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

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 (R2 = 0.42; R2 = 0.26) and citizen scientists (R2 = 0.39; R2 = 0.19) for lightness and chroma, respectively. Foremost, a slightly stronger relationship was found for predicted SOC using the spectrophotometer (R2 = 0.69), and citizen scientists produced comparable results (R2 = 0.58), highlighting the potential of a large-scale citizen-based approach for SOC monitoring.

The usefulness of the Munsell colour indices for identification of drained soils with various content of organic matter

“…In our study, air-dry samples were analysed because the drying of soil enlarges the span of both value and chroma (Pretorius et al 2017;Rubinić et al 2021). Of the three colour variables, value and chroma show to be strongly dependent on the SOM content (Konen et al 2003;Pretorius et al 2017;Jorge et al 2021). In turn, the relationship between SOM and hue is weak (Pretorius et al 2017;Jorge et al 2021).…”

“…Many articles discuss dependences between the colour of soils and the content of soil organic matter (SOM) or organic carbon (C org. ) (Blume and Helsper 1987;Evans and Franzmeier 1988;Łachacz 1993;Schulze et al 1993;Konen et al 2003;Wills et al 2007;Jorge et al 2021), nitrogen (Qian et al 1993;Moritsuka et al 2014) and iron oxides (Leger et al 1979;Torrent et al 1983;Schwertmann 1993;Moritsuka et al 2014). A rapid assessment of the SOM content using Munsell soil colour charts can help to determine trends and consequently the soil degradation state (Minh et al 2020;Rubinić et al 2021).…”

“…There is an urgent need to develop methods for the assessment of the SOM content that will allow rapid and inexpensive evaluation of organic carbon resources over large areas of the globe. Despite the improvement of spectrophotometric and remote methods, the use of Munsell charts will remain the standard practice (Wills et al 2007;Rabenhorst et al 2015;Jorge et al 2021;Schmidt and Ahn 2021).…”

Cultural geographies II: In the critical zone? – Environments, landscapes and life

“…In the place of only being valued as modes of community engagement, cultural geographers are increasingly working as collaborators within large teams of scholars and other stakeholders, addressing complex challenges. Research in this vein ranges from Hilary Geoghagen and colleagues' work on tree health and biodiversity (Ferrando Jorge et al, 2021), to Gail Davies, Beth Greenhough and colleagues' work on science, health and animal welfare (Davies et al, 2020). The work of Chile based 'BioGeoArt', GeoHumanities and Biogeography Group is an excellent cultural geography led example of the scale and potential of these ways of working.…”

Munsell Soil Colour Classification Using Smartphones through a Neuro-Based Multiclass Solution

“…As a result, they studied dirty and old Munsell charts and they found that the prediction accuracy could be reduced due to the noise in the samples. In [8], the authors used spectroscopy and compared the results with observed MCC estimates in order to ensure robustness in the process of observation and data collection. They detected a certain correlation between the spectrophotometer and the observed measures.…”