Measuring Soil Colour to Estimate Soil Organic Carbon Using a Large-Scale Citizen Science-Based Approach

Nerea Ferrando Jorge, Joanna Clark, Macarena L. Cárdenas, Hilary Geoghegan and Vicky Shannon
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Nerea Ferrando Jorge: Department of Geography and Environmental Science, University of Reading, Reading RG6 6AB, UK
Joanna Clark: Department of Geography and Environmental Science, University of Reading, Reading RG6 6AB, UK
Macarena L. Cárdenas: Earthwatch Institute, Oxford OX2 7DE, UK
Hilary Geoghegan: Department of Geography and Environmental Science, University of Reading, Reading RG6 6AB, UK
Vicky Shannon: Department of Geography and Environmental Science, University of Reading, Reading RG6 6AB, UK

Sustainability, 2021, vol. 13, issue 19, 1-17

Abstract: Rapid, low-cost methods for large-scale assessments of soil organic carbon (SOC) are essential for climate change mitigation. Our work explores the potential for citizen scientists to gather soil colour data as a cost-effective proxy of SOC instead of conventional lab analyses. The research took place during a 2-year period using topsoil data gathered by citizen scientists and scientists from urban parks in the UK and France. We evaluated the accuracy and consistency of colour identification by comparing “observed” Munsell soil colour estimates to “measured” colour derived from reflectance spectroscopy, and calibrated colour observations to ensure data robustness. Statistical relationships between carbon content obtained by loss on ignition (LOI) and (i) observed and (ii) measured soil colour were derived for SOC prediction using three colour components: hue, lightness, and chroma. Results demonstrate that although the spectrophotometer offers higher precision, there was a correlation between observed and measured colour for both scientists (R 2 = 0.42; R 2 = 0.26) and citizen scientists (R 2 = 0.39; R 2 = 0.19) for lightness and chroma, respectively. Foremost, a slightly stronger relationship was found for predicted SOC using the spectrophotometer (R 2 = 0.69), and citizen scientists produced comparable results (R 2 = 0.58), highlighting the potential of a large-scale citizen-based approach for SOC monitoring.

Keywords: Munsell soil colour charts; quantitative colour analysis; spectroscopy; CIELAB; soil carbon prediction; citizen scientists (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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