Modeling and Characteristics of Microwave Backscattering From Rice Canopy Over Growth Stages

This paper presents an electromagnetic modeling of temporal variations of microwave backscatter from rice canopy based on radiative transfer theory to understand the complex microwave scattering mechanisms of rice crops at different growth stages. Model validation is made by a comparison with field measurements from four independent campaigns by ground-based scatterometers and spaceborne synthetic aperture radar (SAR). Then, the frequency responses and angular dependence are examined, followed by an analysis of parameter sensitivity and uncertainty to microwave backscatter. The validation shows promising results for the physically based model in assessing radar response of rice plant in its full-growth stage. It is found that radar scattering of rice canopy is highly dependent on the growing stages of rice plants, in addition to radar configurations, such as frequency, polarization, and incident angle. Moreover, backscattering of rice canopy is more dependent on stems than on leaves, and as rice plants grow, increasing stems and leaves tend to promote HH- and VH-polarized scattering, while first promoting and then reducing VV-polarized scattering. Moreover, the greatest uncertainty takes place at the late growth stage for copolarization and at the early stages for cross-polarization. The main contributions of this paper can be summarized as follows: 1) the establishment of a rice canopy microwave backscattering model using radiative transfer theory adapted to all the stages in the phenological cycle of rice; 2) the validation of the performance of the proposed model with numerous independent measurements from spaceborne SAR and ground-based scatterometers from different countries and regions; 3) the investigation of the interactions between microwave backscatter signatures and rice canopy growth variables over growth stages; and 4) the identification of the dominant influential parameters of the rice scattering model and determination model uncertainty.