Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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-ather observation capability. In this study, eight temporal RADARSAT-2 full-polarimetric SAR images re 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 re considered when the double-bounce scattering beten the rice canopy and the underlying surface was firstly considered as ll. Then, three scattering components from an improved polarimetric decomposition re coupled with the MWCM, instead of the backscattering coefficients. Using a genetic algorithm, eight rice variables re 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 shod the MWCM was suitable for the estimation of rice variables during the whole growth season. The validation results shod that the MWCM could predict the temporal behaviors of the rice variables ll during the growth cycle (R2 > 0.8). Compared with the original water cloud model (WCM), the relative errors of rice variables with the MWCM re 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 re determined in general cases, but more applications of the MWCM are necessary in future work.
['Zhi Yang', 'Kun Li', 'Yun Shao', 'Brian Brisco', 'Long Liu']
I am writing a research paper on a idea Estimation of Paddy Rice Variables with a Modified Water Cloud Model and Improved Polarimetric Decomposition Using Multi-Temporal RADARSAT-2 Images
[{"from": "human", "value": "I am writing a research paper on a idea Estimation of Paddy Rice Variables with a Modified Water Cloud Model and Improved Polarimetric Decomposition Using Multi-Temporal RADARSAT-2 Images"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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-ather observation capability. In this study, eight temporal RADARSAT-2 full-polarimetric SAR images re 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 re considered when the double-bounce scattering beten the rice canopy and the underlying surface was firstly considered as ll. Then, three scattering components from an improved polarimetric decomposition re coupled with the MWCM, instead of the backscattering coefficients. Using a genetic algorithm, eight rice variables re 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 shod the MWCM was suitable for the estimation of rice variables during the whole growth season. The validation results shod that the MWCM could predict the temporal behaviors of the rice variables ll during the growth cycle (R2 > 0.8). Compared with the original water cloud model (WCM), the relative errors of rice variables with the MWCM re 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 re determined in general cases, but more applications of the MWCM are necessary in future work."}]