Better data flows to reduce water use
In an effort to reduce agricultural pressure on water resources - the sector accounts for up to 80 % of water use in some parts of the EU, researchers are on a mission to improve on-farm irrigation management. More precise land-surface data will make soil-water content estimates more accurate and reduce water use.
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Water scarcity and drought on a global scale have provoked concerns over sustainable water use in Europe and how to improve this. The agricultural sector puts significant pressure on water resources, especially in areas such as the Mediterranean where agricultural irrigation accounts for 80 % of total water use.
Not all of this water is however needed by the crops being irrigated. Modern irrigation relies on measurements of soil moisture in a plant’s root zone. This detects the onset of crop water stress which, in turn, triggers irrigation. However, these measurements are not always available over extended areas, and are rarely representative at field scale, resulting in water wastage.
The REC project is using remote sensing of soil moisture combined with better land-surface modelling techniques to address the need for root-zone soil moisture estimates at crop scale. A downscaling methodology called DISPATCH has been developed to improve the spatial resolution of a satellite data set known as ‘Near Surface Soil Moisture’ (NSSM).
REC will use DISPATCH estimates of NSSM data sets to develop an innovative operational algorithm to map root-zone soil moisture at field scale, on a daily basis. The coupling of surface models and remote-sensing data will lead to more accurate estimates of soil moisture in each field, every day.
These soil-water content estimates will be integrated into a management system that will then be used to trigger irrigation according to crop needs.
These soil-moisture estimates will also enable better monitoring of a farm’s water use, potentially identifying where more water could be saved. Such data could also inform improved predictions of flooding, landslides, groundwater sources and vegetation carbon.