Rice biomass retrieval from advanced synthetic aperture radar image based on radar backscattering measurement

Abstract

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.