Malone and Searle (2021) described a new approach to convert field measured soil texture categories into quantitative estimates of the proportion of clay, silt and sand fractions. Converted data can seamlessly integrate with laboratory measured data into digital soil mapping workflow. Here, we describe updating the Australian national coverages of clay, sand and silt content. 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. This work demonstrates the need to continually revisit and if necessary, update existing versions of digital soils maps when new methods and efficiencies evolve. This agility is a key feature of digital soil mapping. However, without a companion program of new data acquisition through strategic field campaigns, continued re-modelling of existing data does have its limits and an eventual model skill ceiling will be reached which may not meet expectations for delivery of accurate national scale digital soils information.

Malone和Searle（2021）描述了一种新方法，可将现场测量的土壤质地类别转换为对粘土，粉砂和沙粒比例的定量估计。转换后的数据可以与实验室测量数据无缝集成到数字土壤测绘工作流程中。在这里，我们描述了更新澳大利亚国家对粘土，沙子和粉砂含量的覆盖率。该方法基于机器学习，可在90 m网格单元分辨率，0-5 cm，5-15 cm，15-30 cm，30-60 cm，60-100 cm和100-200深度下预测每个土壤质地分数厘米。该方法适应了将现场测量值转换为纹理分数的定量估计时的不确定性。利用现有的自举重采样方法来预测不确定性，不确定性表示为关于每个网格单元的平均预测的90％预测间隔。通过外部验证数据集评估模型和预测不确定性。将结果与澳大利亚的第1版土壤和景观网格（v1.SLGA）（Viscarra Rossel等。2015）。与v1.SLGA相比，所有预测性和功能准确性诊断程序均显示出改进。在RMSE估计中，注意到砂和粘土含量图的改善分别为3％和2％。淤泥分数测绘的边际改进，相对难以预测。我们还与最近发布的世界土壤网格产品（v2.WSG）进行了比较，并得出了类似的结论。这项工作表明，有必要不断重新审视，并在必要时随着新方法和效率的发展而更新数字土壤图的现有版本。这种敏捷性是数字土壤制图的关键功能。但是，如果没有通过战略性野战获得新数据的配套计划，