Rice is one of the most important crops in the whole world, providing staple food for more than 3000 million people. For this reason FAO declared the year 2004 as The International Year of Rice promoting initiatives and researches on this valuable crop. Assessing the Net Primary Production (NPP) is fundamental to support a sustainable development and to give crop yield forecast essential to food security policy. Crop growth models can be useful tools for estimating growth, development and yield but require complex spatial distributed input parameters to produce valuable map. Light use efficiency (LUE) models, using satellite-borne data to achieve daily surface parameters, represent an alternative approach able to monitor differences in vegetation compound providing spatial distributed NPP maps. An experiment aimed at testing the capability of a LUE model using daily MODIS data to estimate rice crop production was conducted in a rice area of Northern Italy. Direct LAI measurements and indirect LAI2000 estimation were collected on different fields during the growing season to define a relationship with MODIS data. An hyperspectral MIVIS image was acquired in early July on the experimental site to provide high spatial resolution information on land cover distribution. LUE-NPP estimations on several fields were compared with CropSyst model outputs and field biomass measurements. A comparison of different methods performance is presented and relative advantages and drawbacks in spatialization are discussed.