Low-cost digital mapping of soil organic carbon using optical spectrophotometer and Sentinel-2 image


  • Roberto Barbetti CREA




organic carbon, soil color, remote sensing, spectrophotometer


Nowadays, scientific research is involved to identifying methods for measuring and mapping some soil properties allowing the cost reduction for sampling and laboratory analyses. In the topic of precision agriculture, it is of interest to obtain accurate spatial distribution maps of soil organic carbon to drive fertilization and variable rate seeding. The aim of this work is to test the   Pro spectrophotometer using both dry and wet topsoil color for the estimation of the total organic carbon (TOC) in a geographically limited area with relatively low soil variability. The relationships obtained using multiple linear regression (R2 of 0.54, p-value < 0.001) were not very accurate due to a high variance between the measured and predicted values. However, starting from a soil geo-resistivity survey and free remote sensing images, this method has proved effective in increasing the number of measured points thus making an important contribution to the creation of an interpolated precision maps of soil organic carbon, calibrated for the study area.






AITKENHEAD M., DONNELLY D., COULL M., GWATKIN R. (2016) Estimating Soil Properties with a Mobile Phone. In: Digital Soil Morphometrics. Progress in Soil Science. Springer

BANCA DATI SUOLI LOSAN. ERSAF - Ente Regionale per i Servizi alla Agricoltura e alle Foreste Regione Lombardia.https://losan.ersaflombardia.it/

CASTALDI, F., HUENI A, CHABRILLAT S., WARD K., BUTTAFUOCO G., BOMANS B.,VREYS K., BRELL M., VAN WESEMAEL B. (2018). Evaluating the capability of the Sentinel 2 data for soil organic carbon prediction in croplands. ISPRS Journal of Photogrammetry and Remote Sensing. 147. 267-282. 10.1016/j.isprsjprs.2018.11.026.

ESCADAFAL, R. Remote sensing of arid soil surface color with Landsat thematic mapper. Advances in Space Research 9 (1989): 159-163.

HENGL T., HEUVELINK G., ROSSITER D. (2007). About regression-kriging: From equations to case studies. Computers & Geosciences. 33. 1301-1315. 10.1016/j.cageo.2007.05.001.

IBÁÑEZ-ASENSIO S, MARQUÉS-MATEU A., MORENO-RAMÓN H., BALASCH, S. (2013). Statistical relationships between soil color and soil attributes in semiarid areas. Biosystems Engineering. 116. 120-129. 10.1016/j.biosystemseng.2013.07.13.

LILES G, BEAUDETTE D., O’GEEN A., HORWATH W. (2013). Developing predictive soil C models for soils using quantitative color measurements. Soil Science Society of America Journal. 77. 2173. 10.2136/sssaj2013.02.0057.

KIRILLOVA, N.P., KEMP D., ARTEMYEVA, Z. (2017). Colorimetric analysis of soil with flatbed scanners. European Journal of Soil Science. 68. 420-433. 10.1111/ejss.12442.

KIRILLOVA, N.P., ARTEMYEVA, Z.S. 2015. Opredelitel’ tsveta pochv [Soil Colour Extractor (SoColEx)]. RF Patent 2015618613, 12.08.2015, The Federal Institute of Industrial Property (FIPS), Moscow

MANDAL, UMESH. (2016). Spectral color indices based geospatial modeling of soil organic matter in Chitwan district, Nepal. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. XLI-B2. 43-48. 10.5194/isprsarchives-XLI-B2-43-2016.

MINISTERO DELLE POLITICHE AGRICOLE ALIMENTARI E FORESTALI, MIPAF. Gruppo di Lavoro nominato con DM n. 8604 dell’1/09/2015. Linee guida per lo sviluppo dell’agricoltura di precisione in italia, Settembre 2017

POUGET, M., MADEIRA, J., LE FLOCH, E., & KAMAL, S. (1990). Caracteristiques spectrales des surfaces sableuses de la region cotiere Nord-Ouest de I’Egypte: Application aux donnees satellitaires SPOT. In ORSTOM (Ed.), 2eme JoumCes de T detection: Caracterisation et suivi des milieux terrestres en regions arides et tropicales,pp 27–38

PRIORI S., DE BENEDETTO D., STELLACCI A. M., LOSCIALE P., MANFRINI L. Sensori e metodi per rilievi prossimali delle proprietà del suolo e della coltura 2016. Edagricole, Edizioni Agricole http://hdl.handle.net/11585/610779

STIGLITZ, ROXANNE Y., "Application of Low-cost Color Sensor Technology in Soil Data Collection and Soil Science Education" (2017). All Dissertations. 1995. https://tigerprints.clemson.edu/all_dissertations/1995

STIGLITZ, R.Y.; MIKHAILOVA, E.A.; SHARP, J.L.; POST, C.J.; SCHLAUTMAN, M.A.; GERARD, P.D.; COPE, M.P. Predicting Soil Organic Carbon and Total Nitrogen at the Farm Scale Using Quantitative Color Sensor Measurements. Agronomy 2018, 8, 212.

SONNEVELD, M.P.W. & HEUVELINK, GERARD & MOOLENAAR, SIMON. (2014). Application of visual soil examination and evaluation technique at site and farm level. Soil Use and Management. 30. 10.1111/sum.12117




How to Cite

Barbetti, R. (2021). Low-cost digital mapping of soil organic carbon using optical spectrophotometer and Sentinel-2 image. EQA - International Journal of Environmental Quality, 44, 1-8. https://doi.org/10.6092/issn.2281-4485/12071