Google Earth Engine (GEE) revolutionized agricultural monitoring by providing scientists immediate access to huge satellite archives without requiring heavy local computing. In crop science, this means that water stress and growth conditions can be monitored almost in real time. A critical indicator of plant health is canopy water content (CWC), which reflects leaf hydration and strongly correlates with chlorophyll concentration. Accurate estimation of CWC is essential for timely irrigation management, early detection of crop water stress, and improving overall water-use efficiency. In our research, we developed a hybrid method that couples the PROSAIL radiative transfer model and Gaussian Process Regression (GPR) to directly estimate CWC from Sentinel-2 images. For model training, we utilized field measurements taken at ICAR-IARI, New Delhi, on February 23, 2023, and Sentinel-2 Biophysical Processor data. The model performed well, yielding an R² of 0.88, an RMSE of 0.0007, and an NRMSE of 7.59% for 155 test plots. To extend our assessment, we deployed the trained model on the GEE platform. This enabled us to scale up water content across the entire IARI location. The GEE-based estimates were very close to the local estimates and had an R² of 0.96. The results indicate that integrating PROSAIL with GPR and deploying it on GEE provides an efficient, scalable method for crop water status monitoring. This facilitates irrigation scheduling and precision agriculture on a larger regional level.