Abstract
With the increasing global pressure on water resources, accurate assessment and effective management of the agricultural water footprint have become essential for achieving sustainable agricultural development. This paper, based on big data technologies, systematically analyzes the composition and dynamic changes of the agricultural water footprint. By integrating remote sensing, sensor data, meteorological information, and agricultural production data, a multi-source data fusion framework for analyzing agricultural water footprints is proposed. Considering the water demands and environmental dependencies of different crops, the study conducts a differentiated assessment from both spatial and temporal perspectives, revealing the potential impacts of agricultural activities on regional water resources and associated ecological risks. It further explores strategies to reduce water consumption by optimizing agricultural production structures and cropping patterns, with a focus on the application of sustainable farming practices such as smart irrigation, crop rotation, and intercropping. This paper aims to provide a scientific basis and decision-making support for the refined management of agricultural water resources, risk early warning, and sustainable development through a big data-driven water footprint assessment approach.
