Real coded genetic algorithm for assimilating remotely sensed evapotransiration data using a soil-water-atmosphere- plant model. A methodology. Y. Chemin, K. Honda and A.V.M. Ines 1 Abstract Monitoring agricultural activities has benefited so much over the last 20 years from the advances in remote sensing (RS). Nowadays, operational algorithms are available to calculate evapotranspiration (ET) calculation at the pixel level, which is an important state variable for agricultural and water management studies. Since these algorithms are based on thermal and visible information, their success of implementation depends highly on clear sky conditions. Oftentimes, agricultural managers monitoring requirements may not always match with the availability of satellite images. Supplementing the RS data with synthetic dataset is a promising option in this case. This paper describes a methodology where the evapotranspiration data from RS images could be used to generate data during the periods when satellites are not available. A real-coded genetic algorithm is coupled with a Soil-Water- Atmosphere-Plant model (SWAP) to estimate pixel-based soil-plant-water parameters controlling the pixel evapotranspiration derived from the satellite images. The search space, the cost and fitness functions, as well as, the physical meaning of the optimized pixel-based parameters are defined in the paper. Keywords: Genetic algorithm, data assimilation, SWAP, remote sensing, evapotranspiration. Introduction Agricultural monitoring is necessary for efficient food security management at country level. Typically, monitoring requirement from the point of view of an agricultural/irrigation manager would be to “see” each field at a regular interval to which 15 days is reasonable. Evapotranspiration (ETa) is converting the water into crop, and is therefore a crucial indicator of crop productivity. ETa can be estimated from satellite remote sensing (Kustas and Norman, 1996, Bastiaanssen, 1998, Menenti, 2000). However, on the side of satellite platforms specifications, high spatial resolution is at about size of the largest fields (~1 ha), but is available only few times a year practically, while low spatial resolution is available daily (even 8days composites are ready from Internet). A potential solution would be to match the two type of satellite images evapotranspiration by running instances of crop models at both resolutions with proper parameters. Those crop model input parameters are changing on pixel- 1 Y. Chemin, K. Honda, STAR Program, Asian Institute of Technology, Thailand. yann.chemin@ait.ac.th , honda@ait.ac.th ; A.V.M. Ines, International Research Institute for Climate Prediction, Columbia State University, USA. ines@iri.columbia.edu WORLD CONGRESS ON COMPUTERS IN AGRICULTURE AND NATURAL RESOURCES