Predicting soil organic carbon content in Cyprus using remote sensing and Earth observation data

The LUCAS (Land Use/Cover Area frame Statistical Survey) database currently contains about 20,000 topsoil samples of 15 soil properties. It is the largest harmonised soil survey field database currently available for Europe. Soil Organic Carbon (SOC) levels have been successfully determined using both proximal and airborne/spaceborne reflectance spectroscopy. In this paper, Cyprus was selected as a study area for estimating SOC content from multispectral remotely sensed data. The estimation of SOC was derived by comparing 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 compared with the covariate values in the collocated pixels and their eight surrounding neighbours. The regression model adopted made use of Support Vector Machines (SVM) regression analysis. The SVM regression proved to be very efficient in mapping SOC with an R2 fitting of 0.81 and an R2 k-fold cross-validation of 0.68. This study proves that the inference of SOC levels is possible at regional or continental scales using available remote sensing and Earth observation data.

[1]  Chih-Jen Lin,et al.  A tutorial on?-support vector machines , 2005 .

[2]  John A. Eisman,et al.  Communication, Communication, Communication , 2003 .

[3]  Roland Hiederer,et al.  Estimating organic carbon in the soils of Europe for policy support , 2005 .

[4]  Cristiano Ballabio,et al.  A Plant ecology approach to digital soil mapping, improving the prediction of soil organic carbon content in alpine grasslands , 2012 .

[5]  Harry V. Roberts,et al.  Nature statistics , 1989, Nature.

[6]  Silas Michaelides,et al.  Association of geopotential height patterns with heavy rainfall events in Cyprus , 2010 .

[7]  Adriana Bruggeman,et al.  Assessing the urban water balance: the Urban Water Flow Model and its application in Cyprus. , 2012, Water science and technology : a journal of the International Association on Water Pollution Research.

[8]  Bernhard Schölkopf,et al.  A tutorial on v-support vector machines , 2005 .

[9]  Panos Panagos,et al.  A new baseline of organic carbon stock in European agricultural soils using a modelling approach , 2014, Global change biology.

[10]  Roland Hiederer,et al.  Estimating soil organic carbon in Europe based on data collected through an European network , 2013 .

[11]  Rattan Lal,et al.  Soil Science and the Carbon Civilization , 2007 .

[12]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[13]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[14]  A. Zissimos,et al.  Geochemical patterns in the soils of Cyprus. , 2012, The Science of the total environment.

[15]  Gavin C. Cawley,et al.  Efficient leave-one-out cross-validation of kernel fisher discriminant classifiers , 2003, Pattern Recognit..

[16]  Panos Panagos,et al.  Estimating the soil organic carbon content for European NUTS2 regions based on LUCAS data collection. , 2013, The Science of the total environment.

[17]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .

[18]  M. Aizerman,et al.  Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning , 1964 .

[19]  Silas Michaelides,et al.  Spatial and temporal characteristics of the annual rainfall frequency distribution in Cyprus , 2009 .

[20]  G. Tóth.  Impact of land-take on the land resource base for crop production in the European Union. , 2012, The Science of the total environment.

[21]  Muhammet Duman,et al.  Geochemistry of the northern Cyprus (NE Mediterranean) shelf sediments: Implications for anthropogenic and lithogenic impact. , 2012, Marine pollution bulletin.