Fusion of Spatio-Temporal Remotely Sensed Evapotranspiration by Data Assimilation for Irrigation Performance

Fusion of Spatio-Temporal Remotely Sensed Evapotranspiration by Data Assimilation for Irrigation PerformanceMonitoring of water consumption in irrigation systems is paramount to integrated water management. This is already possible with Low spatial resolution (LSR) satellite remote sensing. However, smaller pixel size is still required for more local management, while keeping return period within few days. High spatial resolution (HSR) satellite imagery is indeed available for calculation of evapotranspiration, and has been used in many studies already.

Monitoring of water consumption in irrigation systems is paramount to integrated water management. This is already possible with Low spatial resolution (LSR) satellite remote sensing. However, smaller pixel size is still required for more local management, while keeping return period within few days.High spatial resolution (HSR) satellite imagery is indeed available for calculation of evapotranspiration, and has been used in many studies already. However, its practical return period is a major drawback to its implementation for monitoring irrigation systems. This thesis is perusing into the use of genetic algorithms to assimilate parameters of an agro-hydrological model called SWAP for each of the pixels of HSR images contained into one single pixel of a LSR multi-temporal 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.