Maps of clay minerals - kaolinite, illite and smectite in Australian soils

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.\n\nAttributes: \nUnits of measurement: \n1.\tAbundance of kaolin (0 - 1) for the 0-20 cm and 60-80 cm depths; \n2.\tAbundance of illite (0 - 1) for the 0-20 cm and 60-80 cm depths; \n3.\tAbundance of smectite (0 - 1) for the 0-20 cm and 60-80 cm depths; \n4.\tTernary RGB image of mineral composition for the 0-20 cm and 60-80 cm depths. \n\nFor details please see Viscarra Rossel (2011).\n\nData Type: Float Grid. \nKaolinite, illite, smectite composite maps in GEOTIFF format. \n\nMap projections: Geographic. \n\nDatum: GDA94 \n\nMap units: Decimal degrees. \n\nResolution: 0.00083333333 degrees. \n\nFile Header Information: \nncols 48874; \nnrows 40373; \nxllcorner 112.91246795654; \nyllcorner -43.642475129116; \ncellsize 0.00083333333333333; \nNODATA_value -9999; \nbyteorder LSBFIRST.

黏土矿物是土壤中活性最强的无机组分，其是决定土壤属性的关键因素，并在很大程度上主导土壤的行为与功能。黏土矿物同时在生物地球化学循环中发挥重要作用，并通过与环境的相互作用影响风化、侵蚀与沉积等地貌过程。本数据集为澳大利亚提供了全新的空间显性黏土矿物学信息，有助于深化对土壤及其在景观与生态系统功能中所扮演角色的认知。
本研究采用近红外（near infrared, NIR）光谱技术，测定了澳大利亚土壤中高岭石（kaolinite）、伊利石（illite）与蒙脱石（smectite）的含量。采用模型树算法，本研究以代表土壤形成要素（气候、母质、地形、植被与时间）及其作用过程和空间变异尺度的变量为预测因子，针对两种深度（0~20 cm、60~80 cm）下的每种矿物构建了基于规则的预测模型。
研究结果表明，气候、母质与土壤类型对黏土矿物的含量及空间分布影响最为显著，而地形与植被仅产生局部效应。本研究在3弧秒的网格上对每种矿物进行了数字化成图，这些图谱展示了澳大利亚土壤中高岭石、伊利石与蒙脱石的相对含量及空间分布特征。
高岭石广泛分布于多种气候环境中，但在深度风化土壤、高地土壤及多雨区域占据主导地位。伊利石见于多种景观环境中，可作为寒冷干旱气候的指示矿物，但亦可在暖湿的土壤环境中存在。蒙脱石多为自生矿物，由玄武岩风化形成，亦可赋存于沉积物与钙质基质之上；其主要分布于干旱气候及低地形起伏的景观区域。
这套全新的黏土矿物图谱填补了土壤矿物学信息获取方面的重大空白，可为土壤肥力与粮食生产、碳封存、土地退化、沙尘与气候模拟以及古气候变化等相关研究提供数据支撑。

数据集属性：
测量单位：
1. 0~20 cm与60~80 cm深度下的高岭石含量（取值范围0~1）；
2. 0~20 cm与60~80 cm深度下的伊利石含量（取值范围0~1）；
3. 0~20 cm与60~80 cm深度下的蒙脱石含量（取值范围0~1）；
4. 0~20 cm与60~80 cm深度下的矿物组成三元RGB图像。

详细信息请参阅Viscarra Rossel（2011）的相关研究。

数据类型：浮点型网格数据。
高岭石、伊利石与蒙脱石的合成图谱采用GEOTIFF格式存储。
地图投影：地理坐标系。
基准面：GDA94
地图单位：十进制度。
空间分辨率：0.00083333333 度。

文件头信息：
列数（ncols）：48874；
行数（nrows）：40373；
左下角x坐标（xllcorner）：112.91246795654；
左下角y坐标（yllcorner）：-43.642475129116；
像元大小（cellsize）：0.00083333333333333；
无数据值（NODATA_value）：-9999；
字节序（byteorder）：LSBFIRST（最低有效字节在前）。