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Rice is the world’s major staple food crop occupying over 12% of global cropland area which produces around 800 million tons. Nearly 90% of the world’s rice is produced and consumed in Asian countries. Therefore, information on agricultural plantation area, yield, and production are essential to ensure food security of nearly 3 billion people. At the moment this information is either lacking in many countries or only available post-harvest, this is too late to input into any effecting policy in a specific year. Therefore, there is a pressing need to provide accurate and reliable yield estimation well ahead of harvest. In this project we explore potential of multi source remote sensing data coupled with crop model to provide country scale yield estimation in Thailand. For optical sensor, the study utilised Landsat8 OLI/TIRS satellite data to develop common vegetation indexes (VIs) approach to derive essential crop biophysical variables such as Leaf Area Index. This is supplemented with information from microwave sensor such as Sentinel 1 to overcome issues with cloud. At the end, we produced a regular time series of crop biophysical variable across the growing season. These satellite-based estimates were validated with dedicated field campaign in three provinces covering the entire growing season. Initial results suggest a good agreement between the optical/microwave derived crop biophysical variables and ground data. Finally, these will be used as an input to the ORYZA 2000 crop model to adjust the model parameters and develop a high resolution yield prediction.