| Crop growth monitoring by remote sensing has important application value in the efficient quantitative evaluation of crop distribution and growth, and in providing decision support for management measures and macro policy formulation. The traditional satellite remote sensing monitoring has low spatial and temporal resolution and poor accuracy. The rapid development of UAV remote sensing in recent years can make up for the shortcomings of satellite remote sensing, but the satellite aircraft coordinated remote sensing monitoring method needs to be further improved. At the field scale, this study set up wheat nitrogen and irrigation gradient tests, measured SPAD, LAI and other growth indicators at each key growth period, obtained UAV remote sensing data, and built a wheat growth multi spectral monitoring model; The acquisition model is scaled up using the optimized band ratio correction method combined with Sentinel-2A image. The results showed that at jointing stage, booting stage, flowering stage and filling stage, the cubic function, exponential function, exponential function and power function constructed by Clgreen, Clrededge, OSAVI and OSAVI respectively were the best for SPAD fitting, and the accuracy of verification was better when scale up was applied to booting stage, flowering stage and filling stage; The power function, quadratic function, exponential function and exponential function constructed by Clgreen, Clrededge, DATT and OSAVI in the above four growth periods are the best for LAI fitting, and the accuracy of scaling up application verification is good. According to the monitoring of the growth of winter wheat in Xianyang City based on the satellite computer coordination method, in 2021, the growth of wheat in Wugong County, Xingping City and Sanyuan County will be better, while that in Yongshou County, Chunhua County and Binzhou City will be worse. This study improved the fusion method of UAV and satellite remote sensing images, and constructed the technical process of satellite aircraft cooperative monitoring of winter wheat growth. The results can provide technical and data support for the accurate monitoring of the growth of winter wheat in different growth periods and the formulation of macro production policies in Xianyang City.