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.
Highlights
Rice is the main staple food in Asian countries, which feeds about 3.5 billion people worldwide and accounts for 90% of the global rice supply [1]
The heterogeneity of the rice canopy in the horizontal direction was considered and the double-bounce scattering was added in the modified water cloud model (MWCM)
Improved polarimetric decomposition was adopted, which reduced the overestimation of volume scattering and the negative pixels from the Freeman decomposition
Summary
Rice is the main staple food in Asian countries, which feeds about 3.5 billion people worldwide and accounts for 90% of the global rice supply [1]. Considering rice always grows in the rainy season in almost all Asian countries, the all-weather capability of synthetic aperture radar. (SAR) makes it suitable for monitoring rice paddy fields when it is sensitive to the structure of the rice canopy [4,5,6,7,8,9]. In order to relate SAR signatures to the rice variables, numerous scattering models have been developed, including empirical [9,10], semi-empirical [11,12], and physical models [13,14]. The empirical model is the simplest way to estimate the rice variables, it lacks a theoretical basis and is only suitable for the specific study site [15]. The physical model consists of complicated equations describing the interaction between radar signals and targets accurately, but it is too complex to be applied in practice and insufficient input parameters could cause ambiguities [3]
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