Soil moisture is of particular importance in the study of water resources and agriculture. The microwave electromagnetic spectrum does not have the physical limitations of other radiometric spectral in measuring soil moisture, but they often have very large pixel dimensions (more than 10 km). In this study, in order to apply soil moisture remote sensing data at the farm scale, using field soil moisture measurement data in the Nishabour plain during 2017-2019, calibration of AMSR2 retrieval data was performed. It turned out that altitude and vegetation changes are among the key factors affecting calibration accuracy. Based on the theory of thermal inertia and with the help of MODIS sensor images, the interactions of the average daily soil moisture and the daily surface temperature difference between the two MODIS and AMSR2 sensors were investigated. Using it, downscaling linear relationships of soil moisture images were obtained to convert the image dimensions from 25 km to 1000 m. Validation of R2 statistical indices with a minimum of 0. 73 and a maximum of 0. 84, MAE and RMSE with a range of 1. 6 to 4, showed that the algorithm used in the downscaling is well able to reflect the interactions between rainfall, soil moisture, vegetation and changes in canopy temperature profile and this feature reinforces its application in agrometeorological studies.