Development of a Remote Sensing-Based System for Monitoring Crop Water Use in Agricultural Water Management in Thailand

Abstract. Efficient water management is crucial for enhancing crop productivity and ensuring food security, especially with growing global water scarcity. To support data-driven agricultural decision-making, this research develops a spatial decision support system using satellite-based Big Earth Observation Data. The system leverages MODIS time-series products to automatically retrieve and analyze Evapotranspiration (ET) and Gross Primary Production (GPP), which are used to calculate Water Use Efficiency (WUE)—a key metric for assessing crop water productivity.
The core innovation is a scalable architecture that integrates multi-temporal satellite data with a Web GIS platform. The system uses PostgreSQL with PostGIS for efficient spatial database management and optimized SQL queries. Its backend, powered by Node.js, delivers RESTful APIs, while GeoServer publishes spatial layers as Web Map Services (WMS). These layers are visualized on an interactive, React-based interface using Leaflet.js, allowing users to dynamically analyze historical water use patterns by region and time via a standard browser.
This system's ability to process and visualize large-scale spatiotemporal data empowers agricultural stakeholders to identify inefficiencies, adapt irrigation practices, and optimize resource allocation. The platform demonstrates how advanced geospatial information and Big Data processing contribute to sustainable agriculture and climate resilience, ultimately helping to improve crop yields and secure long-term food stability. Future enhancements may integrate ground-based or farmer-contributed data to further strengthen its decision-making capacity.