Integration of Ground Observations and Crop Simulation Model for Crop Leaf Area Index Estimation

Crop leaf area index (LAI) is a major indicator for crop growth monitoring and yield estimation. Actual observed data provide statistical properties of crop LAI, while crop simulation model provides physical properties of LAI within the whole crop growth period. Then, in this work, a data assimilation scheme integrating observations and crop simulation model based on Ensemble kalman filtering (EnKF) algorithm was proposed for LAI estimation. Calibrating input parameters and initial conditions of crop simulation model based on historic observed data, and then assimilating observations into crop simulation model for LAI estimation base on EnKF algorithm. This algorithm can integrate practical observed information into physical model to construct estimations obeying crop growth rules and actual growth situations. Winter wheat in Beijing was selected as experimental object. The numerical results showed enhanced estimation accuracy of the proposed algorithm. Theoretical analysis and practical experiments fully confirmed the feasibility and effectiveness of this algorithm in LAI estimation.