Access API

NSW Imagery Theme

Note: Export function is for the Mosaic Index only

Raster format in currently not delivered via the Spatial Collaboration Portal and this is view only service.
For all imagery supply enquires please contact us via the 'https://aus01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fspatialservices.createsend1.com%2Ft%2Ft-l-mhkddut-l-y%2F&data=04%7C01%7CKate.Wilkinson%40customerservice.nsw.gov.au%7Cfacc21fd20ad4d8bc46808d918f12734%7C1ef97a68e8ab44eda16db579fe2d7cd8%7C0%7C0%7C637568246339024087%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=lCsQT7upTxYnH3%2FwHLqpPWtfLT2%2BViKVsy%2FQijmUCEk%3D&reserved=0' rel='nofollow ugc'>Spatial Services Customer Hub

Please Note
WGS 84 service aligned to GDA94
This dataset has spatial reference [WGS 84 ≈ GDA94] which may result in misalignments when viewed in GDA2020 environments. A similar service with a ‘multiCRS’ suffix is available which can support GDA2020, GDA94 and WGS 84 ≈ GDA2020 environments.
In due course, and allowing time for user feedback and testing, it is intended that the original service name will adopt the new 'multiCRS' functionality.

Property boundaries represent the extent of ownership of an address in line with NSW Valuer General for the purposes of address verification and rating. The spatial layer is derived from land parcel boundaries (cadastre) originally supplied by NSW Spatial Services who remain the source of cadastral information. See also SIX maps. This spatial layer has been significantly changed and maintained by City of Sydney Spatial Services. This layer is not survey accurate. Geoservice API disabled, but geojson and download permitted.

Access API

NSW Imagery Web Service
Note: Export function is for the Mosaic Index only

Please Note
WGS 84 service aligned to GDA94
This dataset has spatial reference [WGS 84 ≈ GDA94] which may result in misalignments when viewed in GDA2020 environments. A similar service with a ‘multiCRS’ suffix is available which can support GDA2020, GDA94 and WGS 84 ≈ GDA2020 environments.

The NSW Imagery web map service provides spatial imagery covering the extent of NSW. It depicts current cached imagery map of NSW which includes the following data sets:

• LandSat 2014® satellite imagery
• Spatial Services standard coverage ADS sensor orthorectified imagery
• Spatial Services high resolution ADS sensor town imagery
• Spatial Services high resolution ADS sensor project imagery
• AAM 2012 Tweed orthorectified imagery
• AAM 2012 Sydney conurbation 10cm GSD orthorectified imagery
• Jacobs 2009 Upper Hunter AUSIMAGE® orthophoto imagery
• Jacobs 2004 Queanbeyan AUSIMAGE® orthophoto imagery
• Jacobs 2006 Yass AUSIMAGE® orthophoto imagery
• Jacobs 2002 Goulburn AUSIMAGE® orthophoto imagery
• Jacobs 2014 AUSIMAGE® orthophoto imagery
• Jacobs 2016 AUSIMAGE® orthophoto imagery
• Spookfish/Eagleview 2018 Imagery
• Six Cities Conurbation AAM Imagery 2020-2022
• Six Cities Conurbation Aerometrex Imagery 2021-2022

The NSW Imagery web service provides spatial imagery covering the extent of NSW progressively from scales larger than 1:150,000 higher resolution imagery overlays lower resolution imagery and most recent imagery overlays older imagery within each resolution.

This product has been produced to identify visible land cover features and terrain to support Spatial Services along with local and state government programs, including Emergency Services. This product is used on a whole of government basis as a visible record of the landscape at a given point in time.

This web service allows users to easily integrate the Imagery coverage for NSW into Open Geospatial Consortium (OGC) compliant spatial platforms and applications.

Imagery provides an analytical source and contextual background for decision making and supports multiple applications including:

• mapping
• emergency services
• sustainable human and land use development
• geosciences
• natural resource management

The NSW Imagery web service provides access to accurate, authoritative and timely aerial imagery of NSW.

This service ensures users are able to consume spatial imagery without the requirement of hosting the imagery files on their own servers. The Imagery cache is maintained by Spatial Services and is an output of Spatial Services’ imagery collection and maintenance program.

Land and Property Information (LPI’s) Cached map service is a rasterised topographic maps covering NSW. This service contains the current standard Topographic maps from the 1:100,000; 1:50,000 and 1:25,000 series. Where coverage exists at multiple scales the largest scale map is displayed. It compromises the “collars off” tiff images for the current (1:100000, 1:50000 and 1:25000) Topo maps, and replaces the old “Topographic maps (Current Series)” shown in the old six viewer. Land and Property Information (LPI’s) Cached map service is a rasterised topographic maps covering NSW. This service contains the current standard Topographic maps from the 1:100,000; 1:50,000 and 1:25,000 series. Where coverage exists at multiple scales the largest scale map is displayed. It compromises the “collars off” tiff images for the current (1:100000, 1:50000 and 1:25000) Topo maps, and replaces the old “Topographic maps (Current Series)” shown in the old six viewer.

Access API NSW 2020 Spot 6-7 Web Map Tile Service Please Note WGS 84 = GDA94 service This dataset has a spatial reference of [WGS 84 = GDA94] and can NOT be easily consumed into GDA2020 environments. This imagery captured between January and April 2020, is part of the Spatial Services Whole-of-Government initiative providing a bi-annual state-wide, satellite imagery at 1.5m resolution. The state mosaic is provided as a Red Green Blue (RGB) band combination; contrast enhanced lossless 8-bit JPEG2000 file with a world file. To view in ArcMap: Click Catalog > GIS Servers > Add WMTS server Specify this url: https://portal.spatial.nsw.gov.au/tileservices/Hosted/Spot2020Q1/MapServer/WMTS Click Get Layers > Ok Double Click Spot2020Q1 in the catalogue menu under the WMTS. Drag the Spot2020Q1 layer onto the map Metadata Content TitleNSW 2020 Spot 6-7 Web Map Tile ServiceContent TypeOtherDescriptionThe NSW 2019 Spot 6-7 Web Map Tile Service is an imagery service that displays full state capture of satellite imagery sourced from SPOT 6/7 Satellite.Initial Publication Date14/08/2020Data Currency14/08/2020Data Update FrequencyOtherContent SourceOtherFile TypeImagery LayerAttributionData Theme, Classification or Relationship to other DatasetsImagery Theme (NSW Foundation Spatial Framework)AccuracyThis Web Service is built as per Spatial Services project specification.WMS is OGC protocol compatible and suitable for consumption by common GIS platforms.Spatial Reference System (dataset)WGS84Spatial Reference System (web service)EPSG:3857WGS84 Equivalent ToGDA94Spatial ExtentContent LineageData ClassificationUnclassifiedData Access PolicyOpenData QualityTerms and ConditionsCreative CommonsStandard and SpecificationOGC compliant and suitable for consumption by common GIS platforms. This dataset is compliant with the NSW Foundation Spatial Data Framework and its specifications.Data CustodianDCS Spatial Services346 Panorama AveBathurst NSW 2795Point of ContactPlease contact us via the Spatial Services Customer HubData AggregatorData DistributorAdditional Supporting InformationGeospatial Intelligence © CNES (2020) DISTRIBUTION AIRBUS DSTRIM Number

🇦🇺 Australia English Metadata Content Title NSW 2019 Spot 6-7 Web Map ServiceContent Type Web MapDescription NSW Spot Imagery 2019 is first of a series of Bi Annual (commencing second half of 2019) Satellite Image Mosaics covering NSW with an image resolution of 1.5 metres. This imagery is captured in between January and April 2019. The state mosaic is provided as a Red Green Blue (RGB) band combination; contrast enhanced lossless 8-bit JPEG 2000 file with a word file.Initial Publication Date 11/06/2020Data Currency 12/09/2022Data Update Frequency Half-YearlyContent Source OtherFile Type Map Feature Service Attribution Data Theme, Classification or Relationship to other Datasets Accuracy Spatial Reference System (dataset) GDA94 Spatial Reference System (web service) EPSG:4326 WGS84 Equivalent To GDA94 Spatial Extent Content Lineage Data Classification UnclassifiedData Access Policy OpenData Quality Terms and Conditions Creative CommonStandard and Specification Data Custodian Spatial Services | NSW Department of Customer ServicePoint of Contact DCS Spatial Services Customer HubData Aggregator DCS Spatial ServicesData Distributor DCS Spatial ServicesAdditional Supporting Information The web service is built as per Spatial Services's project specification. WMS is OGC protocol compatible and suitable for consumption by common GIS platforms. This dataset is also compliant with the NSW Foundation Spatial Data Framework and its specifications.Dataset Producers and Contributors:Geospatial IntelligenceAirbus DS for SPOT 6/7© CNES (2020) DISTRIBUTION AIRBUS DSTRIM Number

• land values for the past five years (where available)

• the valuing year used to calculate council rates

  </font></li><li><font size='4'>the
  valuation basis 
  
  </font></li><li><font size='4'>the
  property number, address, and zoning information 
  
  </font></li><li><font size='4'>the area
  and boundaries of non strata properties 
  
  </font></li><li><font size='4'>notice of
  any concessions or allowances that apply to the land value. 
  

The map does not show land values for individual strata properties.

• property sales information for individual properties from 2001

  </font></li><li><font size='4'>property
  sales information at a street and suburb level for the last five
  years (where available
  </font></li><li><font size='4'>area for
  non strata properties 
  
  </font></li><li><font size='4'>the
  dealing number and sale date (or contract date) 
  
  </font></li><li><font size='4'>the date
  the property sales information was last updated 
  
  </font></li><li><font size='4'>whether
  the property is strata or non strata, or if the sale is part of a
  multi property sale. 
  

Contact us

Phone : 1800 110 038

Mon-Fri, 8:30am – 5:00pm

Please call TIS National on 131 450 and ask them to call Valuation Services on 1800 110 038.

Metadata

Digital soil maps (DSMs) are prepared through quantitative modelling techniques that are based on relationships between soil attributes and the environment. DSMs are presented over NSW for a range of key soil properties, including soil organic carbon (SOC), pH, cation exchange capacity, sum-of-bases, total phosphorous, electrical conductivity (EC), exchangeable sodium percent (ESP), clay, sand and silt. The maps are at 100 m spatial resolution and cover six soil depth intervals down to 2 m, consistent with standard international systems, plus 0-30 cm and 30-100 cm layers. The modelling techniques applied included multiple linear regression and Cubist decision tree approaches. Validation results for the maps indicate generally moderate performance and effectiveness. The maps provide at least a useful first approximation of these soil properties across the State. The products are described more fully in the technical report: OEH (2017), Digital soil mapping of key soil properties over NSW (37p). The 0-30 and 30-100 cm raster layers, plus 0-5 cm rasters for textural properties (660 and 840 MB zip files) or jpeg images (63 MB zip file) can be downloaded through the NSW SEED data portal. They are also viewable through eSPADE (http://espade.environment.nsw.gov.au). The full suite of depth layers for all soil properties are available through the OEH data broker (data.broker@environment.nsw.gov.au).

Survey and mapping was commissioned by NPWS Western Branch Macquarie Area, Bathurst office. Six floristic quadrats were sampled to further describe the vegetation communities. Walked transects and …Show full descriptionSurvey and mapping was commissioned by NPWS Western Branch Macquarie Area, Bathurst office. Six floristic quadrats were sampled to further describe the vegetation communities. Walked transects and topographic maps were used to delineate vegetation boundaries. The author correlated map units to vegetation communities described in the original Winburndale NR vegetation report (ERM Mitchell McCotter 1996) and allocated them to NSW Vegetation Classes and Formations (Keith 2004) for fire management purposes. Hardcopy maps were digitsed in the Dubbo NPWS Western Branch office. Subsequent to the final report, NPWS staff have corrected correlation errors and re-assigned some map units to the NSW classification (Keith 2004). Mt Horrible (addition to Winburndale NR) mapping supplements existing mapping for Winburndale NR [Vegetation descriptions in: ERM Mitchell McCotter Pty. Ltd. (1996) Bathurst vegetation survey for NSW National Parks and Wildlife Service: Bathurst District covering Winburndale NR, Nangar NP, Conimbla NP and Weddin Mountains NP. NSW National Parks and Wildlife Service, Bathurst. Mapping undertaken by Roger Lembit under contract to NPWS Bathurst District office in 1997 due to inadequacies in ERM mapping.]

Abstract

This data and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are represented here as originally supplied.

Abstract: The Biodiversity Investment Opportunities Map (BIO Map) is a key deliverable of the NSW Government's $40 million Green Corridors program, a Government priority action identified in NSW 2021: A Plan to make NSW number one. The map was prepared with funding provided by the NSW Environmental Trust. The Illawarra BIO Map covers a 112,942-hectare area defined by the Kiama, Shellharbour and Wollongong Local Government Areas. This includes the Illawarra coastal plain and escarpment, and the eastern parts of the sandstone plateau to the west. Each of these landscapes provides a diversity of vegetation types, habitats and landforms, which combined make the region rich in overall biodiversity values. Mapping criteria were used to identify and map priority investment areas, and targeted stakeholder consultation was conducted to inform the outputs of the project. Stakeholders consulted included nine state government authorities, four local councils and six non-government organisations. The priority investment areas comprise of biodiversity core areas and a network of state and regional biodiversity corridors within the Illawarra region. The total area represented within the mapped priority investment areas is 66,827 hectares, comprising 13,980 hectares of core area and 52,847 hectares of corridors. This represents about 59 per cent of the Illawarra region. The BIO Map project aims to achieve better biodiversity outcomes by directing biodiversity investment funding to the strategic locations of greatest benefit. A landholder's right to carry out agricultural and developmental activities on their land are not altered by their property being identified as a priority investment area on the BIO Map. The BIO Map identifies areas where landowners have more opportunities to receive funding to protect their bushland. Any involvement by a landowner in such programs is entirely voluntary. Report Title: Biodiversity Investment Opportunities Map Mapping Priority Investment Areas for the Illawarra Region

Dataset History

This data and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are represented here as originally supplied.

Lineage: Lineage: Core areas are areas of native vegetation and habitat where management will be of greatest benefit to the conservation of state and regional biodiversity values within a region. Combined with state and regional corridors, the areas are termed Priority Investment Areas (PIAs) PIAs were mapped from a combination of existing and established data and from new data layers created specifically for the project. To identify core areas, a seamless vegetation layer was made from 20 separate fine-scale vegetation maps. Vegetation types were then assigned to a single, state-wide classification (i.e. Plant Community Type) and to Threatened Ecological Communities listed in NSW. Core areas were defined as contiguous patches (separated by 30 metres or less) of Threatened Ecological Communities greater than 10 hectares in size. Threatened ecological communities were identified by mapping the associations of PCTs with the NSW Scientific Committee determinations of threatened communities. Land was removed from core areas in cases where it was deemed likely to be affected by development; this included land zoned for urban land uses or areas where land-use intensification or fragmentation was likely. As a general rule, land zoned residential (e.g. R1 to R4 under a standard LEP, or equivalent), industrial (e.g. IN1 to IN4) or business (e.g. B1 to B7) was removed from core areas. Zoning data were obtained from LEPs in force throughout the study area. After stakeholder consultation and feedback, these areas were then refined into fine-scale boundaries based on either property or vegetation boundaries. The boundaries identified focused on capturing entire patches of the vegetation type identified, not just the amount needed to meet the minimum representation target. Therefore, the areas of some vegetation types significantly exceeded their targets. Targeted stakeholder consultation informed and improved the outputs of the project. Nine state government authorities, four local councils and six non-government organisations were engaged to comment on the draft map. Suggestions from stakeholders were assessed against the mapping criteria and (where appropriate) were incorporated into the final BIO Map. Six core areas added to, or expanded, on the basis of stakeholder feedback and the incorporation of more accurate local information. The total area represented within the mapped PIAs is 66 827 hectares, comprising 13 980 hectares of core area and 52 847 hectares of corridors. This represents about 59% of the Illawarra region. Positional accuracy: Digitising was conducted at a scale of approximately 1:10,000-1:15,000. Attribute accuracy: All attributes have been checked. Completeness: The layer is complete. The layer will require periodic updating to account for any clearing or vegtetation change resulting from future landuse activites.

Dataset Citation

NSW Office of Environment and Heritage (2015) Illawarra Region BIO Map Corridors 20150430. Bioregional Assessment Source Dataset. Viewed 18 June 2018, http://data.bioregionalassessments.gov.au/dataset/edd25bee-de70-47ba-a3af-b6f08846fdfa.

These maps and charts were produced by Government departments and other sources.


(SR Map Nos.52571-76). 6 maps.

Note:
This description is extracted from Concise Guide to the State Archives of New South Wales, 3rd Edition 2000.

Impervious surfaces were derived from two existing mapping products, producing six classes of impervious area: Buildings, Roads, Railways, Roads and Railways, Airports and Aerodromes, Stormwater Infrastructure. Impervious Surfaces associated with buildings were derived from the Geoscape® Buildings Theme, which provides polygon representations of every building in Australia with a roof area equal to or greater than 9m2. Building polygons from the Greater Sydney Region were extracted and used to create the ‘Buildings’ feature. Impervious Surfaces not associated with buildings were derived from the NSW Land Use Map 2017 (inclusive of the draft Land Use Mapping for the Sydney Metropolitan Region), which provides land use mapping for NSW at a 1:10,000 reliability scale, based on the Australian Land Use and Management Classification Code. This layer was used to generate the Roads, Railways, Roads and Railways (polygons reclassified during QC) Airports and Aerodromes, and Stormwater Infrastructure features. Once both sets of features had been generated, they were intersected with LGA to aid in computation and provide additional map utility. Following this, any overlap between the Buildings features and the Land Use features was corrected using the Erase Function, before the layers were combined using the union function, and dissolved by LGA and feature class to provide LGA-level breakdowns of the prevalence and providence of impervious areas.

Access API

Please Note
WGS 84 service aligned to GDA94
This dataset has spatial reference [WGS 84 ≈ GDA94] which may result in misalignments when viewed in GDA2020 environments. A similar service with a ‘multiCRS’ suffix is available which can support GDA2020, GDA94 and WGS 84 ≈ GDA2020 environments.
In due course, and allowing time for user feedback and testing, it is intended that the original service name will adopt the new multiCRS functionally.

Survey and mapping was commissioned by NPWS Western Branch Macquarie Area, Bathurst office. Six floristic quadrats were sampled to further describe the vegetation communities. Walked transects and topographic maps were used to delineate vegetation boundaries. The author correlated map units to vegetation communities described in the original Winburndale NR vegetation report (ERM Mitchell McCotter 1996) and allocated them to NSW Vegetation Classes and Formations (Keith 2004) for fire management purposes. Hardcopy maps were digitsed in the Dubbo NPWS Western Branch office. Subsequent to the final report, NPWS staff have corrected correlation errors and re-assigned some map units to the NSW classification (Keith 2004). Mt Horrible (addition to Winburndale NR) mapping supplements existing mapping for Winburndale NR [Vegetation descriptions in: ERM Mitchell McCotter Pty. Ltd. (1996) Bathurst vegetation survey for NSW National Parks and Wildlife Service: Bathurst District covering Winburndale NR, Nangar NP, Conimbla NP and Weddin Mountains NP. NSW National Parks and Wildlife Service, Bathurst. Mapping undertaken by Roger Lembit under contract to NPWS Bathurst District office in 1997 due to inadequacies in ERM mapping.]

Temperate Highland Peat Swamps on Sandstone (THPSS) vegetation maps. THPSS is a Threatened Ecological Community (TEC). Using a 25 m Digital Elevation Modal (DEM) coupled with orthorectified aerial photography, the THPSS of the Sydney Basin were mapped in ArcGIS. Only valley-bottom swamps were mapped. Hanging swamps or hillslope drapes were excluded. In ArcGIS, the physical attributes of the swamps were attributed and measured. This included swamp area, elevation above sea level, swamp slope, catchment area, swamp and catchment elongation ratio, swamp length and distance to coast. Further information on the enforceable undertaking and the terms of the THPSS Research Program can be found at www.environment.gov.au/news/2011/10/21/centennial-coal-fund-145-million-research-program. Attribution to: Macquarie University, K Fryirs & G Hose 2016, THPSS mapping layer. 6 maps showing the spatial distribution of THPSS were produced for the following areas: Blue Mountains - VIS_ID 4480 Budderoo - VIS_ID 4481 Gosford - VIS_ID 4482 Newnes - VIS_ID 4483 Woronora - VIS_ID 4484 Penrose - VIS_ID 4485

The vegetation of Copperhania, Barton, Dapper & Boginderra Hills NRs, located in central-western NSW, is described and mapped at a scale of 1:50 000 based on field survey quadrats, aerial photo interpretation and multivariate analysis. 25 communities in total are described for the four reserves, eight in Copperhania NR, six in each of Barton and Dapper NRs and five in Boginderra Hills. VIS_ID 872) The vegetation of Copperhania, Barton, Dapper & Boginderra Hills NRs, located in central-western NSW, is described and mapped at a scale of 1:50 000 based on field survey quadrats, aerial photo interpretation and multivariate analysis. 25 communities in total are described for the four reserves, eight in Copperhania NR, six in each of Barton and Dapper NRs and five in Boginderra Hills. VIS_ID 872)

This is Version 2 of the Australian Soil Depth product of the Soil and Landscape Grid of Australia.

It supersedes the Release 1 product that can be found at https://doi.org/10.4225/08/546F540FE10AA

The map gives a modelled estimate of the spatial distribution of soil depth in soils across Australia.

The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. Each product contains six digital soil attribute maps, and their upper and lower confidence limits, representing the soil attribute at six depths: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. These depths are consistent with the specifications of the GlobalSoilMap.net project (https://esoil.io/TERNLandscapes/Public/Pages/SLGA/Resources/GlobalSoilMap_specifications_december_2015_2.pdf). The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels).

Detailed information about the Soil and Landscape Grid of Australia can be found at - https://esoil.io/TERNLandscapes/Public/Pages/SLGA/index.html

Attribute Definition: Depth of soil profile (A & B horizons) Units: metres; Period (temporal coverage; approximately): 1950-2021; Spatial resolution: 3 arc seconds (approx 90m); Total number of gridded maps for this attribute: 18; Number of pixels with coverage per layer: 2007M (49200 * 40800); Data license : Creative Commons Attribution 4.0 (CC BY); Target data standard: GlobalSoilMap specifications; Format: Cloud Optimised GeoTIFF; Lineage: Rather than fitting a single model of soil thicknesses we went for a nuanced approach which entailed three separate models for:

Model 1. Predicting the occurrence of rock outcrops.

Model 2. Predicting the thickness of soils within the 0-2m range

Model 3. Predicting the occurrence of deep soils (soils greater than 2m thick)

Models 1 and 3 used the categorical model variant of the Ranger RF which was preceded by distinguishing; for Model 1, the observations that were deemed as rock outcrops from soils. And for Model 3, distinguishing soils that were less than 2m thick (and not rock outcrops) from soils greater than 2m thick. Ultimately both Models 1 and 3 were binary categorical models. 50 repeats of 5-fold CV (cross-validation) iterations of the Ranger RF model were run for each Model variant.

Model 2 used the regression form of the random forest model. After removing from the total data set the observations that were regarded as rock outcrops and soil greater than 2m, there were 111,302 observations available. Of these, 67,698 had explicitly defined soil thickness values. The remaining 43,604 were right-censored data and were treated as follows. For each repeated 5-fold iteration, prior to splitting the data in calibration and validation datasets, values from a beta function were drawn at random of length 43,604. This value (between 0 and 1) was multiplied by the censored value soil thickness and then added to this same value, creating a simulated pseudo-soil thickness. Once the simulated data were combined with actual soil thickness data, the values were square-root transformed to approximate a normal distribution. Ranger RF modelling proceeded after optimising the Hyperparameter settings as described above for the categorical modelling. Like the categorical modelling, 50 repeated 5-fold CV iterations were computed.

All three model approaches were integrated via a simple ‘if-then’ pixel-based procedure. At each pixel, if Model 1 indicated the presence of rock outcrops 45 times or more out of 50 (90% of resampling iterations), the estimated soil thickness was estimated as rock outcrop, or effectively 0cm. Similarly, for Model 3 which was the model based on prediction of deep soils (soils >2m deep). In no situations did we encounter both Models 1 and 3 predict in the positive on 90% or more occasions simultaneously. If Model 1 or 3 did not predict in the positive in 90% of iterations, the prediction outputs of Model 2 were used.

After model integration, we derived a set of soil thickness exceedance probability mapping outputs. These were derived simply by assessing the empirical probabilities (at each pixel) and then tallying the number of occasions the estimated soil depth exceeded given threshold depths of 10cm, 50cm, 100cm, and 150cm. This tallied number was divided by 50 to give an exceedance probability for each threshold depth.

All processing for the generation of these products was undertaken using the R programming language. R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

Code - https://github.com/AusSoilsDSM/SLGA Observation data - https://esoil.io/TERNLandscapes/Public/Pages/SoilDataFederator/SoilDataFederator.html Covariate rasters - https://esoil.io/TERNLandscapes/Public/Pages/SLGA/GetData-COGSDataStore.html

Spatial predictions of plant available water capacity (PAWC), drained upper limit (DUL) and crop lower limit (CLL) for grain-growing regions of NSW, Australia, from Padarian Campusano pedotransfer functions and NSW Office of Environment and Heritage (NSW OEH) datasets.

PAWC is the amount of water a soil can hold against gravity (i.e. water which does not freely drain) that is available to plants through their roots. This soil property is very important in dryland cropping areas which rely on rainfall. The maximum amount of water which can be held by a soil against gravity is called the DUL. The water that remains in a soil after plants have extracted all that is available to them is called the CLL. PAWC is calculated as DUL minus CLL.

Digital soil mapping (DSM) allows the spatial prediction of soil properties across large areas using modelling techniques which combine point data measured in the field and continuous datasets related to soil forming processes such as climate, topography, land cover, existing soil mapping and lithology. Pedotransfer functions (PTFs) are equations which use the easier to measure soil attributes, e.g. sand, clay, bulk density, to model the harder to measure attributes like DUL and CLL. DSM techniques such as Latin Hypercube (LHC) sampling can be used to incorporate the uncertainties associated with the input datasets in the modelling, and to produce estimates of model output precision and reliability.

This data collection consists of spatially predicted PAWC, DUL and CLL for the grain-growing regions of New South Wales, Australia, as defined by the boundary of the Grains Research and Development Corporation's Northern Region. PAWC was modelled using PTFs for DUL and CLL from Padarian Campusano, with LHC sampling to incorporate the uncertainties associated with the input datasets. The PAWC, DUL and CLL were modelled at the six Global Soil Map depths of 0-5 cm, 5-15 cm, 15-30 cm, 30-60 cm, 60-100 cm, and 100-200 cm. The top five depths have been aggregated to create a PAWC prediction for 0-100 cm.

Lineage: INPUT DATASETS 1. Soil attribute layers from the NSW OEH via the eSpade website: clay (%), sand (%), and effective cation exchange capacity (CEC; cmol/kg). The estimated value (mean) and the RMSE values were used for all six Global Soil Map depths (0-5 cm, 5-15 cm, 15-30 cm, 30-60 cm, 60-100 cm, and 100-200 cm). https://www.environment.nsw.gov.au/eSpade2Webapp 2. The Northern Region boundary from the Grains Research and Development Corporation (GRDC)

PEDOTRANSFER FUNCTIONS DUL and CLL equations from Padarian Campusano (2014), which used a subset of 806 soil profiles from the APSoil database that included field measurements of DUL and CLL: 1. DUL = 0.2358 + 0.002572*CEC + 0.001001*clay – 1.70 x 10^-7*sand^3 2. CLL = 0.6151*DUL – 0.02192 3. PAWC = DUL – CLL

METHODS These methods are available from Austin et al. (2019), see Related Links section.

The NSW OEH input datasets were clipped to the study area boundary and divided into tiles of 200 x 200 grid cells prior to parallel processing in a supercomputer environment. Except for the LHC sampling and correlation matrices, all code was written in Python. Layer thickness for each of the six soil depths was calculated in mm from the depth layer upper and lower bounds (e.g. 5 to 15 cm).

A correlation matrix was generated in the R package for the NSW OEH clay, sand, and CEC input datasets for each of the six depths, with correlation values derived using data for the whole study area for each of the inputs.

Each of the six soil depth layers was modelled separately. For every grid cell in each depth layer, the following steps were used to calculate DUL, CLL and PAWC: 1. The RMSE values for the clay, sand, and CEC input variables were used as approximations of standard deviation (SD) for input to the LHC sampling

1. LHC sampling with a correlation matrix (from the R pse library; Chalom and Prado, 2014), using means, SDs and a correlation matrix as inputs, produced fifty realisations of each input variable. Fifty realisations were chosen following the work of Malone et al. (2015) who found that there was little difference in outcome when using more than 50 samples

2. 50 DUL and CLL values were calculated from the 50 input variable realisations using the DUL and CLL equations from Padarian Campusano (2014)

3. 50 PAWC values were calculated from the DUL and CLL values, constrained by the depth layer thickness, with units of mm

4. From the 50 DUL, CLL and PAWC values for each grid cell, the mean, median, 5th and 95th percentiles, and SD were calculated and written to file as geotiffs

The tiled outputs were merged to form single rasters of the study area for DUL, CLL and PAWC at each of the six depths. Additionally, the 0-5, 5-15, 15-30, 30-60 and 60-100 cm soil depth layers were used to calculate 0-1 m versions of DUL, CLL and PAWC. The mean, median, 5th and 95th percentile values were summed to produce the 0-1 m DUL, CLL or PAWC prediction for each grid cell. This aggregation of depths assumes high correlation between layers – for example, the 95th percentile for the 0 – 1 m layer is the sum of the 95th percentiles for each contributing layer. If the layers were uncorrelated, the 95th percentile would end up closer to the mean. The SD for each of the 0-1 m DUL, CLL and PAWC layers was calculated from the summed 5th and 95th percentiles, as per the equation from Malone et al. (2011).

This is Version 1 of the Australian Soil Total Phosphorus product of the Soil and Landscape Grid of Australia.

The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. Each product contains six digital soil attribute maps, and their upper and lower confidence limits, representing the soil attribute at six depths: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. These depths are consistent with the specifications of the GlobalSoilMap.net project (http://www.globalsoilmap.net/). The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels).

These maps are generated by combining the best available Digital Soil Mapping (DSM) products available across Australia.

Attribute Definition: Total phosphorus; Units: %; Period (temporal coverage; approximately): 1950-2013; Spatial resolution: 3 arc seconds (approx 90m); Total number of gridded maps for this attribute: 18; Number of pixels with coverage per layer: 2007M (49200 * 40800); Total size before compression: about 8GB; Total size after compression: about 4GB; Data license : Creative Commons Attribution 4.0 (CC BY); Target data standard: GlobalSoilMap specifications; Format: GeoTIFF. Lineage: The National Soil Attribute Maps are generated by combining the best available digital soil mapping to calculate a variance weighted mean for each pixel. For this soil attribute the Australia-wide three-dimensional Digital Soil Property Maps are the only maps available. Thus the modelling for this soil attribute only used Decision trees with piecewise linear models with kriging of residuals developed from soil site data across Australia. (Viscarra Rossel et al., 2015a).