Study On Winter Wheat Regional Simulation Model Based On Remote Sensing Data And It's Simulations In North China

Accurate crop growth monitoring and yield forecasting is significant to food security and agricultural sustainable development. Crop yield estimation by remote sensing and crop growth simulation models have highly potential application in crop growth monitoring and yield forecasting. However, both of them has limitations in mechanism or regional application respectively. Approach and scientific foundation study on combination of remote sensing and crop growth simulation models are being explored against opportunities and limitations of them in the project.In this paper, a winter wheat growth model was developed in North China based on adjustment of crop model from foreign; by coupling canopy radiative transfer model with crop model, combination method of remote sensing information with crop model in potential production level was studied; through assimilating remote sensing informations, crop model was optimized by re-estimating it's parameters and initial conditions; regional remote-sensing-crop-simulation-framework-model(WSPFRS) was established and it's application was studied. This research is a foudation of developing crop model in water stress production level based on remote sensing data. The main outcomes in this study are as fowllows:(l)Winter wheat variety inheritance parameters related to growth and development in WOFOST were adjusted by using field experiment data in different climate ecological regions. Adjusted WOFOST could simulate well winter wheat development period in North China using re-estimated parameters relevant to temperature and photoperiod. When some of growth parameters, such as specific leaf area, leaf senescence index, partitioning coefficients, maximum photosynthesis rate were adjusted, the goodness of fit value of simulations result of crop model is between 0.11 with 1.51. Taking the low temperature threshold of parameter TMNF (assimilate rate was effected by night low temperature) as index of winter wheat beginning date of overwinter and turn-green, WOFOST could simulate well overwinter course ofwinter wheat. By re-estimating biomass, the average of the goodness of fit value (Q )increases between 0.017 to 0.1528. Adjusted WOFOST simulate well growth and development stage of winter wheat in North China, and this is a good foundation for regional application.(2)Through a canopy state variable (LAI), crop model(WOFOST) adjusted by winter wheat in North China and canopy radiative transfer model(SAIL-PROSPECT) tested by observed data were coupled. Some parameters in crop model, such as emergence date and biomass in turn-green were adjusted by minimizing difference of between SAVI simulated by couple model with SAVI from remote sensing by FSEOPT program. And therefore the crop model was optimized. It shows in this research that remote sensing data during period between jointing stage to earing stage is more critical when biomass in turn-green were re-estimated.(3)Weather data and some crop parameters related to temprature were interpolated by using spatial interpolation method IDW in geo-stastics. Some winter wheat varity parameters were regionized simply according to it's climate ecologicl type. Matching method of remote sensing data, crop and weather data were studied in this paper. By using remote sesing data, weather forcing variables and crop parameters in spatial grid, regional remote-sensing-crop-simulation-framework-model (WSPFRS) was developed in terms of method of coupling remote sensing data with crop model, and it's application software system were programed. A case which utilized remote sensing data to adjusted winter wheat emergence date and biomass in turn-green were tested in North China in 2002-2003. And mapping of simulation result using GIS shows that simulation results in emergence date, anthisis date, muturation date and biomass accumulation process were close to measured values.Methodology which crop growth, development and yield formation in regional scale simulated by combining remotely-sensed information with crop model was studied and some good results...