Improving regional winter wheat yield estimation through assimilation of phenology and leaf area index from remote sensing data

Abstract

Abstract Crop yield estimation at regional scale using crop model is generally subjected to large uncertainties from insufficient spatial information on heterogeneous growth environment and agronomic management practices. To solve this problem, we assimilated crop phenology and leaf area index (LAI) derived from remote sensing into a crop model (MCWLA-Wheat) to improve its reliability in estimating winter wheat yields at regional scale. Since the LAI magnitude was obviously underestimated however its spatial pattern was relatively well captured by remote sensing, we developed a novel spatial assimilation scheme that assimilated the spatial differences instead of the absolute values of LAI into crop model. Firstly, we retrieved the information of critical development stages of winter wheat from remote sensing data to adjust the simulation of phenology by MCWLA-Wheat model; then the spatial differences of LAI derived from remote sensing were assimilated into the MCWLA-Wheat model using a kind of constant gain Kalman Filter algorithm to improve the ability of the model in estimating winter wheat LAI and yields at regional scale in the North China Plain. This assimilation scheme extracted effective information from remote sensing LAI and meanwhile abandoned the information with obvious errors, ensuring that the assimilation variables could be close to the reality. It avoids the requirement for correction of the LAI derived from remote sensing using other high-quality ancillary data from field measurements. Using this assimilation scheme, the performance of crop model improved substantially. It successfully produced more accurate yield estimates at regional scale during the period of 2001–2008 (mean R 2 = 0.42, RMSE = 737/ha) than those without assimilation (mean R 2 = 0.26, RMSE = 1012 kg/ha) and those directly assimilating the absolute LAI values derived from remote sensing (mean R 2 = 0.30, RMSE = 1257/ha). Our findings demonstrated a reliable and promising assimilation scheme for improving yield estimation of crop model at regional scale with low data requirement.