In the last three decades, land resource assessment and soil survey has shifted from an expert-driven qualitative soil-landscape paradigm to a quantitative, correlative environmental modelling approach now commonly referred to as ‘digital soil mapping and assessment’ (DSMA). The role of soil surveyors in producing spatial soil and land suitability assessments under an operational paradigm of digital soil mapping is discussed using the recently completed Northern Australia Water Resources Assessment (NAWRA) project as a case study. Real world DSMA problems are presented, and pragmatic solutions suggested. In NAWRA, a stratified random sampling plan for data collection on ~500 new sites in the field was produced using conditioned Latin hypercube sampling using 30-m resolution environmental data layers that each represented a factor of soil formation but had low correlations between them. Some free survey sites were added in soil-landscapes that were under-represented in the stratified random sampling plan in the judgment of the soil surveyors. Predictive random forest models and gridded maps for soil classes and continuous soil attributes (and their prediction uncertainty) were produced at 90-m resolution in an iterative process that enabled the identification of issues, particularly in the legacy data included for model training. As a consequence errors in data entry in the state soil databases have been corrected. Another stratified random sampling plan using the prediction uncertainties was produced to select sites for field validation. Soil surveyors played a key role in all aspects of the DSMA workflow including identifying soil-landscapes that need to be mapped accurately and applying expert knowledge to the selection of final maps of soil classes and attributes necessary to implement land suitability rules.

在过去的三十年中，土地资源评估和土壤调查已从专家驱动的定性土壤-景观范式转变为一种定量，相关的环境建模方法，现在通常称为“数字土壤制图和评估”（DSMA）。以最近完成的北澳大利亚水资源评估（NAWRA）项目为案例，讨论了在数字土壤制图的操作范式下土壤调查员在进行空间土壤和土地适宜性评估中的作用。提出了现实世界中的DSMA问题，并提出了实用的解决方案。在NAWRA中 使用条件拉丁超立方体抽样方法，使用30 m分辨率的环境数据层（每个条件代表土壤形成的一个因素，但它们之间的相关性较低），制定了分层的随机抽样计划，用于在该地区约500个新站点收集数据。在土壤景观中添加了一些免费的调查点，但在土壤调查员的判断中，分层随机抽样计划中代表性不足的土壤调查点。针对土壤类别和连续土壤属性（及其预测不确定性）的预测性随机森林模型和网格地图是通过90 m分辨率的迭代过程生成的，该过程能够识别问题，尤其是模型训练中包含的遗留数据。结果，纠正了状态土壤数据库中数据输入中的错误。产生了另一个使用预测不确定性的分层随机抽样计划，以选择现场进行现场验证。土壤测量师在DSMA工作流程的所有方面都发挥了关键作用，包括确定需要准确绘制的土壤景观，并将专家知识应用于选择土壤类别和属性的最终地图，以实施土地适用性规则。