Standard aridity indices for agricultural monitoring usually treat potential evapotranspiration (PET) and total precipitation as static, ignoring vegetation's dynamic biophysical responses to climate change. This often leads to the aridity paradox and a misrepresentation of green water.To bridge this gap, a novel Hydro-Physiological Green Aridity Index (HPGAI) was developed by revisiting the Standard UNEP Aridity Index (SAI). This framework introduces a twofold innovation: a) replacing total precipitation with Effective Precipitation (Peff) to account for root-zone water recharge, and b) integrating a dynamic surface resistance model into the FAO PenmanMonteith equation to capture stomatal closure responses to rising CO2 and Vapor Pressure Deficit (VPD). The performance of HPGAI was rigorously evaluated against stepwise scenarios (SAI, Hydrological-only (HGAI), and Physiological-only (PGAI)) across 40 synoptic stations in Iran (1967-2024). A dual-proxy validation utilizing Actual Evapotranspiration (AET) and Normalized Difference Vegetation Index (NDVI) was conducted using Pearson correlation and information-theoretic analysis (KNN-based Mutual Information with bootstrap uncertainty quantification). Results revealed that the fully coupled HPGAI consistently outperformed the standard index, achieving a Relative Improvement Rate (RIR) of up to 29% in annual NDVI correlation. The hydrological refinement unmasked chronic water deficits in humid regions, while the physiological feedback mitigated thermodynamic aridity overestimations in arid zones. Furthermore, MI analysis demonstrated that HPGAI captures non-linear vegetation responses that linear metrics miss. These findings indicate that integrating plant physiological feedback and effective rainfall into aridity metrics is essential for realistic assessment of agricultural water stress, encompassing both root-zone moisture deficits and atmospheric demand.