Title: Reference Area Method for Mapping Soil Organic Carbon Content at Regional Scale
Citation: PROCEDIA EARTH AND PLANETARY SCIENCE vol. 10 p. 330 - 338
Publisher: ELSEVIER BV
Publication Year: 2014
JRC N°: JRC91497
ISSN: 1878-5220
URI: http://www.sciencedirect.com/science/article/pii/S1878522014000903
DOI: 10.1016/j.proeps.2014.08.028
Type: Articles in periodicals and books
Abstract: Soil organic carbon, the major component of soil organic matter, is very important in all soil processes. The decline in soil organic carbon (SOC) is recognized as one of the eight soil threats identified in the European Union Thematic Strategy for Soil Protection (COM (2006)231 final) (EC, 2006). The research challenge in this paper is to develop aggregation techniques which can ‘catch’ the variability at local level and being able to transfer this knowledge to larger scales. The latest developments included the research in upscaling modelling techniques which allow transferring the data values from local to regional scale. Digital Soil Mapping (DSM) and in particular regression kriging has been selected as the most appropriate modelling approach for the study. It has been successfully implemented at regional level and a soil organic carbon map was produced for the selected region (Slavkovsky Forest, 2400 km2, Czech Republic) by transferring knowledge from Lysina Critical Zone Observatory (0.40 km2). The Lysina CZO is situated in western Bohemia, the Czech Republic. Using digital soil mapping (DSM), demonstrated that it is possible to upscale the processes that take place at Critical Zone Observatories (CZOs) towards larger regions. According to the prediction results, the soil organic carbon values in the Slavkovsky Forest are ranging between 0 % - 35.11%. The results of the linear regression procedure is promising, however, the statistical indicators are relatively low (R2=0.31) in the first step of the two stage model. The results of the study encourage applying similar approach at a wider scale. However, the ground data availability is still the key component to have more robust geostatistical models. The model and its outputs can be improved by using more ground data and high resolution environmental covariates.
JRC Directorate:Sustainable Resources

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