<p>Accurate forecasting of soil moisture can play a vital role in hydrological monitoring, agricultural planning and water resource management, especially in areas with high seasonal variability and non-homogeneous climate. This paper suggests a Decomposition-Guided Forecasting Framework of Soil Moisture (DGF-SM) which incorporates satellite-based measurements combined with time-series decomposition and statistical predictions. This was based on extracting monthly soil moisture data of Soil Moisture Active Passive (SMAP) mission in India, Bangladesh, Nepal, and Pakistan using Google Earth Engine and examined between 2016 and 2021. The use of Seasonal Trend Decomposition by Loess (STL) was used to isolate trend, seasonal and residual information and it confirmed the additive seasonal behavior in all regions. The decomposed trend component was forecasted on the basis of ARIMA models, and the ARIMA (4,0,3) model was chosen based on information-theoretic considerations. The suggested scheme has a high predictive accuracy, 96.9% explained variance in India, and shows a steady performance when used on opposite climatic regimes. An additional evaluation, a rolling-horizon, confirms that there is stable generalization behavior in unsteady data availability. Comparison shows a strong seasonal control in the monsoon-based areas and a weak variation in dry areas. The findings show that decomposition-based modeling has greater forecasts and interpretability and can be used in operational soil moisture monitoring and decision-support systems.</p> Graphical Abstract <p></p> <p>This graphical abstract illustrates the proposed Decomposition-Guided Forecasting Framework for Soil Moisture (DGF-SM), integrating satellite observations, spatiotemporal analysis, and robust forecasting. The framework begins with the ingestion of SMAP satellite-derived soil moisture data, followed by spatial aggregation, quality control, and preprocessing to generate region-level time series. A dedicated decomposition layer separates the signal into trend, seasonal, and residual components, enabling targeted modeling of distinct temporal behaviors. Component-wise forecasting is performed using statistical and benchmark learning modules, and forecasts are reconstructed through a hybrid fusion strategy. Model performance is continuously assessed using a rolling-horizon evaluation mechanism to ensure temporal robustness under evolving data availability. The spatial visualization highlights SMAP-derived soil moisture distribution over India, demonstrating large-scale heterogeneity captured through Google Earth Engine. The temporal plot presents monthly soil moisture dynamics across India, Bangladesh, Nepal, and Pakistan, revealing strong seasonal dominance in monsoon-driven regions and subdued variability in arid zones. Together, the graphical abstract conveys how decomposition-guided modeling enhances interpretability, generalization, and forecasting reliability for satellite-based soil moisture monitoring.</p>