Estimating the biomass of rice by combining GF-1 and RADARSAT-2 data

Crop aboveground biomass (AGB) is one of the most important indicators in diagnosing and monitoring agricultural ecosystems. AGB estimation not only closely relates to monitoring crop yield and production but also contributes to the research about the carbon cycle process and global climate change. In this study, the AGB of rice was estimated by vegetation indices (VIs) from optical data (GF-1) and polarization parameters (PPs) from radar data (RADARSAT-2) by best-fitting regression function first. Then, considering the different characteristics of these two types of remote sensing data, the vegetation indices and polarization parameters were combined to estimate the rice AGB. The results showed that all the selected vegetation indices and most of the polarization parameters were significantly correlated with the measured rice biomass; CIgreen and Anisotropy presented the best performance ( R 2 = 0.6123, RMSE = 0.4861 kg/m 2 and R 2 = 0.6543, RMSE = 0.5418 kg/m 2 , respectively). Compared with a single index or parameter, the new hybrid index significantly improves the biomass estimation: R 2 = 0.7049; RMSE = 0.4849 kg/m 2 . A sensitivity analysis further revealed that combining optical vegetation index and microwave bands maintains the good prediction of AGB during the whole rice growth period. The study has proved the potential of retrieving rice AGB jointly using optical images and SAR data.