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.
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Inst Artificial Intelligence R&D, Fruskogorska 1, Novi Sad 21000, Serbia
Smart Cloud Farming, Rosenthaler Str 72a, D-10119 Berlin, GermanyInst Artificial Intelligence R&D, Fruskogorska 1, Novi Sad 21000, Serbia
Pavlovic, Marko
Ilic, Slobodan
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Inst Artificial Intelligence R&D, Fruskogorska 1, Novi Sad 21000, SerbiaInst Artificial Intelligence R&D, Fruskogorska 1, Novi Sad 21000, Serbia
Ilic, Slobodan
Ralevic, Neobojsa
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Univ Novi Sad, Fac Tech Sci, Trg Dositeja Obradov 6, Novi Sad 21000, SerbiaInst Artificial Intelligence R&D, Fruskogorska 1, Novi Sad 21000, Serbia
Ralevic, Neobojsa
Antonic, Nenad
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Smart Cloud Farming, Rosenthaler Str 72a, D-10119 Berlin, GermanyInst Artificial Intelligence R&D, Fruskogorska 1, Novi Sad 21000, Serbia
Antonic, Nenad
Raffa, Dylan Warren
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Smart Cloud Farming, Rosenthaler Str 72a, D-10119 Berlin, Germany
Council Agr Res & Econ CREA, Res Ctr Agr & Environm, Via Navicella 2, I-00184 Rome, ItalyInst Artificial Intelligence R&D, Fruskogorska 1, Novi Sad 21000, Serbia
Raffa, Dylan Warren
Bandecchi, Michele
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Smart Cloud Farming, Rosenthaler Str 72a, D-10119 Berlin, GermanyInst Artificial Intelligence R&D, Fruskogorska 1, Novi Sad 21000, Serbia
Bandecchi, Michele
Culibrk, Dubravko
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Inst Artificial Intelligence R&D, Fruskogorska 1, Novi Sad 21000, Serbia
Smart Cloud Farming, Rosenthaler Str 72a, D-10119 Berlin, GermanyInst Artificial Intelligence R&D, Fruskogorska 1, Novi Sad 21000, Serbia
机构:
Univ New South Wales, Ctr Ecosyst Sci, Sch Biol Earth & Environm Sci, Sydney, NSW 2052, Australia
Australian Museum, Australian Museum Res Inst, Sydney, NSW, AustraliaUniv New South Wales, Ctr Ecosyst Sci, Sch Biol Earth & Environm Sci, Sydney, NSW 2052, Australia
Gillard, G. L.
Rowley, J. J. L.
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Univ New South Wales, Ctr Ecosyst Sci, Sch Biol Earth & Environm Sci, Sydney, NSW 2052, Australia
Australian Museum, Australian Museum Res Inst, Sydney, NSW, AustraliaUniv New South Wales, Ctr Ecosyst Sci, Sch Biol Earth & Environm Sci, Sydney, NSW 2052, Australia