In the evolving field of digital soil mapping (DSM), the determination of sample size remains a pivotal challenge, particularly for large-scale regional projects. We introduced the Jensen-Shannon Divergence (DJS), a novel tool recently applied to DSM, to determine optimal sample sizes for a 2790 km2 area in Ontario, Canada. Utilizing 1791 observations, we generated maps for cation exchange capacity (CEC), clay content, pH, and soil organic carbon (SOC). We then assessed sample sets ranging from 50 to 4000 through conditioned Latin hypercube sampling (cLHS), feature space coverage sampling (FSCS), and simple random sampling (SRS) to calibrate random forest models, analyzing performance via concordance correlation coefficient and root mean square error. Findings reveal DJS as a robust estimator for optimal sample sizes—865 for cLHS, 874 for FSCS, and 869 for SRS, with property-specific optimal sizes indicating the potential for enhanced DSM accuracy. This methodology facilitates a strategic approach to sample size determination, significantly improving the precision of large-scale soil mapping. Conclusively, our research validates the utility of DJS in DSM, offering a scalable solution. This advancement holds considerable promise for improving soil management and sustainability practices, underpinning the critical role of precise soil data in agricultural productivity and environmental conservation.

中文翻译：

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数字土壤测绘的样本量优化：一个经验示例

在不断发展的数字土壤测绘（DSM）领域，样本量的确定仍然是一个关键挑战，特别是对于大型区域项目。我们引入了 Jensen-Shannon Divergence (DJS)，这是最近应用于 DSM 的一种新颖工具，用于确定加拿大安大略省 2790 平方公里区域的最佳样本量。利用 1791 个观测值，我们生成了阳离子交换容量 (CEC)、粘土含量、pH 值和土壤有机碳 (SOC) 的地图。然后，我们通过条件拉丁超立方抽样 (cLHS)、特征空间覆盖抽样 (FSCS) 和简单随机抽样 (SRS) 评估 50 到 4000 个样本集，以校准随机森林模型，通过一致性相关系数和均方根分析性能错误。研究结果表明，DJS 是最佳样本量的稳健估计器——cLHS 为 865，FSCS 为 874，SRS 为 869，特定属性的最佳样本量表明有可能提高 DSM 准确性。该方法有利于确定样本量的战略方法，显着提高大规模土壤测绘的精度。最后，我们的研究验证了 DJS 在 DSM 中的实用性，提供了可扩展的解决方案。这一进步为改善土壤管理和可持续性实践带来了巨大希望，巩固了精确土壤数据在农业生产力和环境保护中的关键作用。