Updating a national soil classification with spectroscopic predictions and digital soil mapping
Traditional soil maps have helped us to better understand soil, to form our concepts and to teach and transfer our ideas about it, and so they have been used for many purposes. Although, soil maps are available in many countries, there is a need for them to be updated because they are often deficien...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
Elsevier BV
2018
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| Online Access: | http://hdl.handle.net/20.500.11937/74134 |
| _version_ | 1848763189532033024 |
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| author | Teng, H. Viscarra Rossel, Raphael Shi, Z. Behrens, T. |
| author_facet | Teng, H. Viscarra Rossel, Raphael Shi, Z. Behrens, T. |
| author_sort | Teng, H. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Traditional soil maps have helped us to better understand soil, to form our concepts and to teach and transfer our ideas about it, and so they have been used for many purposes. Although, soil maps are available in many countries, there is a need for them to be updated because they are often deficient in that their spatial delineations and their descriptions are subjective and lack assessments of uncertainty. Updating them is a priority for federal soil surveys worldwide as well as for research, teaching and communication. New data from sensors and quantitative ‘digital’ methods provide us with the tools to do so. Here, we present an approach to update large scale, national soil maps with data derived from a combination of traditional soil profile classifications, classifications made with visible–near infrared (vis–NIR) spectroscopy, and digital soil class mapping (DSM). Our results present an update of the Australian Soil Classification (ASC) orders map. The overall error rate of the DSM model, tested on an independent validation set, was 55.6%, and a few of the orders were poorly classified. We discuss the possible reasons for these errors, but argue that compared to the previous ASC maps, our classification was derived objectively, using currently best available data sets and methods, the classification model was interpretable in terms of the factors of soil formation, the modelling produced a 1 × 1 km resolution soil map with estimates of spatial uncertainty for each soil order and our map has no artefacts at state and territory borders. |
| first_indexed | 2025-11-14T10:59:30Z |
| format | Journal Article |
| id | curtin-20.500.11937-74134 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:59:30Z |
| publishDate | 2018 |
| publisher | Elsevier BV |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-741342019-08-15T05:13:27Z Updating a national soil classification with spectroscopic predictions and digital soil mapping Teng, H. Viscarra Rossel, Raphael Shi, Z. Behrens, T. Traditional soil maps have helped us to better understand soil, to form our concepts and to teach and transfer our ideas about it, and so they have been used for many purposes. Although, soil maps are available in many countries, there is a need for them to be updated because they are often deficient in that their spatial delineations and their descriptions are subjective and lack assessments of uncertainty. Updating them is a priority for federal soil surveys worldwide as well as for research, teaching and communication. New data from sensors and quantitative ‘digital’ methods provide us with the tools to do so. Here, we present an approach to update large scale, national soil maps with data derived from a combination of traditional soil profile classifications, classifications made with visible–near infrared (vis–NIR) spectroscopy, and digital soil class mapping (DSM). Our results present an update of the Australian Soil Classification (ASC) orders map. The overall error rate of the DSM model, tested on an independent validation set, was 55.6%, and a few of the orders were poorly classified. We discuss the possible reasons for these errors, but argue that compared to the previous ASC maps, our classification was derived objectively, using currently best available data sets and methods, the classification model was interpretable in terms of the factors of soil formation, the modelling produced a 1 × 1 km resolution soil map with estimates of spatial uncertainty for each soil order and our map has no artefacts at state and territory borders. 2018 Journal Article http://hdl.handle.net/20.500.11937/74134 10.1016/j.catena.2018.01.015 Elsevier BV restricted |
| spellingShingle | Teng, H. Viscarra Rossel, Raphael Shi, Z. Behrens, T. Updating a national soil classification with spectroscopic predictions and digital soil mapping |
| title | Updating a national soil classification with spectroscopic predictions and digital soil mapping |
| title_full | Updating a national soil classification with spectroscopic predictions and digital soil mapping |
| title_fullStr | Updating a national soil classification with spectroscopic predictions and digital soil mapping |
| title_full_unstemmed | Updating a national soil classification with spectroscopic predictions and digital soil mapping |
| title_short | Updating a national soil classification with spectroscopic predictions and digital soil mapping |
| title_sort | updating a national soil classification with spectroscopic predictions and digital soil mapping |
| url | http://hdl.handle.net/20.500.11937/74134 |