The increasing population and future drought scenarios caused by climate change require research into new integrations between technologies, such as remote sensing and water productivity models, to optimize the use of water resources in agriculture and strengthen food security. Peru, the world's leading quinoa producer, considers this crop of great interest due to its nutritional value, adaptability, and resistance to water stress. This study is proposed to establish irrigation thresholds based on the crop water stress index (CWSI) and the AquaCrop model, previously calibrated with the volumetric soil moisture (theta) and canopy cover (CC) of the Amarilla Marangani mutant line quinoa variety during the cold season in which quinoa is most cultivated in Peru. Canopy temperature was monitored with an IR thermal camera, CC with a digital camera, and theta with a TDR-300 sensor. The crop underwent stepwise irrigation reductions (50, 75, and 100% ETc) under a drip irrigation system and a control treatment (full ETc). A significant linear relationship (p < 0.05) was found between CWSI and theta, which allowed us to estimate the water status of the crop accurately. In addition, the AquaCrop model showed a "very good" to "good" performance in the simulation of CC, theta, biomass, and yield development. Activating irrigation at a CWSI of 0.44 is recommended, equivalent to the water consumption of 2770 m(3) ha(-1), achieving a 29% water saving and a yield of 2.5 t ha(-1). These results highlight the potential of combining technologies to improve irrigation efficiency in arid areas and have agricultural sustainability options in the face of climate change.