By default, a brief bit of metadata is printed to the console when
+you call the soil thickness object in your R session.
But, {read.abares} provides a function for you to browse the soil
+thickness metadata in your console.
+library(read.abares)
+get_soil_thickness(cache = TRUE) |>
+ print_soil_thickness_metadata()
+#>
+#> ── Soil Thickness for Australian areas of intensive agriculture of Layer 1 (A Horizon - top-so
+#>
+#> ── Dataset ANZLIC ID ANZCW1202000149 ──
+#>
+#> Dataset ANZLIC ID ANZCW1202000149
+#>
+#> Title Soil Thickness for Australian areas of intensive agriculture of Layer 1 (A Horizon -
+#> top-soil) (derived from soil mapping)
+#>
+#> Custodian CSIRO, Land & Water
+#>
+#> Jurisdiction Australia
+#>
+#> Description Abstract Surface of predicted Thickness of soil layer 1 (A Horizon - top-soil)
+#> surface for the intensive agricultural areas of Australia. Data modelled from area based
+#> observations made by soil agencies both State and CSIRO and presented as .0.01 degree grid
+#> cells.
+#>
+#> Topsoils (A horizons) are defined as the surface soil layers in which organic matter
+#> accumulates, and may include dominantly organic surface layers (O and P horizons).
+#>
+#> The depth of topsoil is important because, with their higher organic matter contents,
+#> topsoils (A horizon) generally have more suitable properties for agriculture, including
+#> higher permeability and higher levels of soil nutrients.
+#>
+#> Estimates of soil depths are needed to calculate the amount of any soil constituent in either
+#> volume or mass terms (bulk density is also needed) - for example, the volume of water stored
+#> in the rooting zone potentially available for plant use, to assess total stores of soil
+#> carbon for Greenhouse inventory or to assess total stores of nutrients.
+#>
+#> The pattern of soil depth is strongly related to topography - the shape and slope of the
+#> land. Deeper soils are typically found in the river valleys where soils accumulate on
+#> floodplains and at the footslopes of ranges (zones of deposition), while soils on hillslopes
+#> (zones of erosion) tend to be shallow. Map of thickness of topsoil was derived from soil map
+#> data and interpreted tables of soil properties for specific soil groups.
+#>
+#> The quality of data on soil depth in existing soil profile datasets is questionable and as
+#> the thickness of soil horizons varies locally with topography, values for map units are
+#> general averages.
+#>
+#> The final ASRIS polygon attributed surfaces are a mosaic of all of the data obtained from
+#> various state and federal agencies. The surfaces have been constructed with the best
+#> available soil survey information available at the time. The surfaces also rely on a number
+#> of assumptions. One being that an area weighted mean is a good estimate of the soil
+#> attributes for that polygon or map-unit. Another assumption made is that the look-up tables
+#> provided by McKenzie et al. (2000), state and territories accurately depict the soil
+#> attribute values for each soil type.
+#>
+#> The accuracy of the maps is most dependent on the scale of the original polygon data sets and
+#> the level of soil survey that has taken place in each state. The scale of the various soil
+#> maps used in deriving this map is available by accessing the data-source grid, the scale is
+#> used as an assessment of the likely accuracy of the modelling. The Atlas of Australian Soils
+#> is considered to be the least accurate dataset and has therefore only been used where there
+#> is no state based data. Of the state datasets Western Australian sub-systems, South
+#> Australian land systems and NSW soil landscapes and reconnaissance mapping would be the most
+#> reliable based on scale. NSW soil landscapes and reconnaissance mapping use only one dominant
+#> soil type per polygon in the estimation of attributes. South Australia and Western Australia
+#> use several soil types per polygon or map-unit.
+#>
+#> The digital map data is provided in geographical coordinates based on the World Geodetic
+#> System 1984 (WGS84) datum. This raster data set has a grid resolution of 0.001 degrees
+#> (approximately equivalent to 1.1 km).
+#>
+#> The data set is a product of the National Land and Water Resources Audit (NLWRA) as a base
+#> dataset.
+#>
+#> Search Word(s) AGRICULTURE SOIL Physics Models
+#>
+#> Geographic Extent Name(s) GEN Category
+#>
+#> GEN Custodial Jurisdiction
+#>
+#> GEN Name
+#>
+#> Geographic Bounding Box North Bounding Latitude -10.707149 South Bounding Latitude -43.516831
+#> East Bounding Longitude 113.19673 West Bounding Longitude 153.990779
+#>
+#> Geographic Extent Polygon(s) 115.0 -33.5,115.7 -33.3,115.7 -31.7,113.2 -26.2,113.5
+#> -25.4,114.1 -26.4,114.3 -26.0,113.4 -24.3,114.1 -21.8,122.3 -18.2,122.2 -17.2,126.7
+#> -13.6,129.1 -14.9,130.6 -12.3,132.6 -12.1,132.5 -11.6,131.9 -11.3,132.0 -11.1,137.0
+#> -12.2,135.4 -14.7,140.0 -17.7,140.8 -17.4,141.7 -15.1,141.4 -13.7,142.2 -10.9,142.7
+#> -10.7,143.9 -14.5,144.6 -14.1,145.3 -14.9,146.3 -18.8,148.9 -20.5,150.9 -22.6,153.2
+#> -25.9,153.7 -28.8,153.0 -31.3,150.8 -34.8,150.0 -37.5,147.8 -37.9,146.3 -39.0,144.7
+#> -38.4,143.5 -38.8,141.3 -38.4,139.7 -37.3,139.7 -36.9,139.9 -36.7,138.9 -35.5,138.1
+#> -35.7,138.6 -34.7,138.1 -34.2,137.8 -35.1,136.9 -35.3,137.0 -34.9,137.5 -34.9,137.4
+#> -34.0,137.9 -33.5,137.8 -32.6,137.3 -33.6,135.9 -34.7,136.1 -34.8,136.0 -35.0,135.1
+#> -34.6,135.2 -34.5,135.4 -34.5,134.7 -33.3,134.0 -32.9,133.7 -32.1,133.3 -32.2,132.2
+#> -32.0,131.3 -31.5,127.3 -32.3,126.0 -32.3,123.6 -33.9,123.2 -34.0,122.1 -34.0,121.9
+#> -33.8,119.9 -34.0,119.6 -34.4,118.0 -35.1,116.0 -34.8,115.0 -34.3,115.0 -33.5
+#>
+#> 147.8 -42.9,147.9 -42.6,148.2 -42.1,148.3 -42.3,148.3 -41.3,148.3 -41.0,148.0 -40.7,147.4
+#> -41.0,146.7 -41.1,146.6 -41.2,146.5 -41.1,146.4 -41.2,145.3 -40.8,145.3 -40.7,145.2
+#> -40.8,145.2 -40.8,145.2 -40.8,145.0 -40.8,144.7 -40.7,144.7 -41.2,145.2 -42.2,145.4
+#> -42.2,145.5 -42.4,145.5 -42.5,145.2 -42.3,145.5 -43.0,146.0 -43.3,146.0 -43.6,146.9
+#> -43.6,146.9 -43.5,147.1 -43.3,147.0 -43.1,147.2 -43.3,147.3 -42.8,147.4 -42.9,147.6
+#> -42.8,147.5 -42.8,147.8 -42.9,147.9 -43.0,147.7 -43.0,147.8 -43.2,147.9 -43.2,147.9
+#> -43.2,148.0 -43.2,148.0 -43.1,148.0 -42.9,147.8 -42.9
+#>
+#> 136.7 -13.8,136.7 -13.7,136.6 -13.7,136.6 -13.8,136.4 -13.8,136.4 -14.1,136.3 -14.2,136.9
+#> -14.3,137.0 -14.2,136.9 -14.2,136.7 -14.1,136.9 -13.8,136.7 -13.8,136.7 -13.8
+#>
+#> 139.5 -16.6,139.7 -16.5,139.4 -16.5,139.2 -16.7,139.3 -16.7,139.5 -16.6
+#>
+#> 153.0 -25.2,153.0 -25.7,153.1 -25.8,153.4 -25.0,153.2 -24.7,153.2 -25.0,153.0 -25.2
+#>
+#> 137.5 -36.1,137.7 -35.9,138.1 -35.9,137.9 -35.7,137.6 -35.7,137.6 -35.6,136.6 -35.8,136.7
+#> -36.1,137.2 -36.0,137.5 -36.1
+#>
+#> 143.9 -39.7,144.0 -39.6,144.1 -39.8,143.9 -40.2,143.9 -40.0,143.9 -39.7
+#>
+#> 148.0 -39.7,147.7 -39.9,147.9 -39.9,148.0 -40.1,148.1 -40.3,148.3 -40.2,148.3 -40.0,148.0
+#> -39.7
+#>
+#> 148.1 -40.4,148.0 -40.4,148.4 -40.3,148.4 -40.5,148.1 -40.4
+#>
+#> 130.4 -11.3,130.4 -11.2,130.6 -11.3,130.7 -11.4,130.9 -11.3,131.0 -11.4,131.1 -11.3,131.2
+#> -11.4,131.3 -11.2,131.5 -11.4,131.5 -11.5,131.0 -11.9,130.8 -11.8,130.6 -11.7,130.0
+#> -11.8,130.1 -11.7,130.3 -11.7,130.1 -11.5,130.4 -11.3
+#>
+#> Data Currency Beginning date 1999-09-01
+#>
+#> Ending date 2001-03-31
+#>
+#> Dataset Status Progress COMPLETE
+#>
+#> Maintenance and Update Frequency NOT PLANNED
+#>
+#> Access Stored Data Format DIGITAL - ESRI Arc/Info integer GRID
+#>
+#> Available Format Type DIGITAL - ESRI Arc/Info integer GRID
+#>
+#> Access Constraint Subject to the terms & condition of the data access & management agreement
+#> between the National Land & Water Audit and ANZLIC parties
+#>
+#> Data Quality Lineage The soil attribute surface was created using the following datasets 1.
+#> The digital polygon coverage of the Soil-Landforms of the Murray Darling Basis (MDBSIS)(Bui
+#> et al. 1998), classified as principal profile forms (PPF's) (Northcote 1979). 2. The digital
+#> Atlas of Australian Soils (Northcote et al.1960-1968)(Leahy, 1993). 3. Western Australia land
+#> systems coverage (Agriculture WA). 4. Western Australia sub-systems coverage (Agriculture
+#> WA). 5. Ord river catchment soils coverage (Agriculture WA). 6. Victoria soils coverage
+#> (Victorian Department of Natural Resources and Environment - NRE). 7. NSW Soil Landscapes and
+#> reconnaissance soil landscape mapping (NSW Department of Land and Water Conservation - DLWC).
+#> 8. New South Wales Land systems west (NSW Department of Land and Water Conservation - DLWC).
+#> 9. South Australia soil land-systems (Primary Industries and Resources South Australia -
+#> PIRSA). 10. Northern Territory soils coverage (Northern Territory Department of Lands,
+#> Planning and Environment). 11. A mosaic of Queensland soils coverages (Queensland Department
+#> of Natural Resources - QDNR). 12. A look-up table linking PPF values from the Atlas of
+#> Australian Soils with interpreted soil attributes (McKenzie et al. 2000). 13. Look_up tables
+#> provided by WA Agriculture linking WA soil groups with interpreted soil attributes. 14.
+#> Look_up tables provided by PIRSA linking SA soil groups with interpreted soil attributes.
+#>
+#> The continuous raster surface representing Thickness of soil layer 1 was created by combining
+#> national and state level digitised land systems maps and soil surveys linked to look-up
+#> tables listing soil type and corresponding attribute values.
+#>
+#> Because thickness is used sparingly in the Factual Key, estimations of thickness in the
+#> look-up tables were made using empirical correlations for particular soil types.
+#>
+#> To estimate a soil attribute where more than one soil type was given for a polygon or
+#> map-unit, the soil attribute values related to each soil type in the look-up table were
+#> weighted according to the area occupied by that soil type within the polygon or map-unit. The
+#> final soil attribute values are an area weighted average for a polygon or map-unit. The
+#> polygon data was then converted to a continuous raster surface using the soil attribute
+#> values calculated for each polygon.
+#>
+#> The ASRIS soil attribute surfaces created using polygon attribution relied on a number of
+#> data sets from various state agencies. Each polygon data set was turned into a continuous
+#> surface grid based on the calculated soil attribute value for that polygon. The grids where
+#> then merged on the basis that, where available, state data replaced the Atlas of Australian
+#> Soils and MDBSIS. MDBSIS derived soil attribute values were restricted to areas where MDBSIS
+#> was deemed to be more accurate that the Atlas of Australian Soils (see Carlile et al (2001a).
+#>
+#> In cases where a soil type was missing from the look-up table or layer 2 did not exist for
+#> that soil type, the percent area of the soils remaining were adjusted prior to calculating
+#> the final soil attribute value. The method used to attribute polygons was dependent on the
+#> data supplied by individual State agencies.
+#>
+#> The modelled grid was resampled from 0.0025 degree cells to 0.01 degree cells using bilinear
+#> interpolation
+#>
+#> Positional Accuracy The predictive surface is a 0.01 X 0.01 degree grid and has a locational
+#> accurate of about 1m.
+#>
+#> The positional accuracy of the defining polygons have variable positional accuracy most
+#> locations are expected to be within 100m of the recorded location. The vertical accuracy is
+#> not relevant. The positional assessment has been made by considering the tools used to
+#> generate the locational information and contacting the data providers.
+#>
+#> The other parameters used in the production of the led surface have a range of positional
+#> accuracy ranging from + - 50 m to + - kilometres. This contribute to the loss of attribute
+#> accuracy in the surface.
+#>
+#> Attribute Accuracy Input attribute accuracy for the areas is highly variable. The predictive
+#> has a variable and much lower attribute accuracy due to the irregular distribution and the
+#> limited positional accuracy of the parameters used for modelling.
+#>
+#> There are several sources of error in estimating soil depth and thickness of horizons for the
+#> look-up tables. Because thickness is used sparingly in the Factual Key, estimations of
+#> thickness in the look-up tables were made using empirical correlations for particular soil
+#> types. The quality of data on soil depth in existing soil profile datasets is questionable,
+#> in soil mapping, thickness of soil horizons varies locally with topography, so values for map
+#> units are general averages. The definition of the depth of soil or regolith is imprecise and
+#> it can be difficult to determine the lower limit of soil.
+#>
+#> The assumption made that an area weighted mean of soil attribute values based on soil type is
+#> a good estimation of a soil property is debatable, in that it does not supply the soil
+#> attribute value at any given location. Rather it is designed to show national and regional
+#> patterns in soil properties. The use of the surfaces at farm or catchment scale modelling may
+#> prove inaccurate. Also the use of look-up tables to attribute soil types is only as accurate
+#> as the number of observations used to estimate a attribute value for a soil type. Some soil
+#> types in the look-up tables may have few observations, yet the average attribute value is
+#> still taken as the attribute value for that soil type. Different states are using different
+#> taxonomic schemes making a national soil database difficult. Another downfall of the area
+#> weighted approach is that some soil types may not be listed in look-up tables. If a soil type
+#> is a dominant one within a polygon or map-unit, but is not listed within the look-up table or
+#> is not attributed within the look-up table then the final soil attribute value for that
+#> polygon will be biased towards the minor soil types that do exist. This may also happen when
+#> a large area is occupied by a soil type which has no B horizon. In this case the final soil
+#> attribute value will be area weighted on the soils with a B horizon, ignoring a major soil
+#> type within that polygon or map-unit. The layer 2 surfaces have large areas of no-data
+#> because all soils listed for a particular map-unit or polygon had no B horizon.
+#>
+#> Logical Consistency Surface is fully logically consistent as only one parameter is shown, as
+#> predicted average Soil Thickness within each grid cell
+#>
+#> Completeness Surface is nearly complete. There are some areas (about %1 missing) for which
+#> insufficient parameters were known to provide a useful prediction and thus attributes are
+#> absent in these areas.
+#>
+#> Contact Information Contact Organisation (s) CSIRO, Land & Water
+#>
+#> Contact Position Project Leader
+#>
+#> Mail Address ACLEP, GPO 1666
+#>
+#> Locality Canberra
+#>
+#> State ACT
+#>
+#> Country AUSTRALIA
+#>
+#> Postcode 2601
+#>
+#> Telephone 02 6246 5922
+#>
+#> Facsimile 02 6246 5965
+#>
+#> Electronic Mail Address neil.mckenzie@cbr.clw.csiro.au
+#>
+#> Metadata Date Metadata Date 2001-07-01
+#>
+#> Additional Metadata Additional Metadata
+#>
+#> Entity and Attributes Entity Name Soil Thickness Layer 1 (derived from mapping)
+#>
+#> Entity description Estimated Soil Thickness (mm) of Layer 1 on a cell by cell basis
+#>
+#> Feature attribute name VALUE
+#>
+#> Feature attribute definition Predicted average Thickness (mm) of soil layer 1 in the 0.01 X
+#> 0.01 degree quadrat
+#>
+#> Data Type Spatial representation type RASTER
+#>
+#> Projection Map projection GEOGRAPHIC
+#>
+#> Datum WGS84
+#>
+#> Map units DECIMAL DEGREES
+#>
+#> Scale Scale/ resolution 1:1 000 000
+#>
+#> Usage Purpose Estimates of soil depths are needed to calculate the amount of any soil
+#> constituent in either volume or mass terms (bulk density is also needed) - for example, the
+#> volume of water stored in the rooting zone potentially available for plant use, to assess
+#> total stores of soil carbon for Greenhouse inventory or to assess total stores of nutrients.
+#>
+#> Provide indications of probable Thickness soil layer 1 in agricultural areas where soil
+#> thickness testing has not been carried out
+#>
+#> Use Use Limitation This dataset is bound by the requirements set down by the National Land &
+#> Water Resources Audit
But you can also access it directly and use pander::pander() to
+include it in a document like this vignette.
+
+Dataset ANZLIC ID ANZCW1202000149
+Title Soil Thickness for Australian areas of intensive agriculture of
+Layer 1 (A Horizon - top-soil) (derived from soil mapping)
+Custodian CSIRO, Land & Water
+Jurisdiction Australia
+Description Abstract Surface of predicted Thickness of soil layer 1
+(A Horizon - top-soil) surface for the intensive agricultural areas of
+Australia. Data modelled from area based observations made by soil
+agencies both State and CSIRO and presented as .0.01 degree grid
+cells.
+Topsoils (A horizons) are defined as the surface soil layers in which
+organic matter accumulates, and may include dominantly organic surface
+layers (O and P horizons).
+The depth of topsoil is important because, with their higher organic
+matter contents, topsoils (A horizon) generally have more suitable
+properties for agriculture, including higher permeability and higher
+levels of soil nutrients.
+Estimates of soil depths are needed to calculate the amount of any
+soil constituent in either volume or mass terms (bulk density is also
+needed) - for example, the volume of water stored in the rooting zone
+potentially available for plant use, to assess total stores of soil
+carbon for Greenhouse inventory or to assess total stores of
+nutrients.
+The pattern of soil depth is strongly related to topography - the
+shape and slope of the land. Deeper soils are typically found in the
+river valleys where soils accumulate on floodplains and at the
+footslopes of ranges (zones of deposition), while soils on hillslopes
+(zones of erosion) tend to be shallow. Map of thickness of topsoil was
+derived from soil map data and interpreted tables of soil properties for
+specific soil groups.
+The quality of data on soil depth in existing soil profile datasets
+is questionable and as the thickness of soil horizons varies locally
+with topography, values for map units are general averages.
+The final ASRIS polygon attributed surfaces are a mosaic of all of
+the data obtained from various state and federal agencies. The surfaces
+have been constructed with the best available soil survey information
+available at the time. The surfaces also rely on a number of
+assumptions. One being that an area weighted mean is a good estimate of
+the soil attributes for that polygon or map-unit. Another assumption
+made is that the look-up tables provided by McKenzie et al. (2000),
+state and territories accurately depict the soil attribute values for
+each soil type.
+The accuracy of the maps is most dependent on the scale of the
+original polygon data sets and the level of soil survey that has taken
+place in each state. The scale of the various soil maps used in deriving
+this map is available by accessing the data-source grid, the scale is
+used as an assessment of the likely accuracy of the modelling. The Atlas
+of Australian Soils is considered to be the least accurate dataset and
+has therefore only been used where there is no state based data. Of the
+state datasets Western Australian sub-systems, South Australian land
+systems and NSW soil landscapes and reconnaissance mapping would be the
+most reliable based on scale. NSW soil landscapes and reconnaissance
+mapping use only one dominant soil type per polygon in the estimation of
+attributes. South Australia and Western Australia use several soil types
+per polygon or map-unit.
+The digital map data is provided in geographical coordinates based on
+the World Geodetic System 1984 (WGS84) datum. This raster data set has a
+grid resolution of 0.001 degrees (approximately equivalent to 1.1
+km).
+The data set is a product of the National Land and Water Resources
+Audit (NLWRA) as a base dataset.
+Search Word(s) AGRICULTURE
+SOIL Physics Models
+Geographic Extent Name(s) GEN Category
+GEN Custodial Jurisdiction
+GEN Name
+Geographic Bounding Box North Bounding Latitude -10.707149 South
+Bounding Latitude -43.516831 East Bounding Longitude 113.19673 West
+Bounding Longitude 153.990779
+Geographic Extent Polygon(s) 115.0 -33.5,115.7 -33.3,115.7
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+-25.0,153.0 -25.2
+137.5 -36.1,137.7 -35.9,138.1 -35.9,137.9 -35.7,137.6 -35.7,137.6
+-35.6,136.6 -35.8,136.7 -36.1,137.2 -36.0,137.5 -36.1
+143.9 -39.7,144.0 -39.6,144.1 -39.8,143.9 -40.2,143.9 -40.0,143.9
+-39.7
+148.0 -39.7,147.7 -39.9,147.9 -39.9,148.0 -40.1,148.1 -40.3,148.3
+-40.2,148.3 -40.0,148.0 -39.7
+148.1 -40.4,148.0 -40.4,148.4 -40.3,148.4 -40.5,148.1 -40.4
+130.4 -11.3,130.4 -11.2,130.6 -11.3,130.7 -11.4,130.9 -11.3,131.0
+-11.4,131.1 -11.3,131.2 -11.4,131.3 -11.2,131.5 -11.4,131.5 -11.5,131.0
+-11.9,130.8 -11.8,130.6 -11.7,130.0 -11.8,130.1 -11.7,130.3 -11.7,130.1
+-11.5,130.4 -11.3
+Data Currency Beginning date 1999-09-01
+Ending date 2001-03-31
+Dataset Status Progress COMPLETE
+Maintenance and Update Frequency NOT PLANNED
+Access Stored Data Format DIGITAL - ESRI Arc/Info integer GRID
+Available Format Type DIGITAL - ESRI Arc/Info integer GRID
+Access Constraint Subject to the terms & condition of the data
+access & management agreement between the National Land & Water
+Audit and ANZLIC parties
+Data Quality Lineage The soil attribute surface was created using the
+following datasets 1. The digital polygon coverage of the Soil-Landforms
+of the Murray Darling Basis (MDBSIS)(Bui et al. 1998), classified as
+principal profile forms (PPF’s) (Northcote 1979). 2. The digital Atlas
+of Australian Soils (Northcote et al.1960-1968)(Leahy, 1993). 3. Western
+Australia land systems coverage (Agriculture WA). 4. Western Australia
+sub-systems coverage (Agriculture WA). 5. Ord river catchment soils
+coverage (Agriculture WA). 6. Victoria soils coverage (Victorian
+Department of Natural Resources and Environment - NRE). 7. NSW Soil
+Landscapes and reconnaissance soil landscape mapping (NSW Department of
+Land and Water Conservation - DLWC). 8. New South Wales Land systems
+west (NSW Department of Land and Water Conservation - DLWC). 9. South
+Australia soil land-systems (Primary Industries and Resources South
+Australia - PIRSA). 10. Northern Territory soils coverage (Northern
+Territory Department of Lands, Planning and Environment). 11. A mosaic
+of Queensland soils coverages (Queensland Department of Natural
+Resources - QDNR). 12. A look-up table linking PPF values from the Atlas
+of Australian Soils with interpreted soil attributes (McKenzie et
+al. 2000). 13. Look_up tables provided by WA Agriculture linking WA soil
+groups with interpreted soil attributes. 14. Look_up tables provided by
+PIRSA linking SA soil groups with interpreted soil attributes.
+The continuous raster surface representing Thickness of soil layer 1
+was created by combining national and state level digitised land systems
+maps and soil surveys linked to look-up tables listing soil type and
+corresponding attribute values.
+Because thickness is used sparingly in the Factual Key, estimations
+of thickness in the look-up tables were made using empirical
+correlations for particular soil types.
+To estimate a soil attribute where more than one soil type was given
+for a polygon or map-unit, the soil attribute values related to each
+soil type in the look-up table were weighted according to the area
+occupied by that soil type within the polygon or map-unit. The final
+soil attribute values are an area weighted average for a polygon or
+map-unit. The polygon data was then converted to a continuous raster
+surface using the soil attribute values calculated for each polygon.
+The ASRIS soil attribute surfaces created using polygon attribution
+relied on a number of data sets from various state agencies. Each
+polygon data set was turned into a continuous surface grid based on the
+calculated soil attribute value for that polygon. The grids where then
+merged on the basis that, where available, state data replaced the Atlas
+of Australian Soils and MDBSIS. MDBSIS derived soil attribute values
+were restricted to areas where MDBSIS was deemed to be more accurate
+that the Atlas of Australian Soils (see Carlile et al (2001a).
+In cases where a soil type was missing from the look-up table or
+layer 2 did not exist for that soil type, the percent area of the soils
+remaining were adjusted prior to calculating the final soil attribute
+value. The method used to attribute polygons was dependent on the data
+supplied by individual State agencies.
+The modelled grid was resampled from 0.0025 degree cells to 0.01
+degree cells using bilinear interpolation
+Positional Accuracy The predictive surface is a 0.01 X 0.01 degree
+grid and has a locational accurate of about 1m.
+The positional accuracy of the defining polygons have variable
+positional accuracy most locations are expected to be within 100m of the
+recorded location. The vertical accuracy is not relevant. The positional
+assessment has been made by considering the tools used to generate the
+locational information and contacting the data providers.
+The other parameters used in the production of the led surface have a
+range of positional accuracy ranging from + - 50 m to + - kilometres.
+This contribute to the loss of attribute accuracy in the surface.
+Attribute Accuracy Input attribute accuracy for the areas is highly
+variable. The predictive has a variable and much lower attribute
+accuracy due to the irregular distribution and the limited positional
+accuracy of the parameters used for modelling.
+There are several sources of error in estimating soil depth and
+thickness of horizons for the look-up tables. Because thickness is used
+sparingly in the Factual Key, estimations of thickness in the look-up
+tables were made using empirical correlations for particular soil types.
+The quality of data on soil depth in existing soil profile datasets is
+questionable, in soil mapping, thickness of soil horizons varies locally
+with topography, so values for map units are general averages. The
+definition of the depth of soil or regolith is imprecise and it can be
+difficult to determine the lower limit of soil.
+The assumption made that an area weighted mean of soil attribute
+values based on soil type is a good estimation of a soil property is
+debatable, in that it does not supply the soil attribute value at any
+given location. Rather it is designed to show national and regional
+patterns in soil properties. The use of the surfaces at farm or
+catchment scale modelling may prove inaccurate. Also the use of look-up
+tables to attribute soil types is only as accurate as the number of
+observations used to estimate a attribute value for a soil type. Some
+soil types in the look-up tables may have few observations, yet the
+average attribute value is still taken as the attribute value for that
+soil type. Different states are using different taxonomic schemes making
+a national soil database difficult. Another downfall of the area
+weighted approach is that some soil types may not be listed in look-up
+tables. If a soil type is a dominant one within a polygon or map-unit,
+but is not listed within the look-up table or is not attributed within
+the look-up table then the final soil attribute value for that polygon
+will be biased towards the minor soil types that do exist. This may also
+happen when a large area is occupied by a soil type which has no B
+horizon. In this case the final soil attribute value will be area
+weighted on the soils with a B horizon, ignoring a major soil type
+within that polygon or map-unit. The layer 2 surfaces have large areas
+of no-data because all soils listed for a particular map-unit or polygon
+had no B horizon.
+Logical Consistency Surface is fully logically consistent as only one
+parameter is shown, as predicted average Soil Thickness within each grid
+cell
+Completeness Surface is nearly complete. There are some areas (about
+%1 missing) for which insufficient parameters were known to provide a
+useful prediction and thus attributes are absent in these areas.
+Contact Information Contact Organisation (s) CSIRO, Land &
+Water
+Contact Position Project Leader
+Mail Address ACLEP, GPO 1666
+Locality Canberra
+State ACT
+Country AUSTRALIA
+Postcode 2601
+Telephone 02 6246 5922
+Facsimile 02 6246 5965
+Electronic Mail Address neil.mckenzie@cbr.clw.csiro.au
+Metadata Date Metadata Date 2001-07-01
+Additional Metadata Additional Metadata
+Entity and Attributes Entity Name Soil Thickness Layer 1 (derived
+from mapping)
+Entity description Estimated Soil Thickness (mm) of Layer 1 on a cell
+by cell basis
+Feature attribute name VALUE
+Feature attribute definition Predicted average Thickness (mm) of soil
+layer 1 in the 0.01 X 0.01 degree quadrat
+Data Type Spatial representation type RASTER
+Projection Map projection GEOGRAPHIC
+Datum WGS84
+Map units DECIMAL DEGREES
+Scale Scale/ resolution 1:1 000 000
+Usage Purpose Estimates of soil depths are needed to calculate the
+amount of any soil constituent in either volume or mass terms (bulk
+density is also needed) - for example, the volume of water stored in the
+rooting zone potentially available for plant use, to assess total stores
+of soil carbon for Greenhouse inventory or to assess total stores of
+nutrients.
+Provide indications of probable Thickness soil layer 1 in
+agricultural areas where soil thickness testing has not been carried
+out
+Use Use Limitation This dataset is bound by the requirements set down by
+the National Land & Water Resources Audit
+
