eng Enviro Research Publishers Current Agriculture Research Journal 2347-4688 2321-9971 2026-09-10 14 2 305 315 27440 article Bhavya Chandrasan 1 Solomon Jebaraj 2 Department of CSIT, Centre for Research Excellence and Innovation, JAIN (Deemed-to-be University), Bengaluru, Karnataka, India. Department of CSIT, JAIN (Deemed-to-be University), Bengaluru, Karnataka, India. Proper estimation of Root-Zone Soil Moisture (RZSM) plays a vital role in effective irrigation management in rain-fed agriculture systems in drylands. Ragi (Eleusine coracana G.) is an important cereal grain crop grown in various lateritic districts of Karnataka, which is very sensitive to moisture stress during the crucial growth stages. However, there is no free-access Decision Support System (DSS) tool available for irrigating ragi. This systematic literature review study done following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and involving many peer-reviewed articles published between 2018 and 2025 from Google Scholar and IEEE Xplore provides a summary of various satellite-based and machine learning (ML) techniques to estimate RZSM, its relevance for Karnataka's semi-arid agro-climatic condition, and highlights five operational research gaps. Literature suggests that the ensemble ML models (Random Forest,eXtreme Gradient Boosting (XGBoost), LightGBM) using the fusion of multi-source satellite inputs (Soil Moisture Active Passive(SMAP), Sentinel-1/2, ERA5-Land, Climate Hazards Group InfraRed Precipitation with Station data(CHIRPS), SoilGrids) reliably predict the soil moisture with R² > 0.85. A well-designed research approach is proposed to fill the researchtopractice gap in estimating RZSM and ragi irrigation advice from free remote sensing data. https://www.agriculturejournal.org/volume14number2/towards-an-irrigation-decision-support-system-for-ragi-in-karnataka-a-systematic-review-of-satellite-based-root-zone-soil-moisture-estimation/ Decision Support Systems; Karnataka; Machine Learning; Ragi Irrigation; Remote Sensing; Root-Zone Soil Moisture; Sentinel-1; Soil Moisture Active Passive