Wheat And Maize Yield Model Estimation Based On MODIS And Meteorological Data In Shaanxi Province

Wheat and maize are main crops in China. The production of them in china ranks first and second position respectively in the world. With the population increasing and urbanization in recent years, the quantity and quality of arable land have been declining, and food production has been a hot spot. Remote sensing has been applied for disaster monitoring and evaluation, resource survey, agricultural production, etc. successfully. Yield estimation is one of important applications of remote sensing, the information of crop growth and production could be extracted by the creation of relationship between growth indicators and remote sensing information. This kind of information could be useful for government to crop production.Shaanxi province is one of the main grain-production districts in northwest China. Crop production of Shaanxi is unstable due to its complicated geographic, climate condition and weak agricultural basis. Therefore, it is important to acquire the status of crop production in time. MODIS is a good resource for yield extimation in large area due to its abundant spectral information, acquired frequntly and free fee.In this thesis, the values of NDVI, LAI, GPP of wheat and corn were extracted by Arcgis, from2007to2011. And yield estimation models of wheat and corn with MODIS products were created based on the statistical analysis in SPSS. Meteorological factors, sunshine, average temperature, rainfall, were analysed with yield and models were created. And meteorological factors and remote sensing information were analysed with yield together and prediction models were created too.The results showed that the accuracy of model based on remote sensing variables was better than the model with meteorological variables for wheat. And for corn, the model with meteorological variables was better than models with remote sensing variables. And the models with both of meteorological and remote sensing variables were the best, the accuracy for wheat and maize were91.5%and88.8%respectively. Thus, it is necessary to include both of remote sensing and meteorological variables while modeling.