Estimating effective precipitation using remote sensing and its modeling with meteorological variables under commonly used learning algorithms and FFNN

Precipitation is considered one of the most important components of hydrological cycle, and its effective and usable amount for plants is of great importance in the agricultural sector, especially rainfed cultivation. In this research, the effective precipitation (EP) in dry wheat fields of Khomein city was estimated by using RS and SEBAL on 28 available images from Landsat8 in the crop years 2014 to 2022. Penman-Monteith-Fao method was used to evaluate the accuracy of SEBAL. Then, a model of EP estimation was developed with ANN and meteorological data. For this purpose, the correlation between meteorological data and Growin Degree Days (GDD) with EP was investigated by Pearson's correlation method. the meteorological data of three stations from the closest synoptic stations to the study area were used and The meteorological data of the study area were interpolated using the Inverse Distance Weighting method (IDW). According to the results of the correlations, the average temperature parameter with a correlation of 0.92 and the GDD and the maximum relative humidity respectively with a correlation of 0.86 and -0.77 as effective variables in estimating EP. In the next step, the most effective parameters were used for modeling. the networks were trained under different scenarios, and the performance of the networks was evaluated using the RMSE and MBE error criteria. The results showed that by using the BR learning algorithm and having the variables of daily temperature and GDD, it is possible to predict the amount of EP for the target area with very good accuracy. The RMSE value of this model was 0.1899 mm and MBE was estimated as -0.0115 mm. By using the presented model, with simple meteorological variables, the actual evapotranspiration and finally the EP of the desired area can be determined with appropriate accuracy without the need to solve complex algorithms.