Water is a key input for agriculture production. However, the increasing issue of water scarcity, often associated with climate change, poses a significant obstacle to agricultural expansion, particularly in arid and semi-arid regions. Water crises in agriculture are enhanced by the inefficient water management and competition with other sectors contending the access to the water resources. In this context, exploiting technological advances is increasingly urgent to optimize water utilization in agriculture and ensure the sustainability of food supply for future generations. Here, an advanced irrigation scheduling method to achieve irrigation water optimization for open-field crops is evaluated against field data and, in comparison with a standard approach that only exploits crop growth models. The proposed method is based on the integration of such crop growth models with data acquired by remote sensing technology to demonstrate that a sequential assimilation of satellite crop imagery can be efficiently used to support irrigation planning at farm level as well as at irrigation district level. This study presents an implementation to crops of processing tomato in Campania Region (Southern Italy), by integrating the following components: • Canopy cover data derived from multispectral images captured by Sentinel-2 satellite