Assimilating Remotely Sensed Information with the WheatGrow Model Based on the Ensemble Square Root Filter for Improving Regional Wheat Yield Forecasts

In this study, a deterministic algorithm named Ensemble Square Root Filter (EnSRF), an algorithm significantly improved from the Ensemble Kalman Filter (EnKF), was used to integrate remotely sensed information (ASD spectral data, HJ-1 A/B CCD and Landsat-5 TM data) with a wheat (Triticum aestivum L.) growth model (WheatGrow). The analyzed values of model variables, leaf area index (LAI) and leaf nitrogen accumulation (LNA), were calculated based on EnSRF without perturbed measurements. Independent datasets were used to test EnSRF and the root mean square error (RASE) values were 0.81 and 0.82 g m(-2), with relative error (RE) values of 0.15 and 0.13, for LAI and LNA, respectively. RMSE values for IAI and LNA were 1.39 and 1.70 g m(-2) respectively (RE, 0.28 and 0.34) based on EnKF, 1.17 and 1.80 g m(-2) (RE, 0.24 and 0.35), respectively, based on the WheatGrow model alone, and 0.97 and 1.25 g m(-2) (RE, 0.21 and 0.24), respectively, based on the remote sensing models. These results indicated that the LAI and LNA values based on EnSRF matched the measured values well compared with the EnKF, WheatGrow and remote sensing models. In addition, the predicted results are consistent with the temporal and spatial distribution of winter wheat growth status and grain yields in the study area, with RE values of less than 0.2 and 0.1 for LAI and LNA, respectively. These results provide an important approach for simulating winter wheat growth status based on combining remote sensing and crop growth models.