Advances in remote sensing have transformed real-time crop health monitoring and precision agriculture by enabling large-scale, non-invasive, and multi-temporal assessment of crop conditions. In this study, we developed a composite Vegetation Health Index (VHI) that integrates three key biophysical parameters—Leaf Area Index (LAI), Canopy Chlorophyll Content (CCC), and Canopy Water Content (CWC)—to provide a robust and holistic evaluation of crop health. Sentinel-2 surface reflectance imagery was processed using the Biophysical Processor in ESA’s Sentinel Application Platform (SNAP) to extract these parameters, with LAI validated against field measurements from a Li-COR LAI 2200c plant canopy analyzer, yielding an R² value of 0.65. This indicates that the model shows a moderate to strong agreement between satellite-derived and field-measured LAI, demonstrating the model's suitability for large-scale crop health monitoring despite inherent uncertainties in remote sensing-based estimations. The study was conducted at the Indian Agricultural Research Institute (IARI), covering an area of over 250 hectares, with satellite data acquired on 9th March 2025—a cloud-free day during the Rabi season. To enable rapid, scalable, and repeatable monitoring, the workflow was integrated with Google Earth Engine (GEE), allowing efficient analysis of multi-spatial and multi-temporal datasets without the need for complex local preprocessing. CCC and CWC were directly obtained from SNAP outputs, while all parameters were normalized using min–max scaling and combined through equal-weighted linear aggregation to produce the VHI, ranging from 0 (poor health) to 1 (good health). This integrated approach harnesses the computational power of GEE and the strengths of multiple biophysical indicators, providing a valuable tool for timely crop health assessment, early stress detection, and informed decision-making in precision agriculture.