Crop growth monitoring by integration of time series remote sensing imagery and the WOFOST model

An important vegetation boiphisical parameter, Leaf Area Index (LAI), could be employed for crop growth monitoring in agriculture. Optical and active remote sensing techniques improve directly acquirement of LAI for large spatial extents. For the accuracy improvement of retrieving LAI from remote sensing imageries, we employs least square method as the algorithm assimilating time series HJ CCD imageries into WOFOST based on its fundamental principals analysis. We first set up the relationship between vegetation indices and LAI at experiment sites for time series LAI calculation. Then the WOFOST model localization and initialization was finished before assimilated with remote sensing data. LAI was the joint parameter for model assimilation and the least square method was employed for optimizing model parameters. The calibrated crop growth model was then used for crop growth monitoring and yield estimation in Yutian, Hebei Province. The retrieving LAI value and the model yield were used for assessing the feasibility of the proposed method. The results illuminates that the error of yield and LAI compared with their site specific counterparts are improved 2.28% and 4.98% respectively, which shows the feasibility of the presented method and provides a new choice for spatialising crop growth model at regional scale.