Mapping continuous depth functions of soil carbon storage and available water capacity

Geoderma

Volumn 154, Issue 1-2, 2009, Pages 138-152

  Malone, B P ^a   McBratney, A B ^a   Minasny, B ^a   Laslett, G M ^b  

^a UNIVERSITY OF SYDNEY   (Australia)

^b CSIRO   (Australia)

Author keywords

Available water capacity; Carbon storage; Digital soil mapping; Equal area spline functions; Neural networks

Indexed keywords

AVAILABLE WATER CAPACITIES; CARBON STORAGE; EQUAL-AREA SPLINE FUNCTIONS; SOIL MAPPING; SPLINE FUNCTIONS;

FORESTRY; INTERPOLATION; MAPPING; NEURAL NETWORKS; PLANNING; RESOURCE ALLOCATION; SOILS; SPLINES; WATER RESOURCES;

GEOLOGIC MODELS;

ARTIFICIAL NEURAL NETWORK; CARBON SEQUESTRATION; DATABASE; DECISION MAKING; DIGITAL ELEVATION MODEL; DIGITAL MAPPING; LANDSAT; MODEL VALIDATION; RESOURCE MANAGEMENT; SATELLITE DATA; SATELLITE IMAGERY; SOIL PROFILE; SOIL PROPERTY; SPATIAL VARIATION; VERTICAL DISTRIBUTION; WATER AVAILABILITY; WATER BUDGET;

AUSTRALASIA; AUSTRALIA; EDGEROI; NEW SOUTH WALES;

EID: 70449645048     PISSN: 00167061     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.geoderma.2009.10.007     Document Type: Article

References (30)

1. Beckett P.H.T., and Webster R. Soil variability: a review. Soils and Fertilizers 34 (1971) 1-15
2. Bishop T.F.A., McBratney A.B., and Laslett G.M. Modelling soil attribute depth functions with equal-area quadratic smoothing splines. Geoderma 91 (1999) 27-45
3. Colwell J.D. A statistical-chemical characterization of four great soil groups in southern New South Wales based on orthogonal polynomials. Australian Journal of Soil Research 8 (1970) 221-238
4. Cook S.E., Corner R.J., Groves P.R., and Grealish G.J. Use of airborne gamma radiometric data for soil mapping. Australian Journal of Soil Research 34 (1996) 183-194
5. Erh K.T. Application of spline functions to soil science. Soil Science 114 (1972) 333-338
6. Florinsky I.V., Eilers R.G., Manning G.R., and Fuller L.G. Prediction of soil properties by digital terrain modelling. Environmental Modelling & Software 17 (2002) 295-311
7. Geosciences Australia, 2008. Radiometric data of the Narrabri, Moree, Inverell and Manilla 1:250 000 topographic map sheets. In (Geophysical Archive Data Delivery System (GADDS) Website, date accessed 15/05/08. http://www.geoscience.gov.au/bin/mapserv36?map=/public/http/www/geoportal/gadds/gadds).
8. Grimm R., Behrens T., Marker M., and Elsenbeer H. Soil organic carbon concentrations and stocks on Barro Colorado Island - digital soil mapping using Random Forests analysis. Geoderma 146 (2008) 102-113
9. Hempel J.W., Hammer R.D., Moore A.C., Bell J.C., Thompson J.A., and Golden M.L. Challenges to Digital Soil Mapping. In: Hartemink A.E., McBratney A.B., and Mendonca-Santos M.L. (Eds). Digital Soil Mapping with Limited Data (2008), Springer Science, Australia 81-90
10. Jenny H. Factors of Soil Formation: A System of Quantitative Pedology (1941), McGraw-Hill, New York 281 pp
11. Lagacherie P. Digital Soil Mapping: A State of the Art. In: Hartemink A.E., McBratney A.B., and Mendonca-Santos M.L. (Eds). Digital Soil Mapping with Limited Data (2008), Springer Science, Australia 3-14
12. McBratney A.B., Mendonca-Santos M.L., and Minasny B. On digital soil mapping. Geoderma 117 (2003) 3-52
13. McGarry D., Ward W.T., and McBratney A.B. Soil Studies in the Lower Namoi Valley: Methods and Data. The Edgeroi Dataset (1989), CSIRO Division of Soils, Adelaide 2 vols.
14. McKenzie N.J., and Ryan P.J. Spatial prediction of soil properties using environmental correlation. Geoderma 89 (1999) 67-94
15. Minasny B., McBratney A.B., Mendonca-Santos M.L., Odeh I.O.A., and Guyon B. Prediction and digital mapping of soil carbon storage in the Lower Namoi Valley. Australian Journal of Soil Research 44 (2006) 233-244
16. Minasny B., McBratney A.B., and Lark M.R. Digital Soil Mapping Technologies for Countries with Sparse Data Infrastructures. In: Hartemink A.E., McBratney A.B., and Mendonca-Santos M.L. (Eds). Digital Soil Mapping with Limited Data (2008), Springer Science, Australia 15-30
17. Moore A.W., Russell J.S., and Ward W.T. Numerical analysis of soils: a comparison of three soil profile models with field classification. Journal of Soil Science 23 (1972) 193-209
18. Moore I.D., Gessler P.E., Nielsen G.A., and Peterson G.A. Soil attribute prediction using terrain analysis. Soil Science Society of America Journal 57 (1993) 443-452
19. Odeh I.O.A., McBratney A.B., and Chittleborough D.J. Further results on prediction of soil properties from terrain attributes: heterotopic cokriging and regression-kriging. Geoderma 67 (1995) 215-226
20. Ponce-Hernandez R., Marriott F.H.C., and Beckett P.H.T. An improved method for reconstructing a soil-profile from analysis of a small number of samples. Journal of Soil Science 37 (1986) 455-467
21. Rowe W.D. Understanding uncertainty. Risk Analysis 14 (1994) 743-750
22. Russell J.S., and Moore A.W. Comparison of different depth weightings in the numerical analysis of anisotropic soil profile data. Transactions of the 9th International Congress of Soil Science vol. 4 (1968) 205-213
23. Ryan P.J., McKenzie N.J., O'Connell D., Loughhead A.N., Leppert P.M., Jacquier D., and Ashton L. Integrating forest soils information across scales: spatial prediction of soil properties under Australian forests. Forest Ecology and Management 138 (2000) 139-157
24. Saunders A.M., and Boettinger J.L. Incorporating Classification Trees into a Pedogenic Understanding Raster Classification Methodology, Green River Basin, Wyoming, USA. In: Lagacherie P., McBratney A.B., and Voltz M. (Eds). Digital Soil Mapping: An Introductory Perspective. Developments in Soil Science (2007), Elsevier, Amsterdam 389-399
25. Shrestha D.L., and Solomatine D.P. Machine learning approaches for estimation of prediction interval for the model output. Neural Networks 19 (2006) 225-235
26. Skidmore A.K. A comparison of techniques for calculating gradient and aspect from a gridded gigital elevation model. International Journal of Geographical Information Systems (1989) 323-334
27. Stacey K.F., Lark R.M., Whitmore A.P., and Milne A.E. Using a process model and regression kriging to improve predictions of nitrous oxide emissions from soil. Geoderma 135 (2006) 107-117
28. Stoorvogel J.J., Kempen B., Heuvelink G.B.M., and de Bruin S. Implementation and evaluation of existing knowledge for digital soil mapping in Senegal. Geoderma 149 (2009) 161-170
29. Webster R. Mathematical treatment of soil information. Transactions of the 11th International Congress of Soil Science vol. 3 (1978) 161-190
30. Young P. Data-based mechanistic modelling of environmental, ecological, economic and engineering systems. Environmental Modelling & Software 13 (1998) 105-122