Estimation of the yield and green water footprint of rainfed wheat based on remote sensing and machine learning

In this research, the relationship between remote sensing drought indices NDVI, EVI, SAVI, and LAI with the yield and green water footprint (WF) of rainfed wheat in 5 fields in Saqqez City (2001–2020) was investigated using multivariate regression (MR), random forest (RF), and support vector regression (SVR) methods. Wheat yield of the fields)2001–2020) was simulated with the AquaCrop model. The results showed a high coefficient of determination (R²) (R² = 0.97) between the yield simulated by the AquaCrop model and the observed yield of the fields. The high Nash–Sutcliffe efficiency (NSE) (0.86) and a small amount of underestimation in the calibration step showed the model has a suitable estimation. Results showed that in the simulation of the yield of rainfed wheat, the RF method had a high correlation, NSE was close to one, and root mean square error (RMSE) was less than 0.2 (ton/ha) and had good accuracy. The relationship between the remote sensing drought indices and the green WF of rainfed wheat, as shown by the results, is that the R² varies between 0.87 and 0.73. The RMSE was between 0.13 and 0.1 (m³/ton) in different testing steps and the NSE was close to one. The relationship between WF climate variables and yield was examined. Results showed evapotranspiration (ET) and maximum temperature (Tmax) directly affected the green WF of rainfed wheat. The results showed that the RF method had a good estimate of the green WF of rainfed wheat. There is a significant relationship between the remote sensing drought indices and the green WF of rainfed wheat in the study area.