Title: Predicting soil organic carbon content in Cyprus using remote sensing and earth observation data
Authors: BALLABIO CRISTIANOPANAGOS PanagiotisMONTANARELLA Luca
Citation: PROCEEDINGS OF SPIE - THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING vol. 9229 p. 92290F
Publisher: S P I E - INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING
Publication Year: 2014
JRC N°: JRC89700
ISSN: 0277-786X
URI: http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=1897204
http://publications.jrc.ec.europa.eu/repository/handle/JRC89700
DOI: 10.1117/12.2066406
Type: Articles in periodicals and books
Abstract: The LUCAS database currently contains about 20 thousand topsoil samples of 15 soil properties. This is the largest harmonized soil survey field database at the moment available for Europe. Soil Organic Carbon (SOC) levels have been successfully determined using reflectance spectroscopy, both proximal and airborne/spaceborne. In this paper, Cyprus was selected as a study area estimating SOC content from multispectral remotely sensed data. The estimation of SOC was derived by relating field measurements with a set of spatially exhaustive covariates including DEM derived terrain features, MODIS Vegetation indices (16 days) and Landsat ETM+ data. In particular, the SOC levels in the LUCAS database were related with the covariates values in the collocated pixels and their 8 surrounding neighbours. The regression model adopted made use of Support Vector Machines (SVM) regression. The SVM regression proved to be very efficient in mapping SOC with a fitting R2 of 0.81 and a k-fold cross-validation R2 of 0.68. This study proves that the inference of SOC levels is possible at regional or continental scales using already available remote sensing data and earth observations.
JRC Directorate:Sustainable Resources

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