Prediction of summer grain crop yield with a process-based ecosystem model and remote sensing data for the northern area of the Jiangsu Province, China

Yield prediction is important for agricultural management, food security warning and food trade policy. Remote sensing has been a useful tool for predicting crop yields. In this study, a modified daily process-based ecosystem model (the Boreal Ecosystem Productivity Simulator) is employed in conjunction with land cover and leaf area index (LAI) products from MODIS to predict summer grain crop yields in the northern area of the Yangtze River in the Jiangsu Province, China. The model was driven by soil texture, land cover, daily meteorological and MODIS LAI data for 2004-2006. Simulated growing season net primary productivity (NPP) of summer grain crops (November-May) and census data of crop yields in 2004 were used to derive the county-level harvest index, which is then used in conjunction with simulated NPP to predict crop yields in 2005 and 2006. The model captures 89% and 88% of variations in crop yields at county-level compared with census data in 2005 and 2006, respectively. The root mean square errors are 265 and 277 kg ha(-1) in these two years. The results show the usefulness of a process-based model driven by remote sensing in predicting crop yields. In such predictions, the considerable spatial variability of the harvest index should be taken into consideration.