Texture is a fundamental soil property for multiple applications in environmental and earth sciences. Knowing its spatial distribution allows a better understanding of the response of soil conditions to changes in the environment, such as land use. This paper describes the technical development of Colombia's first texture maps, obtained via a spatial ensemble of national and global digital soil mapping products. This work compiles a new database with 4203 soil profiles, which were harmonized at five standard depths (0–5, 5–15, 15–30, 30–60, and 60–100 cm) and standardized with additive log ratio (ALR) transformation. A compilation of 83 covariates was developed and harmonized at 1 km² of spatial resolution. Ensemble machine learning (EML) algorithms (MACHISPLIN and landmap) were trained to predict the distribution of soil particle size fractions (PSFs) (clay, sand, and silt), and a comparison with SoilGrids (SG) products was performed. Finally, a spatial ensemble function was created to identify the smallest prediction errors between EML and SG. Our results are the first effort to build a national texture map (clay, sand, and silt fractions) based on digital soil mapping in Colombia. The results of EML algorithms showed that their accuracies were very similar at each standard depth, and were more accurate than SG. The largest improvement with the spatial ensemble was found at the first layer (0–5 cm). EML predictions were frequently selected for each PSF and depth in the total area; however, SG predictions were better when increasing soil depth in some specific regions. The final error distribution in the study area showed that sand presented higher absolute error values than clay and silt fractions, specifically in eastern Colombia. The spatial distribution of soil texture in Colombia is a potential tool to provide information for water-related applications, ecosystem services, and agricultural and crop modeling. However, future efforts need to improve aspects such as treating abrupt changes in the texture between depths and unbalanced data. Our results and the compiled database (https://doi.org/10.6073/pasta/3f91778c2f6ad46c3cc70b61f02532db, Varón-Ramírez and Araujo-Carrillo, 2022, https://doi.org/10.6073/pasta/d6c0bf5847aa40836b42dcc3e0ea874e, Varón-Ramírez et al., 2022) provide new insights to solve some of the aforementioned issues.

中文翻译：

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哥伦比亚土壤质地：构建空间集合模型

质地是环境和地球科学中多种应用的基本土壤特性。了解其空间分布可以更好地了解土壤条件对环境变化（例如土地利用）的响应。本文介绍了哥伦比亚第一张纹理图的技术发展，该图是通过国家和全球数字土壤测绘产品的空间集合获得的。这项工作编译了一个包含 4203 个土壤剖面的新数据库，这些剖面在五个标准深度（0-5、5-15、15-30、30-60 和 60-100 厘米）上进行了协调，并使用加性对数比 (ALR) 进行了标准化转型。在 1 km ^{2处开发和协调了 83 个协变量的汇编}的空间分辨率。训练了集成机器学习 (EML) 算法（MACHISPLIN 和landmap）来预测土壤粒度分数（PSF）（粘土、沙子和淤泥）的分布，并与 SoilGrids (SG) 产品进行了比较。最后，创建了一个空间集成函数来识别 EML 和 SG 之间的最小预测误差。我们的结果是首次基于哥伦比亚的数字土壤测绘构建国家纹理图（粘土、沙子和淤泥部分）。EML 算法的结果表明，它们在每个标准深度上的精度非常相似，并且比 SG 更准确。在第一层（0-5 cm）发现了空间集合的最大改进。经常为每个 PSF 和总区域中的深度选择 EML 预测；然而，在某些特定区域增加土壤深度时，SG 预测更好。研究区的最终误差分布表明，沙子的绝对误差值高于粘土和粉砂部分，特别是在哥伦比亚东部。哥伦比亚土壤质地的空间分布是为水相关应用、生态系统服务以及农业和作物建模提供信息的潜在工具。然而，未来的努力需要改进诸如处理深度和不平衡数据之间纹理的突然变化等方面。我们的结果和编译的数据库（https://doi.org/10.6073/pasta/3f91778c2f6ad46c3cc70b61f02532db，特别是在哥伦比亚东部。哥伦比亚土壤质地的空间分布是为水相关应用、生态系统服务以及农业和作物建模提供信息的潜在工具。然而，未来的努力需要改进诸如处理深度和不平衡数据之间纹理的突然变化等方面。我们的结果和编译的数据库（https://doi.org/10.6073/pasta/3f91778c2f6ad46c3cc70b61f02532db，特别是在哥伦比亚东部。哥伦比亚土壤质地的空间分布是为水相关应用、生态系统服务以及农业和作物建模提供信息的潜在工具。然而，未来的努力需要改进诸如处理深度和不平衡数据之间纹理的突然变化等方面。我们的结果和编译的数据库（https://doi.org/10.6073/pasta/3f91778c2f6ad46c3cc70b61f02532db，Varón-Ramírez 和 Araujo-Carrillo，2022 年，https: //doi.org/10.6073/pasta/d6c0bf5847aa40836b42dcc3e0ea874e，Varón-Ramírez 等人，2022 年）为解决上述一些问题提供了新的见解。