Due to different climatic conditions and the high cost of devices, monitoring plant growth is complex and costly. Remote sensing of agricultural lands, especially on a large scale, using satellite imagery, is more appropriate than hardware equipment such as sensors. One of the main challenges in plant growth is the rate of plant evapotranspiration discussed in the FAO-56 paper. The rate of plant evapotranspiration in the initial, development, and late growth stages is an important issue. Factors such as irrigation method, surface albedo, height above sea level, aerodynamic conditions, leaf and stomata characteristics between plants, and soil texture, have made it difficult to study plant growth from placement to harvest. In this research using Landsat 8 satellite imagery and vegetation indices, we present an approach in which exist several important indicators for sensing plant growth, determining soil texture and moisture, land slope, and improving cloud effects in remote sensing. The water required by the plant at different stages of its growth and the equations in FAO-56 paper can be estimated and improved using the indices. We have used cubic spline interpolation, Pearson correlation coefficient, explanation coefficient (R²), Root Mean Square Error (RMSE), Mean Absolute Deviation (MAD), and the Mean Standard Error (MSE) to evaluate and compare the accuracy of each index. Finally, the correlation equation of each index is calculated with its actual values. We have also used hypothesis and regression analysis testing to prove efficiency and predict crop behavior.