smaller). Finally, it was discussed that the MWCM could be used, theoretically, for extensive applications since the empirical coefficients in the MWCM were determined in
general cases, but more applications of the MWCM are necessary in future work. © 2016 by the authors. |
| GEOSCAN ID | 311418 |
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