Descripció del projecte
Agriculture is an important pressure on water resources, especially in the Mediterranean countries where irrigation represents up to 80% of the consumptive uses of water. It now becomes necessary to improve on-farm irrigation management by adjusting water supplies to crop water requirements along the growing season. Modern irrigation agencies rely on in situ root zone soil moisture measurements to detect the onset of crop water stress and to trigger irrigations. However, in situ point measurements are generally not available over extended areas and may not be representative at the field scale.
The H2020-funded REC project proposes a remote sensing-based solution to the need of root-zone soil moisture at the crop scale for irrigation management. The methodology relies on the coupling between a surface model representing the water fluxes at the land surface atmosphere interface, and remote sensing data composed of land surface temperature (thermal infrared), surface reflectances (visible and near infrared) and near-surface soil moisture (passive and active microwaves).
In this PhD program, we propose to implement a simple bucket model to estimate root zone soil moisture. The model will be constraint by surface soil moisture estimated from SMOS or SMAP satellites. The comparison of model surface soil moisture and EO might help improving LSM by detecting irrigation dates and quantity.
