The Soil Moisture Active Passive (SMAP) mission has delivered high-quality and valuable sensing of surface soil moisture since 2015. However, its short time span, coarse resolution, and irregular revisit schedule have limited its use. Utilizing a state-of-the-art deep-in-time neural network, Long Short-Term Memory (LSTM), we created a system that predicts SMAP level-3 soil moisture data using climate forcing, model-simulated moisture, and static physical attributes as inputs. The system removes most of the bias with model simulations and also improves predicted moisture climatology, achieving a testing accuracy of 0.025 to 0.03 in most parts of Continental United States (CONUS). As the first application of LSTM in hydrology, we show that it is more robust than simpler methods in either temporal or spatial extrapolation tests. We also discuss roles of different predictors, the effectiveness of regularization algorithms and impacts of training strategies. With high fidelity to SMAP products, our data can aid various applications including data assimilation, weather forecasting, and soil moisture hindcasting.