Global map of clay minerals in terrestrial soils

We developed a comprehensive dataset of major soil clay minerals covering the global land surface for both topsoil (near-surface) and subsoil at different spatial resolutions. This dataset is intended for application in a variety of earth science fields that require interdisciplinary data. We gathered observational data on clay minerals through a literature survey and meta-analysis. Most observations were originally obtained by x-ray diffraction (XRD) analysis. The multitude of clay minerals that occur in soils were classified into ten groups: chlorite, gibbsite, kaolinite, mica-illite, smectite, quartz, vermiculite, non-crystalline (amorphous and short-range order minerals), iron oxide, and others. We then aggregated the clay mineral composition data on the basis of 12 soil orders. Using a global map of soil orders and additional soil datasets, we developed global maps of clay mineral abundances in topsoil and subsoil at a resolution of 2° grid cells (about 3.7 km) and, by averaging, at lower spatial resolutions (e.g., 1° grid cells). We examined uncertainties in the dataset by statistical (i.e., Monte Carlo) methods and by comparison with previous datasets. The new dataset will facilitate continental-scale studies of biogeochemistry and climatology by providing more precise soil properties related to, for example, soil adsorption and dust emission. The dataset should also find application in interdisciplinary studies in fields such as hydrology and agronomy, both as input data for model simulations and for the interpretation of observational data.

本研究构建了一套覆盖全球陆表的表层土壤（近地表）与亚表层土壤的主要黏土矿物综合数据集，具备多种空间分辨率。本数据集面向需跨学科数据支撑的各类地球科学领域应用。研究团队通过文献调研与荟萃分析收集黏土矿物观测数据，其中绝大多数观测数据最初通过X射线衍射（XRD）分析获取。研究团队将土壤中产出的各类黏土矿物划分为10个类别：绿泥石、三水铝石、高岭石、云母-伊利石、蒙脱石、石英、蛭石、非晶质（无定形与短程有序矿物）、氧化铁及其他类。随后，研究团队基于12个土壤土纲对黏土矿物组成数据进行聚合整合。借助全球土壤土纲分布图与其他土壤数据集，研究团队分别生成了2°网格（约3.7千米）分辨率下的表层与亚表层土壤黏土矿物丰度全球分布图，并通过平均处理得到更低空间分辨率的分布图（如1°网格）。研究团队通过统计方法（即蒙特卡洛法）以及与既往数据集对比的方式，对本数据集的不确定性进行了评估。本数据集可提供更为精准的土壤属性（如土壤吸附性与粉尘排放相关属性），将助力大陆尺度的生物地球化学与气候学研究。此外，本数据集还可作为模型模拟的输入数据或观测数据解译依据，应用于水文学、农学等跨学科研究领域。