Spatial stochastic modeling of topsoil organic carbon content over a cultivated peri-urban region, using soil properties, soil types and a digital elevation model

Digital Soil Assessments and Beyond - Proceedings of the Fifth Global Workshop on Digital Soil Mapping

Volumn , Issue , 2012, Pages 161-166

  Hamiache, J ^a   Bel, L ^a   Vaudour, E ^a   Gilliot, J M ^a  

^a AGROPARISTECH   (France)

Author keywords

[No Author keywords available]

Indexed keywords

COMPOSTING; DIGITAL INSTRUMENTS; FORESTRY; GEOMORPHOLOGY; MAPPING; ORGANIC CARBON; REMOTE SENSING; SOIL SURVEYS; STOCHASTIC MODELS; STOCHASTIC SYSTEMS; TEXTURES; UNCERTAINTY ANALYSIS;

DIGITAL ELEVATION MODEL; ESTIMATION RESULTS; ORGANIC CARBON CONTENTS; PERI-URBAN AGRICULTURES; PHYSICAL AND CHEMICAL PROPERTIES; REMOTE SENSING ESTIMATIONS; SPATIAL STOCHASTIC; STATISTICAL ESTIMATION;

SOILS;

EID: 84866346585     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1201/b12728-33     Document Type: Conference Paper

References (6)

1. Baize, D. 2000. Guide des analyses en pedologie, 2nd edition. Paris: INRA Éditions.
2. McBratney, A.B. et al. 2003. On Digital Soil Mapping. Geoderma 117:3-52.
3. Pebesma, E.J. 2004. Multivariable geostatistics in S: the gstat package. Computers & Geosciences 30:683-691.
4. Vaudour, E. et al. 2012. Topsoil organic carbon content prediction using VNIR-SWIR reflectance spectra at lab, field and satellite levels over a periurban region. In revision.
5. Wackernagel, H. 2003. Multivariate geostatistics: an introduction with applications, 3rd edition. Berlin: Springer-Verlag.
6. Zhang, Zh. 2010. Application of categorical information in the spatial prediction of soil organic carbon in the red soil area of China. Soil Science & Plant Nutrition 56 (2):307-318.