Derivation of soil-attribute estimations from legacy soil maps
Nathan P. Odgers A F , Karen W. Holmes B C D , Ted Griffin B and Craig Liddicoat E
+ Author Affiliations
- Author Affiliations
A Department of Environmental Sciences, Faculty of Agriculture and Environment, C81 Biomedical Building, The University of Sydney, NSW 2006, Australia.
B Department of Agriculture and Food Western Australia, 3 Baron-Hay Court, South Perth, WA 6151, Australia.
C Soil Matrix Group, School of Earth and Environment, The University of Western Australia, Stirling Highway, Crawley, WA 6009, Australia.
D CSIRO Sustainable Agriculture Flagship, Ecosciences Precinct, Dutton Park 4001, Australia.
E Department of Environment, Water and Natural Resources, Plant Biodiversity Centre, Hackney Road, Botanic Gardens, Adelaide, SA 5001, Australia.
F Corresponding author. Email: nathan.odgers@sydney.edu.au
Soil Research 53(8) 881-894 https://doi.org/10.1071/SR14274
Submitted: 1 October 2014 Accepted: 31 March 2015 Published: 29 July 2015
Abstract
It is increasingly necessary to apply quantitative techniques to legacy soil polygon maps given that legacy soil maps may be the only source of soil information over large areas. Spatial disaggregation provides a means of extracting information from legacy soil maps and enables us to downscale the original information to produce new soil class maps at finer levels of detail. This is a useful outcome in its own right; however, the disaggregated soil-class coverage can also be used to make digital maps of soil properties with associated estimates of uncertainty. In this work, we take the spatially disaggregated soil-class coverage for all of Western Australia and the agricultural region of South Australia and demonstrate its application in mapping clay content at six depth intervals in the soil profile. Estimates of uncertainty are provided in the form of the 90% prediction interval. The work can be considered an example of harmonisation to a common output specification. The validation results highlighted areas in the landscape and taxonomic spaces where more knowledge of soil properties is necessary.
Additional keywords: clay content, digital soil mapping, legacy soil data, prediction interval, spatial disaggregation, weighted mean.
References
Arnold RW (1966) Differentiation of soils having cyclic or recurring horizons. Canadian Journal of Soil Science 46, 69–74.| Differentiation of soils having cyclic or recurring horizons.Crossref | GoogleScholarGoogle Scholar |
Bishop TFA, Daniel R, Guest DI, Nelson MA, Chang C (2012) A digital soil map of Phytophthora cinnamomi in the Gondwana Rainforests of eastern Australia. In ‘Digital Soil Assessments and Beyond: Proceedings of the Fifth Global Workshop on Digital Soil Mapping’. Sydney, NSW, 10–13 April 2012. (Eds B Minasny, BP Malone, AB McBratney) pp. 65–68. (Taylor & Francis: London)
Bui EN, Moran CJ (2001) Disaggregation of polygons of surficial geology and soil maps using spatial modelling and legacy data. Geoderma 103, 79–94.
| Disaggregation of polygons of surficial geology and soil maps using spatial modelling and legacy data.Crossref | GoogleScholarGoogle Scholar |
Carré F, McBratney AB, Minasny B (2007) Estimation and potential improvement of the quality of legacy soil samples for digital soil mapping. Geoderma 141, 1–14.
| Estimation and potential improvement of the quality of legacy soil samples for digital soil mapping.Crossref | GoogleScholarGoogle Scholar |
Clifford D, Guo Y (2015) Combining two soil property rasters using an adaptive gating approach. Soil Research 53, 907–912.
De Benedetto D, Castrignanò A, Sollitto D, Modugno F, Buttafuoco G, Io Papa G (2012) Integrating geophysical and geostatistical techniques to map the spatial variation of clay. Geoderma 171–172, 53–63.
| Integrating geophysical and geostatistical techniques to map the spatial variation of clay.Crossref | GoogleScholarGoogle Scholar |
DEWNR (2014a) SA-format soil and land attributes. Department of Environment, Water and Natural Resources, Adelaide, S. Aust. Available at: www.environment.sa.gov.au/Science/Information_data/soil-and-land/describing-soil-land/sa-format (accessed 1 August 2014).
DEWNR (2014b) National-format soil and land attributes. Department of Environment, Water and Natural Resources, Adelaide, S. Aust. Available at: www.environment.sa.gov.au/Knowledge_Bank/Information_data/soil-and-land/describing-soil-land/national-format (accessed 4 August 2014).
FAO (2014) World Reference Base For Soil Resources. World Soil Resources Reports No. 106. (Food and Agriculture Organisation of the United Nations: Rome)
Galbraith JM, Kleinman PJA, Bryant RB (2003) Sources of uncertainty affecting soil organic carbon estimates in northern New York. Soil Science Society of America Journal 67, 1206–1212.
| Sources of uncertainty affecting soil organic carbon estimates in northern New York.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD3sXlslCqtbk%3D&md5=2fddf7cbca43ea4c5f0b0c9344b94415CAS |
Gallichand J, Marcotte D (1993) Mapping clay content for subsurface drainage in the Nile Delta. Geoderma 58, 165–179.
| Mapping clay content for subsurface drainage in the Nile Delta.Crossref | GoogleScholarGoogle Scholar |
Gibbons FR (1983) Soil mapping in Australia. In ‘Soils: an Australian viewpoint’. pp. 267–276. (CSIRO/Academic Press: Melbourne/London)
GlobalSoilMap Science Committee (2013) Specifications: Tiered GlobalSoilMap.net Products, Release 2.3. http://globalsoilmap.net/specifications
Gomez C, Lagacherie P, Bacha S (2012) Using Vis-NIR hyperspectral data to map topsoil properties over bare soils in the Cap Bon region, Tunisia. In ‘Digital soil assessments and beyond: Proceedings of the Fifth Global Workshop on Digital Soil Mapping’. 10–13 April 2012, Sydney, NSW. (Eds B Minasny, BP Malone, AB McBratney) pp. 387–392. (Taylor & Francis: London)
Grundy MJ, Viscarra Rossel RA, Searle RD, Wilson PL, Chen C, Gregory LJ (2015) Soil and Landscape Grid of Australia. Soil Research 53, 835–844.
Hall JAS, Maschmedt DJ, Billing NB (2009) ‘The soils of southern South Australia. Geological Survey of South Australia.’ The South Australian Land and Soil Book Series No. 1. (Government of South Australia: Adelaide)
Häring T, Dietz E, Osenstetter S, Koschitzki T, Schröder B (2012) Spatial disaggregation of complex soil map units: A decision-tree based approach in Bavarian forest soils. Geoderma 185–186, 37–47.
| Spatial disaggregation of complex soil map units: A decision-tree based approach in Bavarian forest soils.Crossref | GoogleScholarGoogle Scholar |
Helmick JL, Nauman TW, Thompson JA (2014) Developing and assessing prediction intervals for soil property maps derived from legacy databases. In ‘GlobalSoilMap: Basis of the global spatial soil information system’. (Eds D Arrouays, NJ McKenzie, JW Hempel, AC Richer de Forges, AB McBratney) pp. 359–366. (Taylor & Francis: London)
Henderson BL, Bui EN, Moran CJ, Simon DAP (2005) Australia-wide predictions of soil properties using decision trees. Geoderma 124, 383–398.
| Australia-wide predictions of soil properties using decision trees.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2cXhtVKmtbbO&md5=85bf65dd9a18d538ff4b143f26c61af2CAS |
Holmes KW, Odgers NP, Griffin EA, van Gool D (2014) Spatial disaggregation of conventional soil mapping across Western Australia using DSMART. In ‘GlobalSoilMap: Basis of the global spatial soil information system’. (Eds D Arrouays, NJ McKenzie, JW Hempel, AC Richer de Forges, AB McBratney) pp. 273–279. (Taylor & Francis: London)
Holmes KW, Griffin EA, Odgers NP (2015) Large-area spatial disaggregation of a mosaic of conventional soil maps: evaluation over Western Australia. Soil Research 53, 865–880.
Hong SY, Kim YH, Han KH, Hyun BK, Zhang YS, Song KC, Minasny B, McBratney AB (2012). Digital soil mapping of soil properties for Korean soils. In ‘Digital Soil Assessments and Beyond: Proceedings of the Fifth Global Workshop on Digital Soil Mapping’. Sydney, Australia, 10–13 April 2012. (Eds B Minasny, BP Malone, AB McBratney) pp. 435–438. (Taylor & Francis: London)
Huang J, Wong VNL, Triantafilis J (2014) Mapping soil salinity and pH across an estuarine and alluvial plain using electromagnetic and digital elevation model data. Soil Use and Management 30, 394–402.
| Mapping soil salinity and pH across an estuarine and alluvial plain using electromagnetic and digital elevation model data.Crossref | GoogleScholarGoogle Scholar |
Isbell RF (1996) ‘The Australian Soil Classification.’ (CSIRO Publishing: Melbourne)
Khosravi A, Mazloumi E, Nahavandi S, Creighton D, van Lint JWC (2011) A genetic algorithm-based method for improving quality of travel time prediction intervals. Transportation Research Part C, Emerging Technologies 19, 1364–1376.
| A genetic algorithm-based method for improving quality of travel time prediction intervals.Crossref | GoogleScholarGoogle Scholar |
Kidd DB, Malone BP, McBratney AB, Minasny B, Webb MA (2014) Digital mapping of a soil drainage index for irrigated enterprise suitability in Tasmania, Australia. Soil Research 52, 107–119.
| Digital mapping of a soil drainage index for irrigated enterprise suitability in Tasmania, Australia.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC2cXltFehu74%3D&md5=396f4d6beec94545ff38e676cbe52eb4CAS |
Kotz S, van Dorp JR (2004) ‘Beyond beta: other continuous families of distributions with bounded support and applications.’ (World Scientific: Singapore)
Lamsal S, Mishra U (2010) Mapping soil textural fractions across a large watershed in north-east Florida. Journal of Environmental Management 91, 1686–1694.
| Mapping soil textural fractions across a large watershed in north-east Florida.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BC3czns12rtA%3D%3D&md5=c57d90cd6eda8f3a3de8d58ee831ba0aCAS | 20434829PubMed |
Lawes RA, Oliver YM, Robertson MJ (2009) Integrating the effects of climate and plant available soil water holding capacity on wheat yield. Field Crops Research 113, 297–305.
| Integrating the effects of climate and plant available soil water holding capacity on wheat yield.Crossref | GoogleScholarGoogle Scholar |
Lin LI-K (1989) A concordance correlation coefficient to evaluate reproducibility. Biometrics 45, 255–268.
| A concordance correlation coefficient to evaluate reproducibility.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DyaL1M3kslKrtg%3D%3D&md5=2fd39f8449b2a4d74b7d3b7a787153a2CAS |
Lotspeich FB (1980) Watersheds as the basic ecosystem: this conceptual framework provides a basis for a natural classification system. Journal of the American Water Resources Association 16, 581–586.
| Watersheds as the basic ecosystem: this conceptual framework provides a basis for a natural classification system.Crossref | GoogleScholarGoogle Scholar |
Malone BP, McBratney AB, Minasny B, Laslett GM (2009) Mapping continuous depth functions of soil carbon storage and available water capacity. Geoderma 154, 138–152.
| Mapping continuous depth functions of soil carbon storage and available water capacity.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXhsVCrs7rM&md5=e99b1098b6d08b9d8b5e7880903ba8bcCAS |
Malone BP, McBratney AB, Minasny B (2011) Empirical estimates of uncertainty for mapping continuous depth functions of soil attributes. Geoderma 160, 614–626.
| Empirical estimates of uncertainty for mapping continuous depth functions of soil attributes.Crossref | GoogleScholarGoogle Scholar |
McDonald RC, Isbell RF (2009) Soil profile. In ‘Australian soil and land survey field handbook’. 3rd edn. pp. 147–204. (CSIRO Publishing: Melbourne)
McKenzie NJ, Jacquier D, Maschmedt DJ, Griffin EA, Brough DM (2012) ‘The Australian Soil Resource Information System (ASRIS) technical specifications (Revised Version 1.6, June 2012).’ Australian Collaborative Land Evaluation Program, Canberra, ACT. (CSIRO, Department of Agriculture: Canberra, ACT)
Minasny B, McBratney AB, Lark RM (2008) Digital soil mapping technologies for countries with sparse data infrastructures. In ‘Digital soil mapping with limited data’. (Eds AE Hartemink, AB McBratney, M de L Mendonça Santos) pp. 15–30. (Springer: Berlin, Heidelberg)
Nauman TW, Thompson JA (2014) Semi-automated disaggregation of conventional soil maps using knowledge driven data mining and classification trees. Geoderma 213, 385–399.
| Semi-automated disaggregation of conventional soil maps using knowledge driven data mining and classification trees.Crossref | GoogleScholarGoogle Scholar |
Odeh IOA, Crawford M, McBratney AB (2006) Digital mapping of soil attributes for regional and catchment modelling, using ancillary covariates, statistical and geostatistical techniques. In ‘Digital soil mapping: an introductory perspective’. Developments in Soil Science 31. (Eds P Lagacherie, AB McBratney, M Voltz) pp. 437–453. (Elsevier: Amsterdam)
Odgers NP, Libohova Z, Thompson JA (2012) Equal-area spline functions applied to a legacy soil database to create weighted-means maps of soil organic carbon at a continental scale. Geoderma 189–190, 153–163.
| Equal-area spline functions applied to a legacy soil database to create weighted-means maps of soil organic carbon at a continental scale.Crossref | GoogleScholarGoogle Scholar |
Odgers NP, McBratney AB, Minasny B (2014a) Digital soil property mapping and uncertainty estimation using soil class probability rasters. In ‘GlobalSoilMap: basis of the global spatial soil information system’. (Eds D Arrouays, NJ McKenzie, JW Hempel, A Richer de Forges, AB McBratney) pp. 341–346. (Taylor & Francis: London)
Odgers NP, Sun W, McBratney AB, Minasny B, Clifford D (2014b) Disaggregating and harmonising soil map units through resampled classification trees. Geoderma 214–215, 91–100.
| Disaggregating and harmonising soil map units through resampled classification trees.Crossref | GoogleScholarGoogle Scholar |
Odgers NP, McBratney AB, Minasny B (2015) Digital soil property mapping and uncertainty estimation using soil class probability rasters. Geoderma 237–238, 190–198.
| Digital soil property mapping and uncertainty estimation using soil class probability rasters.Crossref | GoogleScholarGoogle Scholar |
Pásztor L, Szabó J, Bakacsi Z (2010) Digital processing and upgrading of legacy data collected during the 1 : 25,000 scale Kreybig soil survey. Acta Geodaetica et Geophysica Hungarica 45, 127–136.
| Digital processing and upgrading of legacy data collected during the 1 : 25,000 scale Kreybig soil survey.Crossref | GoogleScholarGoogle Scholar |
Protz R, Presant EW, Arnold RW (1968) Establishment of the modal profile and measurement of variability within a soil landform unit. Canadian Journal of Soil Science 48, 7–19.
| Establishment of the modal profile and measurement of variability within a soil landform unit.Crossref | GoogleScholarGoogle Scholar |
Schoknecht N, Pathan S (2013) Soil groups of Western Australia. Resource Management Technical Report No. 380. Department of Agriculture and Food Western Australia, South Perth, W. Aust.
Schoknecht N, Tille P, Purdie B (2004) Soil-landscape mapping in south-western Australia. Resource Management Technical Report No. 280. Department of Agriculture and Food Western Australia, South Perth, W. Aust.
Shrestha DL, Solomatine DP (2006) Machine learning approaches for estimation of prediction interval for the model output. Neural Networks 19, 225–235.
| Machine learning approaches for estimation of prediction interval for the model output.Crossref | GoogleScholarGoogle Scholar | 16530384PubMed |
Soil Survey Staff (1999) ‘Soil taxonomy: a basic system of soil classification for making and interpreting soil surveys.’ 2nd edn (United States Department of Agriculture Natural Resources Conservation Service: Washington, DC)
Soil Survey Staff (2006) ‘U.S. General Soil Map (STATSGO2).’ (Natural Resources Conservation Service, United States Department of Agriculture: Washington, DC).
Subburayalu S, Jenhani I, Slater BK (2014) Disaggregation of component soil series using possibilistic decision trees from an Ohio County soil survey map. Geoderma 213, 334–345.
| Disaggregation of component soil series using possibilistic decision trees from an Ohio County soil survey map.Crossref | GoogleScholarGoogle Scholar |
Sun W, McBratney AB, Hempel JW, Minasny B, Malone BP, D’Avello T, Burras L, Thompson JA (2010) Digital harmonisation of adjacent analogue soil survey areas—4. Iowa counties. In ‘Proceedings of the 4th Global Workshop on Digital Soil Mapping’. Rome. (International Society of Soil Sciences)
Taylor JK (1970) The development of soil survey and field pedology in Australia, 1927–67. (CSIRO: Melbourne)
Thompson JA, Prescott T, Moore AC, Bell J, Kautz DR, Hempel JW, Waltman SW, Perry CH (2010) Regional approach to soil property mapping using legacy data and spatial disaggregation techniques. In ‘19th World Congress of Soil Science’. Brisbane, Queensland. (International Society of Soil Sciences)
Tranter G, Minasny B, McBratney AB (2010) Estimating pedotransfer function prediction limits using fuzzy k-means with extragrades. Soil Science Society of America Journal 74, 1967–1975.
| Estimating pedotransfer function prediction limits using fuzzy k-means with extragrades.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXhsFahtr3N&md5=b9279bdc15c3d0898fafe7f581595029CAS |
Triantafilis J, Lesch SM (2005) Mapping clay content variation using electromagnetic induction techniques. Computers and Electronics in Agriculture 46, 203–237.
| Mapping clay content variation using electromagnetic induction techniques.Crossref | GoogleScholarGoogle Scholar |
Triantafilis J, Huckel AI, Odeh IOA (2001) Comparison of statistical prediction methods for estimating field-scale clay content using different combinations of ancillary variables. Soil Science 166, 415–427.
| Comparison of statistical prediction methods for estimating field-scale clay content using different combinations of ancillary variables.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD3MXkvFWhu7g%3D&md5=b6b67cfe97a8050ef565ab752e7ef0b3CAS |
Viscarra Rossel RA, Chen C, Grundy MJ, Searle R, Clifford D, Campbell PH (2015) The Australian three-dimensional soil grid: Australia’s contribution to the GlobalSoilMap project. Soil Research 53, 845–864.
Zhao Z, Chow TL, Rees HW, Yang Q, Xing Z, Meng F-R (2009) Predict soil texture distributions using an artificial neural network model. Computers and Electronics in Agriculture 65, 36–48.
| Predict soil texture distributions using an artificial neural network model.Crossref | GoogleScholarGoogle Scholar |
Zhu AX, Band LE (1994) A knowledge-based approach to data integration for soil mapping. Canadian Journal of Remote Sensing 20, 408–418.
| A knowledge-based approach to data integration for soil mapping.Crossref | GoogleScholarGoogle Scholar |
Zhu AX, Band LE, Vertessy R, Dutton B (1997) Derivation of soil properties using a Soil Land Inference Model (SoLIM). Soil Science Society of America Journal 61, 523–533.
| Derivation of soil properties using a Soil Land Inference Model (SoLIM).Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaK2sXjtVyqtL8%3D&md5=dd1551bc66e743d6620be23d981bddbcCAS |
Zhu AX, Hudson B, Burt JE, Lubich K, Simonson D (2001) Soil mapping using GIS, expert knowledge, and fuzzy logic. Soil Science Society of America Journal 65, 1463–1472.
| Soil mapping using GIS, expert knowledge, and fuzzy logic.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD38XptlWn&md5=d94209a344a59728bbaac5f64fba2ed4CAS |
