Response Of Rice Ecological Indicators To Water Consumption Based On Multi-source Data In Irrigation District Scale

China is a large agricultural country with large agricultural water cons umption,uneven distribution of agricultural water resources in time and space,and low utilization efficiency of water.Rice is the main irrigated crop in China that needs to consume a large amount of water during its growth period.It's increasingly important to increase water use efficiency and reduce water consumption at present with the water resource problem such as water pollution,increasing in water use outstanding.The issue of agricultural water resources,one of the main water resource problem,is a spatial issue as the water planning,allocating,evaluating and managing are carried on a spatial scale.So it is of great significance to research the water consumption rules and water productivity of crops on the spatial scale to optimize the allocation and evaluation of water resources.However,the traditional research on crop water consumption and water productivity is based on point scale experiment.The surface of the earth is a highly complex system and the law of one scale is difficult to apply to other scales,therefore,the law obtained by point scale is difficult to directly extend to Regional scale.In order to overcome the limitations of the point scale experiments,the paper takes remote sensing technology as the means,and takes the Heping irrigation area of Suihua City in Heilongjiang Province as the research object,and reference the research achievements of traditional crop water consumption law to study the spatial water consumption law of rice.The study of crop water consumption law in small area such as irrigation area requires remote sensing image data with high spatial and temporal resolution,however,there is an irreconcilable contradiction between the commonly used image data,that is,spatial resolution of images with higher temporal resolution is lower,and vice versa.To solve this contradiction,this paper present a multisource remote sensing data spatial and temporal reflectance fusion method based on fuzzy C clustering model(FCMSTRFM)and multisource Vegetation index(VI)data spatial and temporal fusion method(VISTFM),the Landsat8 OLI and MOD09 GA data are combined to generate high spatial and temporal resolution reflectance data and the landsat8 OLI,MOD09 GA and MOD13Q1 data are combined to generate high spatial and temporal resolution normalized vegetation index(NDVI)and enhanced vegetation index(EVI)data.The rice planting area extraction by spectral correlation similarity(SCS)between standard series EVI curve that based the EVI generated by VISTFM and average value of each EVI class that generated by classing Multiphase EVI into several class,the extraction results are verified by two methods: ground sample and Google Earth image.high spatial and temporal resolution Leaf area index(LAI)that covered the mainly rice growth and development stages is generated by higher precision method between artificial neural network and equation fitting that establish the relationship between NDVI,EVI and LAI.The yield of rice in the spatial scale is generated by establishing the relationship between yield and LAI of the mainly growth and development stages that has the maximum correlation with yield.Daily high spatial resolution evapotranspiration is generated by using multisource remote sensing data spatial and temporal reflectance fusion method to fusion the MODIS-like scale and Landsat-like scale evapotranspiration that generated by The Surface Energy Balance Algorithm for Land(SEBAL).Based on the data,the evapotranspiration,LAI and yield of rice,obtained by remote sensing methods,rice water growth function is established by Jensen,Blank,Stewart and Singh model.The results show that the average value of R,RMSE,E RGAS and variance for reflectance data generated by FCMSTRFM are 0.78807,0.0277,1.6623 and 0.01882,respectively,the average value of R,RMSE,ERGAS and AD for VI data generated by VISTFM are 0.9057,0.0674,1.9795,and 0.0045,respectively,the precision of VI and reflectance data can be used for rice research.The correlation coefficient between the LAI that inversed by the remote sensing and measured by ground sample point is 0.7882,and the correlation coefficient between the yield that inversed by the remote sensing and measured by ground sample point is 0.6855.The results of Google Earth and ground sample verification showed that the accuracy of rice area extraction was 0.94 and 0.92,respectively.For the Heping irrigation area,the optimal water consumption growth function model is the Jensen model between the model of Jensen,Blank,Stewart and Singh.