The efficient monitoring of crop water requirements is fundamental to ensuring sustainable agricultural practices and optimizing irrigation strategies to conserve increasingly scarce water resources. This study focuses on the integration of radar and multispectral remote sensing technologies to provide a precise, scalable, and continuous solution for assessing the water needs of orange trees, a key component of citrus cultivation. Radar images, with their unparalleled ability to penetrate cloud cover and operate independently of light conditions, enable uninterrupted temporal monitoring. By calculating radar indices such as VH-VV, these images contribute to accurate assessments of crop coefficients. Meanwhile, multispectral images, rich in detailed vegetation indices like NDVI, offer critical insights into the water status and overall vegetative health of plants, further enhancing monitoring precision. Data were acquired from Sentinel-1 radar and Sentinel-2 multispectral satellite missions over a comprehensive five-year period (2019–2023). This extensive dataset allowed for robust temporal and spatial analyses, capturing the dynamic water needs of orange trees across various growth stages and environmental conditions. Vegetation and radar indices were computed and integrated into advanced water requirement models, validated meticulously against reference field measurements. Results showed that multispectral-derived crop coefficients achieved a root-mean-square error (RMSE) of 0.1938 during periods of high water demand, underscoring the reliability of the approach. Radar-derived crop coefficients exhibited an even lower RMSE of 0.050, reflecting the superior accuracy of radar data in modeling water requirements. These findings highlight the transformative potential of combining radar and multispectral indices for irrigation monitoring. This integrated approach delivers unprecedented precision in both temporal and spatial scales, significantly reducing reliance on labor-intensive and time-consuming field measurements. The proposed methodology not only enables efficient water resource allocation but also supports informed decision-making in agricultural management, paving the way for enhanced crop productivity and resilience to climatic variability. Furthermore, this research provides a significant contribution to the global agenda for sustainable water management, addressing challenges in agricultural systems increasingly affected by water scarcity and environmental stresses. By advancing the application of remote sensing technologies, this study sets a benchmark for precision agriculture and offers a scalable framework adaptable to other crops and agro-ecosystems.