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.

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.

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.

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.

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

Profile curvature is the rate of change of potential gradient down a flow line and represents the changes in flow velocity down a slope. It is significant for flow acceleration, erosion/deposition rates and geomorphology. The profile curvature product was derived from the Smoothed Digital Elevation Model (DEM-S; ANZCW0703014016), which was derived from the 1 arc-second resolution SRTM data acquired by NASA in February 2000. The calculation of profile curvature from DEM-S accounted for the varying spacing between grid points in the geographic projection. This collection includes Profile Curvature data at 1 arc-second and 3 arc-second resolutions. The 3 arc-second resolution product was generated from the 1 arc-second profile curvature product and masked by the 3” water and ocean mask datasets.

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 arc-second resolution versions of these radiation surfaces have been produced from the 1 arc-second 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:9530 . The 1 arcsecond mosaic data can be found here: https://data.csiro.au/dap/landingpage?pid=csiro:18851

The SRTM grid cell area dataset has values of cell area in square metres. The grid cell area product was derived from the Smoothed Digital Elevation Model (DEM-S; ANZCW0703014016), which was derived from the 1 second resolution SRTM data acquired by NASA in February 2000. The calculation of grid cell area from the DEM-S accounted for the varying spacing between grid points in the geographic projection.

The elevation range measures the full range of elevations within a circular window and can be used as a representation of local relief. The 300 m elevation range product was derived from the Smoothed Digital Elevation Model (DEM-S; ANZCW0703014016), which was derived from the 1 arc-second resolution SRTM data acquired by NASA in February 2000. This collection includes data at 1 arc-second and 3 arc-second resolutions. The 3 arc-second resolution product was generated from the 1 arc-second 300 m elevation range product and masked by the 3” water and ocean mask datasets.

MrRTF is a topographic index designed to identify high flat areas at a range of scales. It complements the MrVBF index that is designed to identify areas of deposited material in flat valley bottoms. Unlike MrVBF, the MrRTF index does not have a clear link to landform processes but it has been found to be a useful adjunct to MrVBF in landform classification. Zero values indicate areas that are steep or low, with values 1 and larger indicating progressively larger areas of high flat land. This collection includes MrRTF data at 1 arc-second and 3 arc-second resolutions. The 3 arc-second resolution product was generated from the 1 arc-second MrRTF product and masked by the 3” water and ocean mask datasets.