Data

Maps of iron oxides and the color of Australian soil

Commonwealth Scientific and Industrial Research Organisation
Viscarra Rossel, Raphael ; Bui, Elisabeth ; de Caritat, Patrice ; McKenzie, Neil
Viewed: [[ro.stat.viewed]] Cited: [[ro.stat.cited]] Accessed: [[ro.stat.accessed]]
ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.4225/08/55DFFC6C56916&rft.title=Maps of iron oxides and the color of Australian soil&rft.identifier=https://doi.org/10.4225/08/55DFFC6C56916&rft.publisher=Commonwealth Scientific and Industrial Research Organisation&rft.description=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.\nAttributes: \nUnits of measurement: \n1.\tMunsell Hue; \n2.\tMunsell Chroma; \n3.\tMunsell value; \n4.\tNIODI; \n5.\tNIODI uncertainty.\nFor details please see Viscarra Rossel et al. (2010). \n\nData Type: Float Grid. \n\nMap projection: Lambert Conformal Conic. \n\nDatum: GDA94. \n\nMap units: Decimal degrees. \n\nResolution: 10,000 metres. \n\nFile Header Information:\nncols 392; \nnrows 361; \nxllcorner -2032461.3; \nyllcorner -4936305.3; \ncellsize 10000; \nNODATA_value -9999; \nbyteorder LSBFIRST.&rft.creator=Viscarra Rossel, Raphael &rft.creator=Bui, Elisabeth &rft.creator=de Caritat, Patrice &rft.creator=McKenzie, Neil &rft.date=2015&rft.edition=v1&rft.coverage=westlimit=112.99875; southlimit=-44.00125; eastlimit=154.016250000662; northlimit=-9.98124999945077; projection=WGS84&rft_rights=CSIRO Data Licence https://research.csiro.au/dap/licences/csiro-data-licence/&rft_rights=Data is accessible online and may be reused in accordance with licence conditions&rft_rights=All Rights (including copyright) CSIRO Australia 2014.&rft_subject=TERN_Soils&rft_subject=TERN_Soils_DSM&rft_subject=hematite&rft_subject=goethite&rft_subject=soil color&rft_subject=soil colour&rft_subject=visible-near-infrared reflectance&rft_subject=soil mapping&rft_subject=geostatistical simulations&rft_subject=Soil sciences not elsewhere classified&rft_subject=Soil sciences&rft_subject=ENVIRONMENTAL SCIENCES&rft.type=dataset&rft.language=English Access the data

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Brief description

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.\tMunsell Hue;
2.\tMunsell Chroma;
3.\tMunsell value;
4.\tNIODI;
5.\tNIODI 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.

Available: 2015-08-28

154.01625,-9.98125 154.01625,-44.00125 112.99875,-44.00125 112.99875,-9.98125 154.01625,-9.98125

133.50750000033,-26.991249999725

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