Estimating winter wheat yield by assimilation of remote sensing data with a four-dimensional variation algorithm considering anisotropic background error and time window

Abstract

To further improve the yield estimation accuracy of regional winter wheat, based on En4DVar algorithm, an anisotropic background error and time window four-dimensional variation (ABT-4DVar) algorithm was constructed by introducing a variation time window. Based on a comparison with the simulated crop yield without data assimilation at a single point scale and in a typical test area, the effectiveness and feasibility of the constructed ABT-4DVar algorithm were effectively verified. On this basis, using the official statistics as verification data, the ABT-4DVar algorithm was applied to a winter wheat yield estimation system by assimilating remotely sensed LAI into the WOFOST (WOrld FOod STudies) crop growth model at a regional scale. The yield estimation results of winter wheat in Hengshui city showed that the RE between the average estimated yield and official statistics was 3.18%, and the REs between the average estimated yields and the official statistics of the 11 counties were all within 14.80%. The yield estimation results of winter wheat in Hengshui city indicated that the ABT-4DVar algorithm had good performance in regional yield estimation and that the assimilation algorithm could provide technology for crop growth evaluation and yield simulation.