Estimation of winter wheat yield in Hebi city based on crop models and remote sensing data assimilation

Abstract

Abstract Assimilating remote sensing data with crop models is an effective approach to improve the accuracy of crop model applications at the regional scale. Sobol global sensitivity analysis and Markov Chain Monte Carlo methods were used to calibrate parameters in the WOFOST and WheatSM models within a gridded model framework in the study area. Time series data reconstruction techniques were employed to correct MODIS LAI and ET data. Four assimilation scenarios were tested, including assimilation of only leaf area index (LAI), assimilation of only evapotranspiration (ET), simultaneous assimilation of LAI and ET and a control scenario without assimilation. These scenarios were applied to model winter wheat yields in Hebi City, China, from 2013 to 2018. Statistical validation demonstrated that assimilating ET or LAI individually significantly improved model accuracy compared to the control scenario with similar levels of improvement. The highest model accuracy was achieved when assimilating both ET and LAI simultaneously, showing the highest correlation coefficients and the lowest root mean square errors among the four scenarios. This research provides a basis for selecting assimilation variables when applying crop models at the regional scale. The coupling of crop growth models with remote sensing data empowers governments and agricultural producers to devise more effective agricultural strategies, allocate resources efficiently, and implement disaster response measures. This enhances scientific management within the agricultural sector, promotes increased food production and elevates farmers’ incomes.