Response of Rice Ecological Indicators to Water Consumption Based on Multi-source Data in Irrigation District Scale

<p>The study of law of crop water consumption in small scale such as irrigation area requires remote sensing image data with high spatial and temporal resolution, however, remote sensing images that possess both high temporal and spatial resolution cannot be obtained for technical reasons. To solve the problem, 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 model (VISTFM), the Landsat8 OLI and MOD09GA data are combined to generate high spatial and temporal resolution reflectance data and the landsat8 OLI, MOD09GA and MOD13Q1 data are combined to generate high spatial and temporal resolution normalized vegetation index (NDVI) and enhanced vegetation index (EVI) data.</p><p>The rice area is mapped 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.</p>