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Soil moisture is one of the important climate variables and it plays a great role in global climate because it is one of the significant components in the hydrological cycle for weather conditions. Moreover, it helps to better understand, estimate, and predict the drought, and the flooding. Soil moisture influences crop yield, and irrigation planning. Given that importance, it is essential to study the availability and sources of the soil moisture datasets. Soil moisture datasets can be found from many sources such as in-situ, model-derived, and satellite-derived. However, every data source has some limitations and more specifically these products have a coarse spatial resolution. The purpose of this study is to generate higher resolution of soil moisture products by implementing artificial intelligence (AI) techniques. Here we will apply machine learning (ML) methods and deep learning (DL) methods to generate field-scale soil …