Crop growth and production can be simulated by models for the whole canopy as a function of intercepted radiation, water availability, air temperature and nitrogen availability. Simulation models supply quantitative outputs starting from quantitative inputs and they need quite complex databases to run simulations. In practice, the more complex and physically based these tools are, the more inputs are required for their application. In most cases such data are not available. This is the reason why, for large scale evaluations, simplified models are often applied and satellite data are used as input. In particular, multi-temporal Earth Observation data represent a valid tool to define crop phenological stages and derive temporal and spatial variability of vegetation biophysical parameters, such as the Leaf Area Index (LAI). In 2003 and 2004 two intensive field campaigns were conducted over different areas of the Italian Rice Belt, Northern Italy, with the objective of collecting data for growth model calibration. Field spectroradiometer measurements and LAI estimation, retrieved by LAI2000, have been used to study the best Vegetation Index (VI) for rice growth monitoring. VI vs LAI relationship has been scaled up to MODIS data to produce LAI map for the entire growing season and the key phenological rice events have been detected by multitemporal MODIS analysis. Preliminary results of rice production estimation using a Light Use efficiency model that ingests spatially distributed phenological information are presented. Comparison with CropSyst model phenological parameters are provided and the contribution of multi-temporal EO data for regional crop monitoring is discussed.