Advances in soil sensors, remote sensing, and forecasting technologies have expanded the data types and integration options for irrigation optimization. This data includes in-situ measurements, satellite-derived products, weather forecasts, hydrological, and machine learning models. However, there is still limited operational guidance on translating soil moisture data into actionable decisions. This paper reviews recent literature and discusses advancements in soil moisture-driven irrigation systems, focusing on how soil moisture information is translated and embedded in decision-support frameworks. Studies show that irrigation efficiency depends on how crop root zones are defined, how moisture is converted into crop-relevant indicators, and how information from multiple depths is synthesized. However, the approaches to this vary widely. Fixed-depth, dynamic, and root-weighted root-zone representations coexist, each balancing accuracy with practical constraints. Similarly, irrigation frameworks range from reactive, sensor-based systems to forecast-informed and hybrid architectures. Each framework reflects different trade-offs between complexity and reliability. The review showed that increasing data integration alone does not necessarily guarantee better information for irrigation decisions. However, improving irrigation support systems requires shifting the emphasis from precision in soil moisture estimation to the transparency and interpretability of irrigation decision logic.
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