Simulation Of Winter Wheat In Jiangsu Province With The Optimized Model And Its Application

Accurate prediction of production and crop growth situation are conducive to the protection of regional or national food security, and it is of great significance to agricultural management and sustainable development. The satellite remote sensing data are objective, large coverage, timeliness of detection features, can provide new solutions in the detection of crops growth and yield estimation for crop growth model, therefore the combination of the two will be able to take advantage of effectively, improve crop growth monitoring and yield prediction model accuracy.This paper takes the winter wheat in Jiangsu Province as the research object,and the phenophase extracted by remote sensing data and the phenophase by model are as the connection. The SCE- UA algorithm is used to optimize the sensitivity parameters of WOFOST crop model, and the simulation results are compared with the measured data of winter wheat which from the xuzhou, huaian, kunshan three meteorological site. The purpose of this study was to improve the precision of crop yield estimation and monitoring the growth and development of Winter Wheat. Main work content is as follows:(1) Remote sensing data preprocessing. The remote sensing data used in this paper are the surface reflectance data and leaf area index data of MODIS. Because of the spaceborne sensor is easily affected by cloud, aerosol, soil background and other unpredictable factors, the MODIS data value will appear abnormal value, so this paper uses Savitzky-Golay filtering method to smooth the two data values. The MODIS-LAI data were corrected with the field measured leaf area index value and the Logistic equation. Then we obtain the time series curve of leaf area index in the growing period which is more in line with the actual situation of the growth and development of Winter Wheat.(2) Extraction of key phenological period of winter wheat in Jiangsu Province based on MODIS NDVI time series data. The preprocessing remote sensing data use the dynamic threshold method to extract of winter wheat greenup,heading and mature this three key phenological period..Then we get the distribution map of Winter Wheat in Jiangsu province phenological phase space, and use the measured data of winter wheat growth period in Xuzhou, Huaian, Xinghua and Kunshan to verify the extraction results. The results show that the mean root mean square error of greenup,heading and mature are all in 5, 6 days. And with the increasing of latitude,the phenological transition dates of greenup, heading and mature are gradually delayed,which resembles the actual situation.(3) WOFOST crop model localization. Use of meteorological data from 2001 to 2010 of xuzhou, huaian and kunshan three meteorological stations in Jiangsu Province, in combination with the results on crop parameters sensitivity analysis, thereference literature, to adjust the crop parameters and soil parameters of WOFOST crop model, established a winter wheat system parameters of WOFOST crop model in Jiangsu Province, then the simulation results were verified.The results show that localized model can basically meet the needs of the simulation process of growth and development of winter wheat in Jiangsu province.(4) Based on the localization of WOFOST crop model, the SCE-UA algorithm is used to optimize the model. The sensitivity analysis of the key crop parameters of the WOFOST crop model was carried out, and the two parameters of winter wheat sowing date (Sowing Date) and low temperature threshold (TMNFTB) were optimized. The growth period observation data of Xuzhou, Huaian and Kunshan during 2001-2010,production statistics and the continuous growth period MODIS-LAI corrected data are used to verify the optimization results. The results show that the simulated results of assimilation are better than that of assimilation,and are closer to the actual growth and development.