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.

Rice cultivation is a major source of methane (CH₄) emission in the agricultural sector, with a significantly higher global warming potential than carbon dioxide. Accurate and scalable quantification of CH₄ from rice paddies is essential for carbon accounting. This study presents an automated framework for estimating rice CH₄ emissions from irrigated paddies in the central plain of Thailand, integrating multi-sensor Synthetic Aperture Radar (SAR) observations with the IPCC methodology. The framework combines Sentinel-1 C-band SAR time series for phenological detection, ALOS-2 PALSAR-2 L-band full-polarimetric SAR for water regime classification, and IPCC water-scaling factors corresponding to Continuous Flooding, Single Drainage, or Multiple Drainage regimes. Evaluated across five stratified holdout sets, the phenology detection algorithm achieved planting and harvesting date Mean Absolute Errors of 6.1 ± 1.4 and 8.3 ± 1.7 days, with a 97.0% ± 2.7% operational detection rate. Water regime classification employed rice growth stage-specific Support Vector Machine classifiers with Radial Basis Function kernels (SVM-RBF), achieving per-stage test Balanced Accuracy ranging from 0.59 to 0.89. End-to-end integration using a four-track counterfactual decomposition yielded a full-pipeline mean absolute error of 18.5 ± 4.5 $\mathrm{kg}{\mathrm{CH}}_4{\mathrm{ha}}^{-1}$ (21.4% of the mean ground-based CH₄ calculation) and a mean bias of 3.5 ± 5.8 $\mathrm{kg}{\mathrm{CH}}_4{\mathrm{ha}}^{-1}$. Water level classification was confirmed as the dominant algorithmic uncertainty source, while the IPCC Tier 1 emission factor structural range (−32% to +48% of the default) exceeded all algorithmic errors combined. The proposed framework provides a spatially explicit approach for integrating multi-frequency SAR data into IPCC-compliant methane estimation, supporting Monitoring, Reporting, and Verification applications.