<![CDATA[The increasing global population and the complexity of food security challenges are driving an urgent need for accurate, fast, and large-scale rice production data. This study analyzes the potential of Google Earth Engine (GEE) a cloud computing platform that integrates a petabyte satellite data catalog with planetary-scale analysis capabilities as a primary tool for estimating rice production. The methodology studied focuses on a vegetation index (Normalized Difference Vegetation Index/NDVI)-based approach and a simple linear regression model. A case study in Sleman Regency demonstrates that a regression model can be developed, but critical analysis reveals a very low coefficient of determination (R2). This indicates that the linear vegetation index-based model is only able to explain a small portion of the variability in rice productivity in the field. This finding emphasizes that non-vegetative factors, such as rainfall and soil structure, play a much more significant role than vegetation indices alone can capture. While GEE offers a revolutionary solution to overcome the limitations of conventional methods (time and cost), its effectiveness as an accurate estimation tool is highly dependent on the development of more complex models. Future success requires the integration of multispectral data with multidisciplinary data (e.g., climate and soil). GEE, with its cloud computing architecture, is positioned as a key catalyst for the transition to proactive, data-driven precision agriculture.]]>
