Soil moisture is crucial for agriculture and hydrology, but its accurate prediction is challenging due to inadequate representation of various complex land surface processes and meteorological influences. In this research, we employ the Long Short-Term Memory (LSTM) framework, a specific architecture of deep learning networks that is effective in processing time series data, for predicting soil moisture. We have developed the Next Generation Interactive Soil Moisture Forecasting System to advance skillful soil moisture predictions at sub-seasonal timescales by leveraging advanced analytics and deep learning, with LSTM at its core. We combined the state-of-the-art climate model's (Community Earth System Model Version 2) forecast that incorporates the effects of the large-scale climatic drivers, including sea-surface temperature and atmosphere circulation features into soil water forecast with the LSTM-based Deep Learning model. Our Deep Learning model understands the local forecast biases using the weekly hindcast data from 1999 to 2016. We used this trained LSTM model to test its performance from 2017 to 2021 and enhanced the forecast proficiency and aid in analyzing future soil moisture anomalies, i.e., departure from climatology using data fusion and spatial downscaling. For performance assessment, optimal metrics include Mean Absolute Error (MAE) values near 0 (0-0.6), Root Mean Square Error (RMSE) around 0.5, and Anomaly Correlation Coefficient (ACC) nearing 1. These breakthroughs in system design and modeling facilitate improved soil moisture prediction, benefiting water management and our understanding of land-atmosphere interactions.
