A collaborative study involving a private sector consortium and an international and a national rice research institution was recently conducted to validate the design of an internet-based information service that would provide more timely and objective data on rice area and production than the rice statistics collection systems currently practiced in most Asian countries. The system consists of two components that make use of the geo-spatial tools including remote sensing, GIS and GPS technologies. The remote sensing component comprises a largely automated protocol using multi-data SAR imagery for mapping and estimating rice area and planting dates. These outputs are fed into a production estimation component comprising a crop growth simulation model, which then predicts harvest dates and crop yield using meteorological data. The results showed that the use of limited number of image acquisitions induces larger errors for the detection of planting dates and land cover classification. In Pangasinan Philippines, only 78% of the planting dates were correctly detected because of fewer image acquisitions compared to Nueva Ecija (93%) and Isabela (89%). In the prediction of rice yield, the accuracy of the IRIS yield estimation relative to fieldwork data and the NIA Statistics showed 85% and 84%, respectively. It is difficult to say which yield figure is the most correct one. However, the IRIS yield predictions can probably be increased slightly by using meteorological data from more stations as compared to the currently gathered data from only 3 stations as experienced from the results of other Asian countries.