Iron (Fe) oxide mineralogy in most Australian soils is poorly characterized, even though Fe oxides play an important role in soil function. Fe oxides reflect the conditions of pH, redox potential, moisture, and temperature in the soil environment. The strong pigmenting effect of Fe oxides gives most soils their color, which is largely a reflection of the soil’s Fe mineralogy. Visible-near-infrared (vis-NIR) spectroscopy can be used to identify and measure the abundance of certain Fe oxides in soil, and the visible range can be used to derive tristimuli soil color information. We measured the spectra of 4606 surface soil samples from across Australia using a vis-NIR spectrometer with a wavelength range of 350-2500 nm. We determined the Fe oxide abundance for each sample using the diagnostic absorption features of hematite (near 880 nm) and goethite (near 920 nm) and derived a normalized iron oxide difference index (NIODI) to better discriminate between them. The NIODI was generalized across Australia with its spatial uncertainty using sequential indicator simulation, which resulted in a map of the probability of the occurrence of hematite and goethite. We also derived soil RGB color from the spectra and mapped its distribution and uncertainty across the country using sequential Gaussian simulations. The simulated RGB color values were made into a composite true color image and were also converted to Munsell hue, value, and chroma. These color maps were compared to the map of the NIODI, and both were used to interpret our results. The maps were validated by randomly splitting the data into training and test data sets, as well as by comparing our results to existing studies on the distribution of Fe oxides in Australian soils. Attributes: Units of measurement: 1. Munsell Hue; 2. Munsell Chroma; 3. Munsell value; 4. NIODI; 5. NIODI uncertainty. For details please see Viscarra Rossel et al. (2010). Data Type: Float Grid. Map projection: Lambert Conformal Conic. Datum: GDA94. Map units: Decimal degrees. Resolution: 10,000 metres. File Header Information: ncols 392; nrows 361; xllcorner -2032461.3; yllcorner -4936305.3; cellsize 10000; NODATA_value -9999; byteorder LSBFIRST.

Clay minerals are the most reactive inorganic components of soils. They help to determine soil properties and largely govern their behaviors and functions. Clay minerals also play important roles in biogeochemical cycling and interact with the environment to affect geomorphic processes such as weathering, erosion and deposition. This data provides new spatially explicit clay mineralogy information for Australia that will help to improve our understanding of soils and their role in the functioning of landscapes and ecosystems. I measured the abundances of kaolinite, illite and smectite in Australian soils using near infrared (NIR) spectroscopy. Using a model-tree algorithm, I built rule-based models for each mineral at two depths (0-20 cm, 60-80 cm) as a function of predictors that represent the soil-forming factors (climate, parent material, relief, vegetation and time), their processes and the scales at which they vary. The results show that climate, parent material and soil type exert the largest influence on the abundance and spatial distribution of the clay minerals; relief and vegetation have more local effects. I digitally mapped each mineral on a 3 arc-second grid. The maps show the relative abundances and distributions of kaolinite, illite and smectite in Australian soils. Kaolinite occurs in a range of climates but dominates in deeply weathered soils, in soils of higher landscapes and in regions with more rain. Illite is present in varied landscapes and may be representative of colder, more arid climates, but may also be present in warmer and wetter soil environments. Smectite is often an authigenic mineral, formed from the weathering of basalt, but it also occurs on sediments and calcareous substrates. It occurs predominantly in drier climates and in landscapes with low relief. These new clay mineral maps fill a significant gap in the availability of soil mineralogical information. They provide data to for example, assist with research into soil fertility and food production, carbon sequestration, land degradation, dust and climate modeling and paleoclimatic change. Attributes: Units of measurement: 1. Abundance of kaolin (0 - 1) for the 0-20 cm and 60-80 cm depths; 2. Abundance of illite (0 - 1) for the 0-20 cm and 60-80 cm depths; 3. Abundance of smectite (0 - 1) for the 0-20 cm and 60-80 cm depths; 4. Ternary RGB image of mineral composition for the 0-20 cm and 60-80 cm depths. For details please see Viscarra Rossel (2011). Data Type: Float Grid. Kaolinite, illite, smectite composite maps in GEOTIFF format. Map projections: Geographic. Datum: GDA94 Map units: Decimal degrees. Resolution: 0.00083333333 degrees. File Header Information: ncols 48874; nrows 40373; xllcorner 112.91246795654; yllcorner -43.642475129116; cellsize 0.00083333333333333; NODATA_value -9999; byteorder LSBFIRST.

The digital 3-dimenional (3D) mineral mapping suite of Queensland comprises ~20 “standardized” products at the spectral resolution of the ASTER (Advanced Space-borne Thermal Emission and Reflection Radiometer) sensor and generated from publicly-available satellite, airborne, field and drill core spectral data spanning the visible near infrared (VNIR; 0.4 to 1.0 µm), shortwave infrared (SWIR; 1.0 to 2.5 µm) and thermal infrared (TIR; 7.5 to 12.0 µm) wavelength regions, including: 1. Satellite ASTER maps at both 30 m and 90 m pixel resolution with complete coverage of the state of Queensland, i.e. 1.853 million km²; 2. Airborne HyMap maps at ~5 m pixel resolution with a coverage of ~25,000 km2 from areas across north Queensland; 3. Field point samples (~300) from the National Geochemical Survey of Australia (NGSA) collected from a depth of 0-10 cm of flood overbank sediments; 4. Drill-core profiles (~20) of the National Virtual Core Library (NVCL) selected from the area around the Georgetown seismic line (07GA-IG2). Key to the processing of the remote sensing data-sets (ASTER and HyMap) was the implementation of unmixing methods to remove the effects dry and green vegetation. This unmixing was not applied to the Australian ASTER geoscience maps released in 2012 (called here Version 1 or V1) resulting in extensive areas with little/no mineral information because of the need to apply masks. The vegetation unmixing methods used in the Version 2 (V2) processing of the ASTER and HyMap imagery has resulted in very few areas without coherent mineral information. The resultant V2 “mineral group” products were designed to measure mineral information potentially useful for mapping: (i) primary rock composition; (ii) superimposed alteration effects; and (iii) regolith cover. These V2 products may assist in mapping soil properties and groundwater conditions. However their relatively low spectral resolution (based on ASTER’s 14 VNIR-SWIR-TIR bands) means that they do not provide the high level of mineralogical detail available from hyperspectral systems (>100 spectral bands), like HyMap and the HyLogger. Nevertheless, the relatively low spectral resolution of ASTER means that all other sensor data can be spectrally resampled to that resolution. Furthermore, the ASTER global data archive, which now spans entire Earth’s land surface <80degrees latitude, means that it can be used as global base-map for integrating all other spectral data.

The Revised Universal Soil Loss Equation (RUSLE) estimates the annual soil loss that is due to erosion using a factor-based approach with rainfall, soil erodibility, slope length, slope steepness and cover management and conservation practices as inputs. The collection is (i) a set of maps that represent the RUSLE factors, (ii) a map of the RUSLE estimates of soil erosion in Australia and (iii) a map of the uncertainty in the estimates of erosion.

We measured the spectra of 4606 surface soil samples from across Australia using a vis-NIR spectrometer. These spectra provide an integrative measure that provides information on the fundamental characteristics and composition of the soil, including colour, iron oxide, clay and carbonate mineralogy, organic matter content and composition, the amount of water present and particle size. This soil information content of the spectra was summarised using a principal component analysis (PCA). We used model trees to derive statistical relationships between the scores of the PCA and 31 predictors that were readily available and we thought might best represent the factors of soil formation (climate, organisms, relief, parent material, time and the soil itself). The models were validated and subsequently used to produce digital maps of the information content of the spectra, as summarised by the PCA, with estimates of prediction error at 3-arc seconds (around 90 m) pixel resolution. The maps might be useful in situations requiring high-resolution, quantitative soil information e.g. in agricultural, environmental and ecologic modelling and for soil mapping and classification. Attributes: Units of measurement: 1. Principal component 1; 2. Principal component 3; 3. Principal component 3. For interpretations please see Viscarra Rossel & Chen (2011). Data Type: Float Grid. Map Projection: Geographic. Datum: GDA94. Map units: Decimal degrees. Resolution: 0.00083333333 degrees. File Header Information: ncols 48874; nrows 40373; xllcorner 112.91246795654; yllcorner -43.642475129116; cellsize 0.00083333333333333; NODATA_value -9999; byteorder LSBFIRST.

<p>This is Version 1 of the Australian Soil Total Phosphorus product of the Soil and Landscape Grid of Australia. It is superseded by the Release 2 product that can be found at: <a href="../../../metadata/TERN/be382e63-5ff6-40a9-930f-c84655a5bd87" target="_blank" rel="noopener">Soil and Landscape Grid National Soil Attribute Maps - Total Phosphorus (3" resolution) - Release 2</a>.</p> <p>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.</p>

Mean monthly solar radiation was modelled across Australia using topography from the 1 arcsecond resolution SRTM-derived DEM-S and climatic and land surface data. The SRAD model (Wilson and Gallant, 2000) was used to derive: • Incoming short-wave radiation on a sloping surface • Short-wave radiation ratio (shortwave on sloping surface / shortwave on horizontal surface) • Incoming long-wave radiation • Outgoing long-wave radiation • Net long-wave radiation • Net radiation • Sky view factor All radiation values are in MJ/m2/day except for short-wave radiation ratio which has no units. The sky view factor is the fraction of the sky visible from a grid cell relative to a horizontal plane. The radiation values are determined for the middle day of each month (14th or 15th) using long-term average atmospheric conditions (such as cloudiness and atmospheric transmittance) and surface conditions (albedo and vegetation cover). They include the effect of terrain slope, aspect and shadowing (for sun positions at 5 minute intervals from sunrise to sunset), direct and diffuse radiation and sky view. The data in this collection are available at 1 arcsecond resolution as single (mosaicked) grids for Australia in TIFF format. The 1 arcsecond tiled data can be found here: https://data.csiro.au/dap/landingpage?pid=csiro:18335 . The 3 arcsecond resolution versions of these radiation surfaces have been produced from the 1 arcsecond resolution surfaces, by aggregating the cells in a 3x3 window and taking the mean value. The 3 arcsecond mosaic data can be found here: https://data.csiro.au/dap/landingpage?pid=csiro:18336

Mean monthly solar radiation was modelled across Australia using topography from the 1 arcsecond resolution SRTM-derived DEM-S and climatic and land surface data. The SRAD model (Wilson and Gallant, 2000) was used to derive: • Incoming short-wave radiation on a sloping surface • Short-wave radiation ratio (shortwave on sloping surface / shortwave on horizontal surface) • Incoming long-wave radiation • Outgoing long-wave radiation • Net long-wave radiation • Net radiation • Sky view factor All radiation values are in MJ/m2/day except for short-wave radiation ratio which has no units. The sky view factor is the fraction of the sky visible from a grid cell relative to a horizontal plane. The radiation values are determined for the middle day of each month (14th or 15th) using long-term average atmospheric conditions (such as cloudiness and atmospheric transmittance) and surface conditions (albedo and vegetation cover). They include the effect of terrain slope, aspect and shadowing (for sun positions at 5 minute intervals from sunrise to sunset), direct and diffuse radiation and sky view. The monthly data in this collection are available at 1 arcsecond resolution as 1x1 degree tiles in ESRI float grid format. 813 tiles make up the extent of Australia. The 1 arcsecond mosaic data can be found here: https://data.csiro.au/dap/landingpage?pid=csiro:18491 . The 3 arcsecond resolution versions of these radiation surfaces have been produced from the 1 arcsecond resolution surfaces, by aggregating the cells in a 3x3 window and taking the mean value. The 3 arcsecond mosaic data can be found here: https://data.csiro.au/dap/landingpage?pid=csiro:18492

<p>This is Version 1 of the Australian Soil Clay product of the Soil and Landscape Grid of Australia. It is superseded by: <span data-sheets-root="1"><a href="../../../metadata/TERN/f95dc442-013b-4fad-b31f-91ba86fbe7f5" target="_blank" rel="noopener">Soil and Landscape Grid National Soil Attribute Maps - Clay (3" resolution) - Release 2</a>.</span></p> <p>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: 2 μm mass fraction of the less than 2 mm soil material determined using the pipette method; 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.</p>

Mean monthly solar radiation was modelled across Australia using topography from the 1 arcsecond resolution SRTM-derived DEM-S and climatic and land surface data. The SRAD model (Wilson and Gallant, 2000) was used to derive: • Incoming short-wave radiation on a sloping surface • Short-wave radiation ratio (shortwave on sloping surface / shortwave on horizontal surface) • Incoming long-wave radiation • Outgoing long-wave radiation • Net long-wave radiation • Net radiation • Sky view factor All radiation values are in MJ/m2/day except for short-wave radiation ratio which has no units. The sky view factor is the fraction of the sky visible from a grid cell relative to a horizontal plane. The radiation values are determined for the middle day of each month (14th or 15th) using long-term average atmospheric conditions (such as cloudiness and atmospheric transmittance) and surface conditions (albedo and vegetation cover). They include the effect of terrain slope, aspect and shadowing (for sun positions at 5 minute intervals from sunrise to sunset), direct and diffuse radiation and sky view. The monthly data in this collection are available at 3 arcsecond resolution as single (mosaicked) grids for Australia in TIFF format. The 3 arcsecond resolution versions of these radiation surfaces have been produced from the 1 arcsecond resolution surfaces by aggregating the cells in a 3x3 window and taking the mean value. The 1 arcsecond tiled data can be found here: https://data.csiro.au/dap/landingpage?pid=csiro:9630 . The 1 arcsecond mosaic data can be found here: https://data.csiro.au/dap/landingpage?pid=csiro:18670