Estimation of summer maize biomass based on a crop growth model

The challenge in the field of agricultural remote sensing monitoring has always been how to use crop growth models to quantitatively analyze the dynamic changes of regional summer maize biomass（referring to aboveground biomass weight，AGBW）. In this study, we constructed a summer maize biomass process simulation model (SMSMBP) to analyze AGBW and its change characteristics at different growth stages of field summer maize in Jianghuai regional along the east coast of China. First, the initial biomass simulation model was used to estimate AGBW of summer maize from the seedling emergence to the jointing stage, from the jointing to the tasseling stage , and from the tasseling to the grain filling stage. Then, the biomass simulation model parameters were adjusted based on measured leaf area index and AGBW data at the jointing stage of summer maize. Finally, the adjusted model was used to estimate AGBW from jointing to tasseling and from tasseling to filling. The results showed that the relative errors of the predicted and measured values of summer maize AGBW from seedling emergence to before jointing, during the early stage of jointing, before tasseling, and during the grain filling stage were 3.35%, 11.56%, 23.26%, and 15.83%, respectively. For the adjusted model , the root mean square error (RMSE) between the predicted and the measured values before tasseling was 219.43 kg/hm2 and the relative error was 4.18%, and the relative error between the predicted and the measured values during the grain filling stage was 3.44%. Adjusting the model parameters using data from the maize jointing stage improved the prediction from jointing to tasseling and from tasseling to grain filling. This study provides a reference for the prediction of AGBW and its dynamic changes in different growth stages of summer maize, and could assist agricultural management department to adjust production measures.
Keywords: Biomass; Estimation; Planting areas along the east coast of China; Process simulation model; Summer maize