###### Abstract  
Soil moisture (SM) is an important biophysical parameter by which to evaluate water resource potential, especially for agricultural activities under the pressure of global warming. The recent advancements in different types of satellite imagery coupled with deep learning-based frameworks have opened the door for large-scale SM estimation. In this research, high spatial resolution Sentinel-1 (S1) backscatter data and high temporal resolution soil moisture active passive (SMAP) SM data were combined to create short-term SM predictions that can accommodate agricultural activities in the field scale. We created a deep learning model to forecast the daily SM values by using time series of climate and radar satellite data along with the soil type and topographic data. The model was trained with static and dynamic features that influence SM retrieval. Although the topography and soil texture data were taken as stationary, SMAP SM data and Sentinel-1 (S1) backscatter coefficients, including their ratios, and climate data were fed to the model as dynamic features. As a target data to train the model, we used in situ measurements acquired from the International Soil Moisture Network (ISMN). We employed a deep learning framework based on long short-term memory (LSTM) architecture with two hidden layers that have 32 unit sizes and a fully connected layer. The accuracy of the optimized LSTM model was found to be effective for SM prediction with the coefficient of determination (\(R^{2}\)) of 0.87, root mean square error (RMSE) of 0.046, unbiased root mean square error (ubRMSE) of 0.045, and mean absolute error (MAE) of 0.033. The model's performance was also evaluated concerning above-ground biomass, land cover classes, soil texture variations, and climate classes. The model prediction ability was lower in areas with high normalized difference vegetation index (NDVI) values. Moreover, the model can better predict in dry climate areas, such as arid and semi-arid climates, where precipitation is relatively low. The daily prediction of SM values based on microwave remote sensing data and geophysical features was successfully achieved by using an LSTM framework to assist various studies, such as hydrology and agriculture. soil moisture; time series analysis; long short-term memory; ISMN; Sentinel-1; SMAP 20222022022022022022022022022022020220202202022020220202202022020220202202022020202202022020202202022020220202202022020202022020220202020202020220202020202020202022020  
The dynamics of SM are influenced by the physical properties of topography and soil as well as temporal changes in atmospheric conditions. The impact of these parameters on the variability of SM has been studied in depth concerning topographic data [8; 9; 10; 11], soil texture [12; 13], and climate variables [14; 15; 16]. In general, the prediction of SM in local studies (e.g., station-based SM forecasting) does not require static parameters, such as topography and soil texture, because these data vary insignificantly. However, the variability of SM in time depends on climate data in both local, regional, or global scale studies. In the literature, researchers focused on minimizing the prediction uncertainties to estimate SM by using in situ measurements [17; 18; 19; 20; 21]. Including the meteorological parameters in estimating SM enhances the prediction accuracy significantly. The study conducted in [18] predicted the SM values of five stations located in Shandong Province of China by using varying depth measurements of SM together with meteorological variables. A similar study was performed in [19], extending the spatial distribution of stations worldwide, to forecast the SM values. In this study, however, the time series of each station were trained and validated separately. Another study carried out by [20] used the SM values of globally distributed stations of the International Soil Moisture Network (ISMN) coupled with climate, topography, and soil texture data to create a model for the daily prediction of SM in different depth layers.