The Study On The Method Of Rice Yield Estimation Using Statistical And MODIS Data

China is a large agricultural country,rice area and total output occupies second and first place in the world,respectively.All levels Chinese government and social public pay high attention to the variation of rice area and yield,which information is the very important for food economic policy decision.Therefore,it is very essential to know about the rice area and yield in time.Chinese Bureau of Statistics undertakes the nationwide rice yield investigation.After several decades'effort,a statistic and investigation system has been formed.However,with the development of social economy,government decision department,social public and some other statistic service objects have increased their demands for the statistic results,as well as other problems and pressures,it is urgent to improve the traditional investigating method using the advanced technologies.3S technologies,especially remote sensing,features the large area coverage,in-time, objectivity and so on,provide very good tools for improving the traditional statistic method.Remote sensing rice yield estimation has been studied for several years. However,with the new satellite data and technologies updated,the study of rice yield estimation approach using the integrated temporal and spatial resolution remote sensing data is very helpful to implement the rice area and yield estimation on vocational operation.Hunan province was selected as the study area in this thesis,where the rice area and total yield occupy the first place in China,and features the typical topography and rice cropping system.After rice yield estimation division and rice detection,the purpose of this study is to build different models and estimate rice yield using different MODIS products(MOD09A1,MOD09GA,MYD09GA,MOD13Q1,MYD13Q1,MOD15A2, MOD17A2,MOD17A3) at the different level.The summary of the major chapters in this thesis as follows:1 The study of rice yield estimation division based on spatial analysis and two-dimensional optimal tree clusters in Hunan provinceRice planting system,unit yield of rice,the ratio between rice and total land area,and the ratio between plain and total land area were selected as the main factors for rice yield estimation division.Two first-grade zoning,and five-grade zoning in Hunan province were got based on the integration of spatial analysis and two-dimensional optimal tree clusters,2 Detection and estimation of paddy rice area based on rice typical phenology spectral characteristics and multi temporal MODIS data in Hunan provinceThe periods of rice transplanting and heading were selected as the key stages,the pixel of MOD09A1with clould contamination was filled by QA information and adjacent maximum fitting method.Using the relationship between EVI and LSWI during the transplanting and heading periods,non-paddy rice plant and other disturbing factors were removed,the early,late and singlerice from 2000 to 2008 were extracted.Our results were validated with finer resolution(2.5m) Satellite Pour l'Observation de la Terre 5 High Resolution Geometric(SPOT 5 HRG) data,land-use data at the scale of 1/10000 and county-level statistical rice area.The results showed that three paddy rice crop patterns could be discriminated and their spatial distribution quantified.We show the area of single crop rice have increased annually and almost doubling in extent from 2000 to 2008,but unique declines in the extent of early and late paddy rice.These results were more accurate than previous satellite-based methods.3 Provincial rice total yield estimation based on the results of division and rice detection using remote sensing and county level statistic dataBased on rice yield estimation division and rice area detection,MOD09A1 EVI corresponding the rice area detection from 2000 to 2008 was extracted using the administrative map at the scale of 1/250 000 at the county level.Transplanting,booting, heading,milky and harvest stage were selected as the key periods for rice yield estimation,EVI multiplied by county rice area and county rice total yield were took as the independent and dependent variables respectively,and then different models were made based on division results and not using MODIS and statistics data from 2000 to 2007.The optimal rice yield estimation models were selected after errors analysis,and then the rice forecasting yield in 2008 were obtained.The results showed that the accuracy of rice yield estimation based on division was better than without division,and almost all optimal models focused on the period from booting to milky,and the quadratic nonlinear and stepwise models are better than linear models.The estimation yield had a significant positive correlation with statistical data,and the relative errors for rice estimation and forecasting models were less than 5%.4 Provincial rice unit yield estimation using MOD13Q1,MYD13Q1 EVI and plot level measured dataOne of the very important sources for statistic data for Chinese Statistics Bureau is the measured data from sampling plot.3×3 window size MOD13Q1 and MYD13Q1 combined EVI corresponding with those plots in Hunan province were extracted. Transplanting,booting,heading,milky and harvest stage were selected as the key periods for rice yield estimation,and the different type models were built.The optimal rice yield estimation models were selected after errors analysis,and then the rice forecasting yield were obtained.According to the results of rice detection,the provincial rice total yield was obtained.Results showed that the relative errors of forecasting rice yield and were less than 5%.5 County rice yield estimation using MOD13Q1,MYD13Q1 EVI and township level statistic dataAccording to remote sensing rice yield estimation based on county level statistic data, one mountain county(Liling) with a large error was selected to further analysis the rice yield estimation using township level statistic data.MOD13Q1 and MYD13Q1 combined EVI corresponding with the rice area based on 1/10000 scale land-use data was extracted. Transplanting,booting,heading,milky and harvest stage were selected as the key periods for rice yield estimation,EVI multiplied by township rice area and township rice total yield were took as the independent and dependent variables,and then different type models were built.The optimal rice yield estimation models were selected after errors analysis,and then the rice forecasting yield were obtained.The results show that the relative errors for rice estimation and forecasting models is less than 0.1%and5%, respectively.6 Rice yield estimation based on MODIS GPP/NPP data at the pixel level,and the impact of the rice area percentage in one pixel on the accuracy of rice yield estimationBased on previous researches,algorithms for rice yield estimation using MODIS GPP/NPP data were studied.8-day NPP was calculated using 8-day LAI,PSNnet data and MODIS17 user's guide,and then rice yield estimation was performed using 8-day MODIS GPP/NPP and rice growth period data.We used MODIS GPP image to create 1×1km grid,and calculated the percentage of paddy rice area in each grid,and then the impact of different percentage of paddy rice area in each grid on rice yield estimation result was studied.The result showed that:there is a significant positive correlation between 8-day estimation NPP results and MODIS annual NPP product.(P＜0.01),and the rice yield estimation based on NPP data was low compared to statistic data,but the results of MODIS GPP is better.The relative errors between rice yield from estimation and statistic data decrease with the percentage of rice area in the 1×1km increasing,and they are less than 20%,even 10%when the percentage is larger than 80%According to the above researches,the main conclusions are as follows:the relative error of the rice yield estimation based on county,township level statistic data,plot measured data,and MODIS GPP was less than 5%.However,for the sake of improving rice yield estimation accuracy,the models based on the integration of Terra and Aqua data at the township level are the best choice.Considering rice area and yield estimation on vocational operation,the methods based on the integration of Terra and Aqua data and measured data are the best choice.If the plot measured data aren't available,the rice yield estimation methods,with the support of zoning and rice area detection,and based on the county level statistic data are perfect.For the large paddy rice area,the rice yield estimation algorithms based on MODIS GPP/NPP at the level of pixel are dependable.