EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Can we use citizen science to upscale soil data collection?

Christian Schneider1, Susanne Döhler1,2, Luise Ohmann1, and Ute Wollschläger1,2
Christian Schneider et al.
  • 1Helmholtz-Zentrum für Umweltforschung – UFZ
  • 2BonaRes - Centre for Soil Research

Citizen science approaches are still relatively rare in soil sciences. However, the Tea Bag Index (TBI) has been successfully implemented in projects all over the world.

Our citizen science project “Expedition ERDreich – Mit Teebeuteln den Boden erforschen” (EE) aims to upscale open soil data by applying the TBI as well as other soil assessment methods all over Germany. Beside the strong focus on creating awareness for soils and its functions we want to answer the following questions:

The project will combine aspects of co-production as well as environmental education. Co-production means, soil data will individually be compiled by citizen scientists with the support of a team of scientists from a network of project partners. While conducting various soil assessments and experiments participating citizen scientists will be given background information and guidance meant to educate and to raise awareness about soils and soil quality.

We are aiming to involve a broad spectrum of citizens from various backgrounds, for example school children, students, farmers, forest owners, gardeners, municipal administrations, and of course soil scientists.

Within the project citizen scientists will submit turnover data from their location, together with information on the sampling sites, as well as information on soil properties like pH value, soil texture, and soil color. This information will be complemented with climatic and geo-scientific co-variables by the scientific project team.

So far we identified the following main challenges:

  • How can citizens from various backgrounds and in various geographical locations be addressed and involved in the project?

  • How do we get high quality soil data while still teaching soil awareness?

  • How do we address the complexity of soils in soil education?

  • How do we manage the quality of data and identify potential errors?

  • How do we communicate data management procedures to keep the project as transparent as possible?

  • What and how can we give back an added value to citizen scientists?

  • How do we involve citizen scientists in the scientific progress beyond collecting data and beyond the current projects timeframe?

How to cite: Schneider, C., Döhler, S., Ohmann, L., and Wollschläger, U.: Can we use citizen science to upscale soil data collection?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20373,, 2020


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  • CC1: Comment on EGU2020-20373, Sarah Garré, 02 May 2020

    Hi Christian,

    this sounds like a great project. I am very curious indeed how we can check and improve the quality of citizen soil data and identify errors. Can you address a bit what your methodology will be for this? I also wonder how exactly you plan on measuring the educational impact of the project. Success with this cool work!

    • AC1: Reply to CC1, Christian Schneider, 04 May 2020

      Good morning Sarah,

      and thank you very much for your feedback and your question!

      Error identification and data flagging will in indeed not be an easy but very important task. Unfortunately, we do not have a comprehensive data quality/validation protocol yet.

      But as a first step we will a.) implement automated filters in our data uploading portal, b.) ask participants for photo documentation, c.) ask participants to give us a notice in case something went wrong with a measurement, and d) check for completeness of the basic information needed.

      Once the data is uploaded we will identify outliers and do plausibility tests checking for consistency within a data set. Also we will use spatial geo-information data sets for plausibility checks such as soil texture maps, soil parent material maps etc.

      In the end we will internally flag data sets with a low confidence in plausibility and/or data quality.

      In order to develop the protocol we will also be happy to share ideas and experiences with other projects and to stay in touch about this important issue!