Combination of Crop Growth Model and Radiation Transfer Model with Remote Sensing Data Assimilation for Fapar Estimation

Accurate assessment of Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) in large scale is significant for crop productivity estimation and climate change analysis. The object of study is to simulate FAPAR in the rice growth period for exploring photosynthetic capacity of rice in large-scale. The daily FAPAR is calculated based on a coupled model consisting of the leaf-canopy radiative transfer model (PROSAIL) and the World Food Study Model (WOFOST). Due to the limitation of the PROSAIL and WOFOST model, we introduced the remote sensing data assimilation method, which assimilated the Normalized Difference Vegetation Index (NDVI) into the coupled model, to improve the prediction accuracy and carry out the large-scale application. The results show high correlation between the simulated FAPAR and the measured data, with the determinate coefficient $(R^{2})$ of 0.75 in the study area. The spatial distribution of FAPAR is uniform in flat area, which indicates that the rice in the whole study area has well growth condition and photosynthetic capacity. This study suggest that the coupled model (PROSAIL + WOFOST) assimilated with remote sensing data could accurately simulate daily FAPAR during the crop growth period.