Modeling the Amount of Water in Soil using Satellite Image Information and Infiltration Flux to Estimate Precipitation and Irrigation Water Data

Precipitation is one of the essential parameters of the water cycle, the estimation of which is effective in water and soil resources management. In this study, the SM2RAIN-NWF algorithm was used to estimate irrigation water consumption at the field scale based on satellite soil moisture data. Satellite soil moisture observations obtained from Advanced Microwave Scanning Radiometer (AMSR2) along with GLEAM products and rainfall were used for the period of 2012-2020 as model inputs. Precipitation estimation was performed for three different sites including an agricultural land in Miandoab, a vegetated surface in Malekan, and a barren land in Bonab. Using this model, the coefficient of determination (R2) of precipitation estimation was between 0.53 and 0.70. A comparison of the results revealed that the model exerted much better results in regions with no vegetation. The results of irrigation estimation in Miandoab plain showed that although the model systematically over/under-estimated irrigation data in some seasons compared to the in-situ data, the average performance of the model in irrigated regions (NS = 0.55, R2 = 0.63, and PRMSE = 2.48%) proved that the proposed approach can provide a suitable prediction of irrigation pattern.