TY - JOUR
T1 - Spatio-temporal topsoil organic carbon mapping of a semi-arid Mediterranean region: The role of land use, soil texture, topographic indices and the influence of remote sensing data to modelling
AU - Schillaci, Calogero
AU - Acutis, Marco
AU - Lombardo, Luigi
AU - Lipani, Aldo
AU - Fantappiè, Maria
AU - Märker, Michael
AU - Saia, Sergio
N1 - KAUST Repository Item: Exported on 2019-02-13
Acknowledgements: The authors are grateful to Maria Gabriella Matranga, Vito Ferraro and Fabio Guaitoli from the Regional Bureau for Agriculture, rural Development and Mediterranean Fishery, the Department of Agriculture, Service 7 UOS7.03 Geographical Information Systems, Cartography and Broadband Connection in Agriculture, Palermo. The authors also thank three anonymous reviewers for their constructive comments, which helped to improve the manuscript.
PY - 2017/6/2
Y1 - 2017/6/2
N2 - SOC is the most important indicator of soil fertility and monitoring its space-time changes is a prerequisite to establish strategies to reduce soil loss and preserve its quality. Here we modelled the topsoil (0–0.3m) SOC concentration of the cultivated area of Sicily in 1993 and 2008. Sicily is an extremely variable region with a high number of ecosystems, soils, and microclimates. We studied the role of time and land use in the modelling of SOC, and assessed the role of remote sensing (RS) covariates in the boosted regression trees modelling. The models obtained showed a high pseudo-R2 (0.63–0.69) and low uncertainty (s.d.
AB - SOC is the most important indicator of soil fertility and monitoring its space-time changes is a prerequisite to establish strategies to reduce soil loss and preserve its quality. Here we modelled the topsoil (0–0.3m) SOC concentration of the cultivated area of Sicily in 1993 and 2008. Sicily is an extremely variable region with a high number of ecosystems, soils, and microclimates. We studied the role of time and land use in the modelling of SOC, and assessed the role of remote sensing (RS) covariates in the boosted regression trees modelling. The models obtained showed a high pseudo-R2 (0.63–0.69) and low uncertainty (s.d.
UR - http://hdl.handle.net/10754/625113
UR - http://www.sciencedirect.com/science/article/pii/S0048969717313475
U2 - 10.1016/j.scitotenv.2017.05.239
DO - 10.1016/j.scitotenv.2017.05.239
M3 - Article
C2 - 28578240
SN - 0048-9697
VL - 601-602
SP - 821
EP - 821
JO - Science of The Total Environment
JF - Science of The Total Environment
ER -
