A methodology for estimating Leaf Area Index by assimilating remote sensing data into crop model based on temporal and spatial knowledge

In this paper, a methodology for Leaf Area Index (LAI) estimating was proposed by assimilating remote sensed data into crop model based on temporal and spatial knowledge. Firstly, sensitive parameters of crop model were calibrated by Shuffled Complex Evolution method developed at the University of Arizona (SCE-UA) optimization method based on phenological information, which is called temporal knowledge. The calibrated crop model will be used as the forecast operator. Then, the Taylor’s mean value theorem was applied to extracting spatial information from the Moderate Resolution Imaging Spectroradiometer (MODIS) multi-scale data, which was used to calibrate the LAI inversion results by A two-layer Canopy Reflectance Model (ACRM) model. The calibrated LAI result was used as the observation operator. Finally, an Ensemble Kalman Filter (EnKF) was used to assimilate MODIS data into crop model. The results showed that the method could significantly improve the estimation accuracy of LAI and the simulated curves of LAI more conform to the crop growth situation closely comparing with MODIS LAI products. The root mean square error (RMSE) of LAI calculated by assimilation is 0.9185 which is reduced by 58.7% compared with that by simulation (0.3795), and before and after assimilation the mean error is reduced by 92.6% which is from 0.3563 to 0.0265. All these experiments indicated that the methodology proposed in this paper is reasonable and accurate for estimating crop LAI.