A new approach for estimating plant growth and determining water requirement using remote sensing

Due to different climatic conditions and the high cost of devices, monitoring plant growth is complex and costly. Remote sensing of agricultural lands 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, and soil texture are main to review 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. The water required by the plant at different stages of growth and the equations in the FAO-56 paper estimate and improve using these 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.

The results show that the proposed approach by evaluating spatial and temporal data in different climates leads to greater efficiency and crop. The study can consider important parameters in precision agriculture to achieve efficiency, productivity, quality, profitability, and sustainability of crops.

Graphical Abstract