Large-scale mapping of soil particle size distribution using legacy data and machine learning-based pedotransfer functions

IF 5.6 1区 农林科学 Q1 SOIL SCIENCE Geoderma Pub Date : 2025-01-21 DOI:10.1016/j.geoderma.2025.117178
Piroska Kassai, Mihály Kocsis, Gábor Szatmári, András Makó, János Mészáros, Annamária Laborczi, Zoltán Magyar, Katalin Takács, László Pásztor, Brigitta Szabó
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Abstract

Large-scale maps of particle size fractions (i.e., sand, silt, and clay contents) were created for a case study based on the newly developed Profile-level Database of the Hungarian Large-Scale Soil Mapping (Hungarian acronym: NATASA). This database combines data from previous surveys, offering potential to improve soil mapping accuracy. The database includes information on soil taxonomy and basic soil chemical and physical properties. However, this database contains no direct information on sand, silt and clay content, only an indirect parameter, namely, the upper limit of soil plasticity. Particle size distribution is crucial for various applications, such as assessing soil degradation, hydrology and fertility. To overcome this limitation, we developed pedotransfer functions (PTFs) to compute the particle size distribution from the soil properties available in the NATASA dataset (1,372 soil profiles). The PTFs were trained and tested on the Hungarian Detailed Soil Hydrophysical Database (3,970 soil profiles) using the random forest method. For the prediction model, i) additive log-ratio transformed clay, silt and sand content were used as the dependent variables, and ii) the upper limit of soil plasticity, soil type, calcium carbonate content, organic matter content and pH were included as independent variables. The results indicate that the R2 values of the PTFs are 0.69 for clay, 0.58 for silt, and 0.74 for sand content. Since the NATASA database contains soil information from different depths, we splined the data into six standard depth layers (0–5, 5–15, 15–30, 30–60, 60–100 and 100–200 cm depths). The spatial modelling was performed by random forest kriging (RFK) using environmental auxiliary variables. The R2 values of the RFK models range from 0.19 to 0.67 for clay content, from 0.49 to 0.62 for silt content and from 0.69 to 0.74 for sand content. We compared the high-resolution (25 m) maps with the global SoilGrids (250 m resolution) and the national DOSoReMI.hu soil maps (100 m resolution). Our high-resolution maps offer more detailed information on clay, silt and sand content vertically and horizontally compared to global and national soil maps. This enhanced detail will facilitate future assessments of soil texture-related processes in the area.
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使用遗留数据和基于机器学习的土壤传递函数对土壤粒度分布进行大规模映射
基于新开发的匈牙利大规模土壤制图(匈牙利首字母缩略词:NATASA)的剖面级数据库,为案例研究创建了粒度分数(即沙子,淤泥和粘土含量)的大比例尺地图。该数据库结合了以前调查的数据,提供了提高土壤制图精度的潜力。该数据库包括土壤分类和基本的土壤化学和物理性质信息。然而,该数据库中没有砂、粉和粘土含量的直接信息,只有一个间接参数,即土壤塑性上限。粒度分布对各种应用至关重要,例如评估土壤退化、水文和肥力。为了克服这一限制,我们开发了土壤传递函数(ptf)来计算来自美国国家航空航天局数据集中(1372个土壤剖面)的土壤性质的粒度分布。采用随机森林方法在匈牙利详细土壤水物理数据库(3970个土壤剖面)上对ptf进行了训练和测试。预测模型以加性对数比转化粘土、粉砂含量为因变量,以土壤塑性上限、土壤类型、碳酸钙含量、有机质含量、pH值为自变量。结果表明,黏土、粉土、砂粒的PTFs R2分别为0.69、0.58和0.74。由于NATASA数据库包含来自不同深度的土壤信息,我们将数据样条分为6个标准深度层(0 - 5,5 - 15,15 - 30,30 - 60,60 - 100和100-200 cm深度)。采用随机森林克里格法(RFK)对环境辅助变量进行空间建模。RFK模型粘土含量的R2值为0.19 ~ 0.67,粉砂含量的R2值为0.49 ~ 0.62,砂粒含量的R2值为0.69 ~ 0.74。我们将高分辨率(25米)地图与全球SoilGrids(250米分辨率)和国家DOSoReMI进行了比较。胡土壤图(100米分辨率)。与全球和国家土壤地图相比,我们的高分辨率地图在垂直和水平方向上提供了更多关于粘土、淤泥和沙子含量的详细信息。这一增强的细节将有助于未来对该地区土壤质地相关过程的评估。
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来源期刊
Geoderma
Geoderma 农林科学-土壤科学
CiteScore
11.80
自引率
6.60%
发文量
597
审稿时长
58 days
期刊介绍: Geoderma - the global journal of soil science - welcomes authors, readers and soil research from all parts of the world, encourages worldwide soil studies, and embraces all aspects of soil science and its associated pedagogy. The journal particularly welcomes interdisciplinary work focusing on dynamic soil processes and functions across space and time.
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