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.

Topographic Wetness Index (TWI) is calculated as log_e(specific catchment area / slope) and estimates the relative wetness within a catchment. The TWI product was derived from the partial contributing area product (CA_MFD_PARTIAL), which was computed from the Hydrologically enforced Digital Elevation Model (DEM-H; ANZCW0703014615), and from the percent slope product, which was computed from the Smoothed Digital Elevation Model (DEM-S; ANZCW0703014016). Both DEM-S and DEM-H are based on the 1 arcsecond resolution SRTM data acquired by NASA in February 2000. Note that the partial contributing area product does not always represent contributing areas larger than about 25 km2 because it was processed on overlapping tiles, not complete catchments. This only impacts TWI values in river channels and does not affect values on the land around the river channels. Since the index is not intended for use in river channels this limitation has no impact on the utility of TWI for spatial modelling. The TWI data are available in gridded format at 1 arcsecond and 3 arcsecond resolutions. The 3 arcsecond resolution TWI product was generated from the 1 arcsecond TWI 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 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:9633 . The 1 arcsecond mosaic data can be found here: https://data.csiro.au/dap/landingpage?pid=csiro:18611

The Prescott Index is a measure of water balance that has proven to be a useful in soil mapping both to stratify study areas for sampling and as a quantitative predictor of soil properties (Prescott, 1949; McKenzie et al, 2000). The index was designed to give an indication of the intensity of leaching by excess water and is calculated using long-term average precipitation P and potential evaporation E, both expressed as mean monthly values in mm (mean annual values divided by 12): PI = 0.445P / E^0.75 The evaporation was estimated from temperature and net radiation; the net radiation was computed by the SRAD solar radiation model using the smoothed 1 arc-second resolution DEM-S (ANZCW0703014016) and includes both regional climatic influences and local topographic effects. Precipitation and temperature were obtained from national climate surfaces averaged over the same time period as the climatic information used in the radiation calculations (1981-2006). The Prescott Index has no units. Larger values indicate wetter conditions. The 3 arc-second resolution version of the Prescott Index has been produced from the 1 arc-second resolution surface, by aggregating the cells in a 3x3 window and taking the mean value.

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:18851 . 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 3 arcsecond mosaic data can be found here: https://data.csiro.au/dap/landingpage?pid=csiro:18852

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 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 data in this collection are available at 1 arcsecond resolution as 1x1 degree tiles (ESRI grid format) or as a single grid mosaic of Australia (TIFF format), and at 3 arcsecond resolution as a single grid of Australia (TIFF format).

The elevation range measures the full range of elevations within a circular window and can be used as a representation of local relief. The 1000 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 Relief data at 1 arc-second and 3 arc-second resolutions. The 3 arc-second resolution product was generated from the 1 arc-second 1000 m elevation range product and masked by the 3” water and ocean mask datasets.