Abstract: Water demand prediction is often required for high accuracy and operational efficiency. However, the conventional model is still lacking in the dynamic adjustment under various scenarios. Particularly, there are the ever-increasing challenges of water management in sustainable agriculture under increasingly variable and unpredictable climates. This research aims to systematically integrate the critical agrometeorological parameters, with particular emphasis on the reference evapotranspiration (ET₀) and dynamic crop coefficients (K_c). A crop water demand prediction was developed and then validated using remote sensing and the Penman formula, termed the Remote Sensing Inversion-Time Series Decomposition and Linear Regression-Irrigation Water Requirement Assessment Model (RSI-SDLR-IWRAM). The framework was then designed with both precision and practicality in mind. A structured multi-stage computational architecture was first employed using the advanced Sequence Decomposition-Linear Regression (SDLR) algorithm in conjunction with the physically-based and theoretically robust Penman-Monteith equation. The high-accuracy 1-7 day-ahead forecasts of the reference evapotranspiration were generated systematically. Subsequently, the geospatial processing was incorporated using the widely used ArcGIS platform. Agricultural field boundaries were precisely delineated and digitally characterized to obtain the vector-based parcel maps. The fundamental spatial units served as the geospatial foundation for the subsequent analysis. The Remote Sensing Inversion (RSI) component was dynamically derived from the near-real-time crop coefficient values using multi-temporal spectral imagery. Furthermore, the complex spatiotemporal variability of the crop growth was effectively captured and then quantified over the different agricultural field parcels. The IWRAM module was then integrated with these diverse meteorological and biophysical inputs using advanced data assimilation techniques. Field-specific irrigation water requirement was estimated to characterize the unprecedented levels of both spatial and temporal resolution. Validation experiments were conducted during multiple growing seasons. The results demonstrated that there was the superior performance of the integrated system: 1) The SDLR prediction module was consistently achieved in the remarkable accuracy over all forecasting horizons (1-7 days), with the root mean squared error metrics ranging between 0.435-0.782 mm/day, mean absolute errors within 0.303-0.526 mm/day bounds, and coefficient of determination values in the range of 0.821-0.942, all of which outperform the baseline models; 2) Computational efficiency analysis revealed that, compared with the two baseline models, theSDLR model reduces the number of parameters by 90.7% and 92%, and the inference timeby 97.4% and 98.5%, respectively. Thereby, the highly efficient deployment was realized near real-time operation in actual agricultural settings; 3) The RSI-based Kc estimation subsystem successfully reproduced the characteristic temporal trajectories, which were documented in the authoritative FAO-56 guidelines. While the dynamic adjustments were obtained under actual vegetation status using remote sensing platforms; 4) Most significantly, the full-scale implementation resulted in a substantial 8.17% reduction in the water consumption during the critical 2018 growing season in the Yanghe II irrigation district, compared with the conventional irrigation scheduling practices. There was a significant water conservation potential without any compromise in the crop productivity. Therefore, the framework can be expected to implement the precision water strategies at operational regional scales. Substantial improvements can be offered over existing approaches in terms of the prediction accuracy, operational efficiency, and adaptive capacity. Modular architecture can be suitable for the practical adaptability under the diverse agroecological contexts and farming systems. Essential computational tractability is also maintained for the large-area implementation. The timely advancement in the irrigation decision-support technologies can bridge the gap between theoretical modeling and practical water management applications.