This article presents an analysis of the scattering measurements for an entire wheat growth cycle by ground-based scatterometers at a frequency of 5.3 GHz. Since wheat ears are related to wheat growth and yield, the radar backscatter of wheat was analyzed at two different periods, i.e., with and without wheat ears. Simultaneously, parameters such as wheat and soil characteristics as well as volume scattering and soil scattering were analyzed for the two periods during the entire growth cycle. Wheat ears have been demonstrated to have a great influence on radar backscatter; therefore, a modified version of water-cloud model used for retrieving biomass should consider the effect of wheat ears. This work presents two retrieval models based on the water-cloud model and adopts the advanced integral equation model to simulate the soil backscatter before the heading stage and the backscatter from the layer under wheat ears after the heading stage. The research results showed that the biomass retrieved from the advanced synthetic aperture radar (ASAR) images to agree well with the data measured in situ after setting the modified water-cloud model for the growth stages with ears. Furthermore, it was concluded that wheat ears should form an essential component of theoretical modeling as they influence the final yield.
A biomass inversion algorithm based on a semi-empirical scattering model has been developed by using the simultaneous observation data, which are obtained by ground-based and space-based scatterometers during the rice-growing season. Three steps are applied to build the algorithm: (1) the backscattering coefficients are collected in eight acquisitions at different growth periods. Meanwhile, the ground-truth data are measured, such as rice biomass, leaf-area index, and canopy height and related ecophysiological canopy variables. Moreover, three scenes of advanced synthetic aperture radar (ASAR) AP images covering the study area are acquired. (2) The inversion models are built based on a semi-empirical rice water-cloudy model with the data measured in field. The rice backscattering coefficients of HH and VV polarizations are the input parameters. (3) By processing the ASAR images, the backscattering coefficients are extracted and input to the inversion model, and then the rice biomass maps are outputted at three different periods’ images. By comparing the rice biomass measured with the inverse values from the scattering data and SAR images, it shows that the inversion values are considerably consistent with the true measured values. The inversion results show that the multitemporal SAR images at the C-band can be used to monitor the growth of rice by using the semi-empirical inversion model.
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