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Remote Sens. 2016, 8(10), 878; doi:10.3390/rs8100878

Estimation of Paddy Rice Variables with a Modified Water Cloud Model and Improved Polarimetric Decomposition Using Multi-Temporal RADARSAT-2 Images

1
Institute of Remote Sensing and Digital Earth (RADI), Chinese Academy of Sciences, Datun Road, Chaoyang District, Beijing 100094, China
2
College of Resources and Environment, University of Chinese Academy of Sciences, Yuquan Road, Shijingshan District, Beijing 100049, China
3
Earth Observation and Geosolutions Division (EOGD), Earth Sciences Sector, Canada Centre for Remote Sensing, Natural Resources Canada, Ottawa, ON K1S 4M2, Canada
*
Author to whom correspondence should be addressed.
Academic Editors: Nicolas Baghdadi and Prasad S. Thenkabail
Received: 16 August 2016 / Revised: 12 October 2016 / Accepted: 18 October 2016 / Published: 23 October 2016
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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. View Full-Text
Keywords: paddy rice; polarimetry; water cloud model (WCM); LAI; height; ear biomass; heterogeneity; RADARSAT-2; synthetic aperture radar (SAR) paddy rice; polarimetry; water cloud model (WCM); LAI; height; ear biomass; heterogeneity; RADARSAT-2; synthetic aperture radar (SAR)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Yang, Z.; Li, K.; Shao, Y.; Brisco, B.; Liu, L. Estimation of Paddy Rice Variables with a Modified Water Cloud Model and Improved Polarimetric Decomposition Using Multi-Temporal RADARSAT-2 Images. Remote Sens. 2016, 8, 878.

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