Spatiotemporal fusion of rice actual evapotranspiration with genetic algorithms and an agrohydrological model

Monitoring of water consumption in irrigation systems has become increasingly important for water managers in the actual trend of integrated water management. Low spatial resolution (LSR) satellite remote sensing has already proven the capacity of monitoring evapotranspiration (ETa) over large areas at high temporal frequencies, by which monitoring for large irrigation systems can be satisfied. However, smaller pixel size is still required for more local management while keeping the return period within a few days. High spatial resolution (HSR) satellite imagery is indeed available for calculation of ETa and has already been used in many studies. However, its practical return period is a major drawback to its implementation for monitoring irrigation systems. This paper is perusing into the use of genetic algorithms to assimilate parameters of an agrohydrological model called soil-water-air-plant for each of the pixels of HSR images contained into one single pixel of an LSR multitemporal image. The methodology developed and experimented here is trying to take advantage of the spatial content of HSR images and the temporal content of LSR images by fusing them by the process of data assimilation. Index Terms--Data assimilation, data fusion, evapotranspiration (ETa), genetic algorithm (GA), soil-water-air-plant (SWAP) model.