Soil and Landscape Grid National Soil Attribute Maps - Silt (3" resolution) - Release 2

This is Version 2 of the Australian Soil Silt Content product of the Soil and Landscape Grid of Australia.\n\nIt supersedes the Release 1 product that can be found at https://doi.org/10.4225/08/546F48D6A6D48\n\nThe map gives a modelled estimate of the spatial distribution of silt in soils across Australia.\n\nThe Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. Each product contains six digital soil attribute maps, and their upper and lower confidence limits, representing the soil attribute at six depths: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. These depths are consistent with the specifications of the GlobalSoilMap.net project (https://esoil.io/TERNLandscapes/Public/Pages/SLGA/Resources/GlobalSoilMap_specifications_december_2015_2.pdf). The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels).\n\nDetailed information about the Soil and Landscape Grid of Australia can be found at - https://esoil.io/TERNLandscapes/Public/Pages/SLGA/index.html\n\nAttribute Definition: 2-20 um mass fraction of the < 2 mm soil material determined using the pipette method\nUnits: %;\nPeriod (temporal coverage; approximately): 1950-2021;\nSpatial resolution: 3 arc seconds (approx 90m);\nTotal number of gridded maps for this attribute: 18;\nNumber of pixels with coverage per layer: 2007M (49200 * 40800);\nData license : Creative Commons Attribution 4.0 (CC BY);\nTarget data standard: GlobalSoilMap specifications;\nFormat: Cloud Optimised GeoTIFF;\nLineage: The approach, based on machine learning, predicts each soil texture fraction at 90 m grid cell resolution, at depths 0–5 cm, 5–15 cm, 15–30 cm, 30–60 cm, 60–100 cm and 100–200 cm. The approach accommodates uncertainty in converting field measurements to quantitative estimates of texture fractions. Existing methods of bootstrap resampling were exploited to predict uncertainties, which are expressed as 90% prediction intervals about the mean prediction at each grid cell. The models and the prediction uncertainties were assessed by an external validation dataset. Results were compared with Version 1 Soil and Landscape Grid of Australia (v1.SLGA) (Viscarra Rossel et al. 2015). All predictive and functional accuracy diagnostics demonstrate improvements compared with v1.SLGA. Improvements were noted for the sand and clay fraction mapping with average improvement of 3% and 2%, respectively, in the RMSE estimates. Marginal improvements were made for the silt fraction mapping, which was relatively difficult to predict. We also made comparisons with recently released World Soil Grid products (v2.WSG) and made similar conclusions.\n\nAll processing for the generation of these products was undertaken using the R programming language. R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.\n\nCode - https://github.com/AusSoilsDSM/SLGA\nObservation data - https://esoil.io/TERNLandscapes/Public/Pages/SoilDataFederator/SoilDataFederator.html\nCovariate rasters - https://esoil.io/TERNLandscapes/Public/Pages/SLGA/GetData-COGSDataStore.html

本数据集为澳大利亚土壤与景观网格（Soil and Landscape Grid of Australia, SLGA）发布的澳大利亚土壤粉粒含量产品的第2版。

本产品替代了可通过https://doi.org/10.4225/08/546F48D6A6D48获取的第1版产品。

本数据集通过建模估算了澳大利亚全境土壤粉粒的空间分布格局。

澳大利亚土壤与景观网格已推出一系列数字化土壤属性产品。单套产品包含6张数字化土壤属性图及其上下置信限，分别对应6个土壤深度层级：0~5cm、5~15cm、15~30cm、30~60cm、60~100cm及100~200cm。该深度划分标准与GlobalSoilMap.net项目的规范完全一致（详见https://esoil.io/TERNLandscapes/Public/Pages/SLGA/Resources/GlobalSoilMap_specifications_december_2015_2.pdf）。本数据集的数字化土壤属性图采用栅格格式，分辨率为3角秒（约90×90米像素）。

关于澳大利亚土壤与景观网格的详细信息可访问：https://esoil.io/TERNLandscapes/Public/Pages/SLGA/index.html

属性定义：采用移液管法测定的粒径<2mm的土壤组分中，2~20μm粒径颗粒的质量占比
单位：百分比（%）
时间覆盖范围（近似值）：1950年—2021年
空间分辨率：3角秒（约90米）
本属性对应的栅格地图总数量：18张
单图层有效像素数量：2007百万（49200×40800）
数据许可协议：知识共享署名4.0国际许可（Creative Commons Attribution 4.0, CC BY）
目标数据标准：GlobalSoilMap规范
文件格式：云优化地理标签图像文件格式（Cloud Optimised GeoTIFF, COG）

数据溯源：本方法基于机器学习，以90米栅格单元分辨率，对0~5cm、5~15cm、15~30cm、30~60cm、60~100cm及100~200cm六个深度层级的土壤质地组分进行预测。该方法可处理野外实测数据转换为质地组分定量估算结果时的不确定性。研究采用现有的bootstrap重采样方法预测不确定性，以每个栅格单元预测均值的90%预测区间表示不确定性结果。通过外部验证数据集对模型及预测不确定性进行评估，并将结果与澳大利亚土壤与景观网格第1版（v1.SLGA，Viscarra Rossel等，2015）进行对比。所有预测与功能精度诊断结果均显示，本产品相较v1.SLGA有显著提升：砂粒与粘粒组分制图的均方根误差（Root Mean Square Error, RMSE）分别平均降低3%与2%；粉粒组分制图虽提升幅度较小，但也实现了小幅优化——粉粒组分本身属于较难预测的质地组分。本研究同时与最新发布的世界土壤网格产品（v2.WSG）进行了对比，得到了一致的结论。

本产品的全部生成流程均采用R编程语言完成。R核心开发团队（2020）. R：统计计算语言与环境. 奥地利维也纳统计计算R基金会，网址：https://www.R-project.org/.

代码仓库：https://github.com/AusSoilsDSM/SLGA
实测数据集：https://esoil.io/TERNLandscapes/Public/Pages/SoilDataFederator/SoilDataFederator.html
协变量栅格数据：https://esoil.io/TERNLandscapes/Public/Pages/SLGA/GetData-COGSDataStore.html