Based on historical weather data to predict summer field-scale maize yield: Assimilation of remote sensing data to WOFOST model by ensemble Kalman filter algorithm

Abstract

Effective support for agricultural production-management strategies relies on the accurate monitoring of crop growth and grain yield estimation at the field scale, making it an urgent need for the development of precision agriculture. Crop models are capable of simulating the growth and development of crops. In addition, Unmanned Aerial Vehicle (UAV) provides high temporal and spatial resolution for remote sensing, which can quickly and accurately obtain the crop growth status of small and medium-sized areas. This research utilized multispectral remote sensing information collected via UAV, alongside the WOFOST (World Food Studies) model, to evaluate the growth of summer maize within the experimental site. By utilizing vegetation indices derived from the UAV remote sensing data, the LAI (Leaf Area Index) of summer maize was ascertained, which further revealed a robust correlation between LAI and NDVI (Normalized Difference Vegetation Index). The model parameters were calibrated using crop and soil data from the Irrigation Experiment Station of Northwest A&F University. Verification indices revealed good consistency with a normalized root mean square error (nRMSE) of around 25 %, indicating that the model was suitable for simulating summer maize growth in this region. The ensemble Kalman filter algorithm was utilized to assimilate the remote sensing observation data with the WOFOST model, and the results showed that assimilation improved the accuracy of the yield simulation for each treatment. The optimal assimilation frequency should be more than three times, and assimilation during the flowering and grain-filling stages of summer maize could obtain better yield simulation results. By assimilating remote sensing observation data with 33 years of historical meteorological data from the Yangling Meteorological Station using the ensemble Kalman filter algorithm, this study predicted the yield and obtained a relative error of less than 5 %. The predicted yield value obtained on September 6 was more accurate than the predicted yield obtained at other times.