Verde use case:
Improve the accuracy of field water balance calculations
By Dr Matthew Smith, Chief Product Officer
on February 10, 2020
Estimations of a field's water balance can improved using Verde: satellite-derived field attributes from Airbus. This article contains a simple walkthrough on how this could be achieved.
Getting field water balance right is essential to producing a healthy crop. Therefore, having an advanced understanding of water-balance related threats to production is valuable to much of the agri-food sector, from deciding when and where to irrigate to trading in crop futures. Verde Field Attribute data could be used to enable a more accurate estimation of the water balance of a field.
This graph shows the key difference between reference evapotranspiration and potential evapotranspiration in the late summer of 2018, influenced by the lack of photosynthetic leaves of the grass crop in that time window.



We have created a simple walkthrough example of incorporating satellite-derived Leaf Area Index into a model computing soil water balance. This demonstrates how it can be used to improve understanding of crop water demand and plant water stress.

The example takes you through a year in the life of a chosen field of grass. Over that time the LAI information shows that the grass grows larger from the winter through to the summer until it declines rapidly in mid to late summer.
This graph demonstrates the relationship between the plant and soil water properties. Soil Water content is clearly influenced by daily rainfall.


That decline coincides with an extended period, of about a month and a half, for which rain is very low. The data implies that when the field dries out, the grass crop experiences water stress and begins to die back. Once the rains return in the autumn the grass recovers.

The example then shows how to produce an estimation of soil water balance and plant water stress using a combination of the data on Agrimetrics Data Marketplace, which includes the LAI data, soil texture data and weather data.

The result is a set of time series that include our estimates of available soil water and plant water stress. These imply that the grass died back as the soil dried out.
Developers and data scientists can find a walkthrough of example code for this use-case here:
About Agrimetrics
Agrimetrics is the food and farming sector's Data Marketplace. We enable organisations to safely share and monetise their data, whilst making it easier for data-consumers to access the information they need. Our goal is to help create a more productive and sustainable food system by enabling next-generation solutions as quickly and affordably as possible.

We are one of four centres for agricultural innovation founded with an initial investment from Innovate UK. Our founding partners are NIAB, SRUC, Rothamsted Research and The University of Reading. We have strategic partnerships with Airbus and Microsoft and are a participant in Microsoft's prestigious AI for Earth programme.

About the four centres
The Agri-Tech Centres are a unique collaboration between Government, academia and industry created to drive greater efficiency, resilience and wealth across the agrifood sector. A £90m investment from the UK's strategic innovation agency (Innovate UK) is enabling the Centres to harness leading UK research and expertise as well as build new infrastructures and innovation. They include CHAP (Crop Health and Protection), CIEL (Centre for Innovation Excellence in Livestock), Agri-EPI (Engineering and Precision Technologies), and Agrimetrics.
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