Abstract:
Abstract: Knowing the proper time to harvest crops is a major step towards successful farming because it helps to avoid the negative effects of bad weather and improves the quality and quantity of the crops. Thus, it is of significant importance to predict the maturity period of the crops for improving the production benefits and decreasing the loss. At present, researchers have developed models to predict the maturity period, including meteorological statistical model, crop-growing model, and remote sensing monitoring model, etc. But those models have limitations in timeliness, regional promotion or have complex implementation process. Therefore, in this research, we aimed at improving maturity period prediction of winter wheat using winter wheat planting regions in Hebei, Henan and Shandong province as study areas. Firstly we retrieved the heading stage from S-G filtered MODIS LAI in 2013 when wheat LAI reached the peak using dynamic threshold method. Then, in order to obtain the maturity period forecasts values for 1 km by 1 km winter wheat grids, effective accumulated temperatures and total radiation distribution from heading to maturity have been collected through historical agro-meteorological observational data and ground meteorological data from 2008 to 2012 by the Thiessen polygons method. It assumed that a Thiessen polygon has uniform varieties of winter wheat, uniform effective accumulated temperature and total solar radiation. Effective accumulated temperatures and total radiation model from heading to maturity were built based on the effective accumulated temperatures and total radiation distribution, then each grid cell was calculated for the accumulated temperature of the date when LAI reached its peak and total solar radiation. After that, in order to obtain the predicting data of all the regions, we predicted the maturity period of the winter wheat for each day for 16 days after the present time combining with TIGGE (THORPEX Interactive Grand Global Ensemble) and ground meteorological data. When the effective accumulated temperatures and total radiation of a grid cell met the requirements of effective accumulated temperatures and total radiation from heading to maturity, the winter wheat would reach maturity date. Finally, we used the heading period data of agricultural meteorological station to verify the data of maturity period. The results showed that the correlation coefficient R2 and the root mean square error (RMSE) between observed date and predicted date for the heading were 0.89 and 3.62 days, respectively. The R2 and RMSE between predicting date and observed date for the maturity was 0.92 and 2.89 days respectively. Predicting errors of maturity which was extracted from MODIS LAI haven't increased much more, it turned out that predicting accuracy for maturity based on meteorological data was higher than the maturity date based on remote sensing data, but the prediction of maturity based on remote sensing data fitted large scale region. So the prediction which used both remote sensing data and meteorological data could obtain the satisfactory results. The method provided a reference of crop maturity data for other agricultural regions. So the method was easy to be used in larger scale, and also serves as a simplified model. Besides, the method solved the existing problems of poor timeliness and lacking spatial distribution, thus it helps a lot to predict the maturity period of the crops.