Organic Matter Removal on Oxide Determination in Oxisols Via Portable X-ray Fluorescence

Many factors can influence the results obtained by portable X-ray fluorescence analysis (pXRF). The effect of soil organic matter on pXRF results is not satisfactory understood. Thus, we conducted this study to verify the effect of organic matter removal on oxide determination by pXRF in Oxisols. To obtain soil material with different organic matter contents and maintaining the same elemental composition from soil minerals, six contrasting Oxisols were heated in a muffle furnace for 30 min at the following temperatures (°C): 100; 200; 300; 400; 500 and 600. After heating, the soil samples were scanned using a pXRF Bruker® S1 Titan LE model (Dual Soil mode) for 60 s and the contents of SiO₂, Al₂O₃, Fe₂O₃, TiO₂, P₂O₅, and MnO were recorded. The soil organic matter presence underestimated the pXRF results for lightest oxides (Si and Al) compared to heaviest oxides (Fe, Ti, and Mn). These oxides are important for tropical soils classification and for many soil-related studies and pXRF technology has been a useful tool for soil chemical characterization. Our findings contribute to more suitable use of pXRF highlighting the possible effect of organic matter.     

结合统计和数字地形数据的可视化方法预测土壤深度

Information about the spatial distribution of soil attributes is indispensable for many land resource management applications; however, the ability of soil maps to supply such information for modern modeling tools is questionable. The objectives of this study were to investigate the possibility of predicting soil depth using some terrain attributes derived from digital elevation models (DEMs) with geographic information systems (GIS) and to suggest an approach to predict other soil attributes. Soil depth was determined at 652 field observations over the Al-Muwaqqar Watershed (70 km²) in Jordan. Terrain attributes derived from 30-m resolution DEMs were utilized to predict soil depth. The results indicated that the use of multiple linear regression models within small watershed subdivisions enabled the prediction of soil depth with a difference of 50 cm for 77% of the field observations. The spatial distribution of the predicted soil depth was visually coincided and had good correlations with the spatial distribution of the classes amalgamating three terrain attributes, slope steepness, slope shape, and compound topographic index. These suggested that the modeling of soil-landscape relationships within small watershed subdivisions using the three terrain attributes was a promising approach to predict other soil attributes.     

Identification of key soil and terrain properties that influence the spatial variability of soil moisture throughout the growing season

Combining soils and terrain information is the key to understanding hydrological processes at a landscape scale. Increasing the scale of soil maps has been shown to allow the spatial patterns of soil moisture to be more fully represented in the landscape, but soil data are often only available at reconnaissance scales (e.g. 1:250 000). It is widely acknowledged that soil hydrological properties vary within the landscape and there are widely available digital terrain models at a 10-m grid resolution in the UK. The aim of this study was to investigate soil moisture variations and how soil and terrain data can be used in combination to explain the spatial variation in soil moisture contents. Field monitoring of surface soil moisture content on eight occasions in three different fields in Bedfordshire (UK) was undertaken between April and July in 2004 and 2005. Between 100 and 120 points were sampled in each survey using a Delta-T ML2x™ Theta Probe. The results from regression models show that up to 80% of the variation in surface soil moisture can be explained using 1:10 000 soil series maps and terrain variables. Short-wave radiation on a sloping surface (SWRSS), calculated by SRAD, and a topographic wetness index combined explained a maximum of 44% of the variation. The results show that the terrain effect on soil moisture is modified by soils. The additional variation explained by adding 1:10 000 soil series information to terrain variables was up to 50% and adding 1:25 000 soil series information increased the variation explained by up to 29%. The interactions in the variation explained by soils and landscape indices at different scales tie in with the concept of hydropedology. It also has implications for data requirements for modelling soil hydrological response and associated soil functions.     

基于GIS和随机森林算法的宁东土壤饱和导水率分布与预测

为探明宁东土壤饱和导水率(Ks)的空间分布特征,在宁东采集136个原状土,采用经典统计和地统计方法分析土壤Ks的空间结构特征,并以地形因子、土壤属性等作为辅助变量,运用随机森林法(RF)、普通克里格法(OK)和逐步回归克里格法(RK)对区域土壤Ks进行预测并对3种方法的预测结果进行精度评价。结果表明:Ks介于0.05~7.13 mm/min,平均值为1.46 mm/min,变异系数为106.86%;Ks与容重、孔隙度、高程、坡度、坡向、平面曲率和剖面曲率在不同滞后距离下具有自相关关系和交互相关关系;土壤Ks块金值为38,表明随机因素引起的土壤Ks变异性较大,空间异质比为15.32%,在空间上呈现强变异性;RF法的预测精度最高,其平均相对误差(MRE)和均方根误差(RMSE)绝对值均为最小,相比OK和RK方法预测精度分别提高了5.53%和2.49%,且对局部细节的描述更准确、模拟效果最佳。RF法可以较为准确的预测宁东土壤Ks,为了解研究区土壤水文过程及林草植被建设提供数据参考。     

机器学习用于耕地土壤有机碳空间预测对比研究——以亚热带复杂地貌区为例

耕地土壤有机碳(SOC)是土壤质量的重要指标,也是生态系统健康的重要表征。当前机器学习(Machine Learning, ML)用于SOC数字制图日益热门,但不同算法在高空间分辨率SOC数字制图中的对比研究尚有欠缺。本研究以福建省东北部复杂地形地貌区为例,采用10m空间分辨率Sentinel-2影像数据,选取地形、气候、遥感植被变量为驱动因子,重点分析当前常用的机器学习算法——支持向量机(SupportVector Machine,SVM)、随机森林(RandomForest,RF)在SOC预测中的差异,并与传统普通克里格模型(Ordinary Kriging, OK)进行比较。结果表明:基于地形、遥感植被因子和气候因子构建的RF模型表现最佳(RMSE=2.004,r=0.897),其精度优于OK模型(RMSE=4.571, r=0.623),而SVM模型预测精度相对最低(RMSE=5.190, r=0.431);3种模型预测SOC空间分布趋势总体相似,表现为西高东低、北高南低,其中RF模型呈现的空间分异信息更加精细;最优模型反演得到耕地土壤有机碳平均含量为15.33 g·kg-1; RF模型和SVM模型变量重要性程度表明:高程和降水是影响复杂地貌区SOC空间分布的重要变量,而遥感植被因子重要性程度低于高程。     

Advances in Tropical Soil Characterization via Portable X-Ray Fluorescence Spectrometry

Portable X-ray fluorescence (pXRF) spectrometers can be used to determine the elemental composition easily, rapidly, and without using chemical reagents, which is very important for tropical regions due to the lack of detailed soil characterization data. Moreover, pXRF data can be used to predict the results of more expensive, time-consuming, and conventional laboratory analyses. This study sought to determine the elemental composition of various soil profiles using pXRF. Two operational modes (Trace Mode and General Mode) and two scanning time (30 and 60 s) were assessed to determine their effects on the correlation of pXRF dataset with respect to conventional inductively coupled plasma (ICP)-optical emission spectrometry analysis. This relationship has been reported in previous studies, however, few studies were performed on tropical soils, which are unique. Furthermore, such relationships establish the viability of developing prediction models directly from pXRF data. Linear regression was applied to develop calibration models for the prediction of ICP analysis results and exchangeable and available elemental contents based on pXRF data. High coefficients of determination (R²) were obtained for Ca (0.87), Cu (0.90), Fe (0.95), Mn (0.85), Cr (0.95), V (0.72), and Ni (0.90), with adequate validation. Statistically significant results were not found for Al, K, Zn, Ti, and Zr. The models predicting the exchangeable Ca based on the total Ca from pXRF reached an R² of up to 0.85. Operational modes influenced the pXRF results. Our results illustrate that pXRF holds great promise for tropical soil characterization and the development of prediction models, justifying the need for larger-scale studies in tropical countries worldwide.     

Regional-scale modelling of the spatial distribution of surface and subsurface textural classes in alluvial soils using Markov chain geostatistics

Soil texture is directly associated with other soil physical and chemical properties and can affect crop yield, erodibility and water and pollutant movement. Thus, maps of soil textural class are valuable for agricultural management. Conventional spatial statistical methods do not capture the complex large-scale spatial patterns of multi-class variables. Markov chain geostatistics (MCG) was recently proposed as a new approach for the conditional simulation of categorical variables. In this study, we apply an MCG algorithm to simulate the spatial distribution of textural classes of alluvial soils at five different depths in a 15-km² area on the North China Plain. Soil texture was divided into five classes – sand, sandy loam, light loam, medium loam and clay. Optimal prediction maps, simulated maps and occurrence probability maps for each depth were generated from sample data. Simulated results delineated the distribution of the five soil textural classes at the five depths and quantified related spatial uncertainties caused by limited sample size (total of 139 points). These results are not only useful for understanding the spatial distribution of soil texture in alluvial soils, but also provide valuable quantitative information for precision agriculture, soil management and studies on environmental processes affected by surface and subsurface soil textures.     

Effect of spatial scale of soil data on estimates of soil ecosystem services: Case study in 100 km2 area in France

Over the last decade, the ecosystem services (ESs) framework has been increasingly used to support mapping and assessment studies for sustainable land management purposes. Previous analysis of practical applications has revealed the significance of the spatial scale at which input data are obtained. This issue is particularly problematic with soil data that are often unavailable or available only at coarse scales or resolutions in various part of the world. In this context, four soil-based ecosystem services, namely biomass provision, water provision, global climate regulation, and water quality regulation, are assessed using three conventional soil maps at the 1:1,000,000, 1:250,000 and 1:50,000 scales. The resulting individual and joint ES maps are then compared to examine the effects of changing the spatial scale of soil data on the ES levels and spatial patterns. ES levels are finally aggregated to landforms, land use, or administrative levels in order to try to identify the determinants of the sensitivity of ES levels to change in the scale of input soil data. Whereas the three soil maps turn out to be equally useful whenever ESs levels averaged over the whole 100 km² territory are needed, the maps at the 1:1,000,000 and 1:250,000 induced biases in the assessment of ESs levels over spatial units smaller than 100 and 10 km², respectively. The simplification of the diversity and spatial distribution of soils at the two coarsest scales indeed resulted in local differences in ES levels ranging from several 10 to several 100%. Identification of the optimal representation of soil diversity and distribution to obtain a reliable representation of ESs spatial distribution is not straightforward. The ESs sensitivity to scale effect is indeed context-specific, variable among individual ESs, and not directly or simply linked with the soil typological diversity represented in soil maps. Forested and natural lands in the study area appear particularly sensitive to soil data scales as they occupy marginal soils showing very specific ESs signatures.     

Classification of soil types using geographic object-based image analysis and random forests

There is increasing interest in developing automatic procedures to segment landscapes into soil spatial entities that replace conventional, expensive manual procedures for delineating and classifying soils. Geographic object-based image analysis (GEOBIA) partitions remote sensing imagery or digital elevation models into homogeneous image objects based on image segmentation. We used an object-based methodology for the detailed delineation and classification of soil types using digital maps of topography and vegetation as soil covariates, based on the Random Forests (RF) classifier. We compared the object-based method's results with those of a pixel-based classification using the same classifier. We used 18 digital elevation model derivatives and 5 remote sensing indices that were related to vegetation cover and soil. Using 171 soil profiles with their associated environmental variable values, the RF method was used to identify the most important soil type predictors for use in the segmentation process. A stack of raster-geodatasets corresponding to the selected predictors was segmented using a multi-resolution segmentation algorithm, which resulted in homogeneous objects related to soil types. These objects were further classified as soil types using the same method, RF. We also conducted a pixel-based classification using the same classifier and soil profiles, and the resulting maps were assessed in terms of their accuracy using 30% of the soil profiles for validation. We found that GEOBIA was an effective method for soil type mapping, and was superior to the pixel-based approach. The optimized object-based soil map had an overall accuracy of 58%, which was 10% higher than that of the optimized pixel-based map.     

Relationship between soil depth and terrain attributes in karst region in Southwest China

Purpose

Soil depth generally varies in peak-cluster depression regions in rather complex ways. Because conventional soil survey methods in these regions require a considerable amount of time, effort, and consequently relatively large budget, new methods are required in karst regions.

Materials and methods

This study explored the relationship between soil depth and terrain attributes abstracted from digital elevation models (DEMs) at different spatial resolutions in the Guohua Karst Ecological Experimental Area, a representative region of peak-cluster depression in Southwest China. A uniform 140 m?×?140 m grid combined with representative hillslope methodology was used to select 171 sampling points where soil depth was measured. Nine primary and secondary terrain attributes, such as elevation, slope, aspect, especial catchment area, wetness index, length-slope factor, stream power index, relief degree of land surface, and distance from ridge of mountains, were computed from DEMs at different spatial resolutions. The optimal DEM spatial resolution was determined by Grey relational analysis (GRA) to reflect the correlations between soil depth and terrain attributes.

Results and discussion

GRA revealed that the 10-m spatial resolution DEM can best reflect the relationship between soil depth and terrain attributes; therefore, the terrain attributes at this resolution were used for multiple linear stepwise regression (MLSR) analysis. The result of MLSR indicated that slope, TWI, and elevation could explain about 61.4 % of the total variability in soil depth in the study area.

Conclusions

The terrain attributes of slope, WTI and elevation can be used to evaluate soil depth in this region very well. This proposed approach may be applicable to other peak-cluster depression regions in the karst areas at a larger scale.     

Using Moran's I and geostatistics to identify spatial patterns of soil nutrients in two different long‐term phosphorus‐application plots

The spatial variation of soil nutrients especially the soil test phosphorus (STP) in grassland soils is becoming important because of the use of soil‐nutrients information as a basis for policies such as the recently EU‐introduced Nitrates Directive. Up to now, the small‐scale spatial variation of soil nutrients in grassland has not been studied. The main aim of this study was to investigate the spatial patterns of soil nutrients in two grazed grassland plots with a long‐term (38 y) P‐application experiment, in order to better understand the spatial variation of soil nutrients and the correlation among soil nutrients in grasslands. Two small areas (one from a high‐P background and the other from a medium‐P background) were selected. Soil samples (304 per study area) were collected based on a 1 m × 1 m grid system. The samples were analyzed for STP, Mg, K, pH, and lime requirement (LR). The results were analyzed using conventional statistics, Moran's I, geostatistics, and a GIS. Based on the global Moran's I values, significant positive spatial autocorrelations were found for STP, Mg, pH, and LR in both study areas. Spatial clusters and spatial outliers were detected using the local Moran's I index. Clear linear‐shaped high‐high or low‐low value clusters of the studied variables except K were observed in the study areas due to long‐term usage of machine spreader or other agricultural‐management methods in the past. The corresponding linear patterns were further found in the spatial‐distribution maps. Small spatial patches were found for soil K revealing that it had a random spatial distribution related to the relatively uniform K fertilizer in the study areas. The spatial clusters revealed by local Moran's I were in line with the spatial patterns in the distribution maps.     

Sampling procedure simulating on‐the‐go sensing for soil nutrients

The emergence of a new sensor technology based on the use of ion‐selective membranes provides an increasing number of opportunities for on‐the‐go field measurements of soil nutrients and soil pH. In the future, on‐the‐go sensing should provide a cost‐effective monitoring of heterogeneous soils with high sampling resolution. It is suitable for site‐specific management because it can be focused on the spatial representativity of observation. This study evaluates the on‐the‐go‐sensing sampling design by comparing it with a standard approach to soil sampling for soil pH and the base nutrients P, K, and Mg under local field conditions in Germany. Soil samples were taken in two test sites at a resolution and in a manner as if they were sampled with an on‐the‐go sensing system and were compared with soil samples taken at a coarser resolution and with standard methods. In general, a higher variability was observed among the on‐the‐go samples due to their smaller sample support. The finer sampling resolution of the on‐the‐go design improved field‐scale semivariogram‐analysis results, identifying the spatial structures for soil pH, P, and Mg clearly. In addition, kriged maps of these soil parameters had predominantly higher estimation accuracies. However, the on‐the‐go samples were strongly influenced by the small‐scale variability of K in one of the test sites. This variability increased the kriging standard deviation for K by 50% compared with standard sampling design. Despite of this problem, the on‐the‐go‐sensing sampling design revealed field‐scale spatial variability for base nutrient status more accurately. Except for K, the mean absolute error of fertilizer‐application maps was reduced when using the on‐the‐go sample design in comparison with the standard sample design (Ca: 210/268 kg ha^–1, P: 2.85/6.75 kg ha^–1, K: 13.7/6.0 kg ha^–1, Mg 5.7/6.8 kg ha^–1). This will reduce over‐ and underfertilization using variable‐rate fertilizer‐application systems. In the future, it will be of interest if real on‐the‐go soil‐sensor measurements exhibit the same variability behavior addressed here or if results will differ substantially.     

不同土壤物理属性空间分布对水文过程模拟的影响

基于不同空间分辨率的土壤物理属性数据(土壤田间持水量、孔隙度和饱和水力传导度),运用WATLAC分布式水文模型模拟了西苕溪流域2005-2010年的水文过程。对比评价了土壤物理属性空间分布对地下水补给、土壤蒸发、径流量及其组分的影响。结果显示,利用不同空间分辨率土壤物理属性数据模拟的流量过程与实测值都匹配的很好,模拟精度相当,更详尽的土壤属性空间分布信息未能明显提高模型模拟的精度;而对于地下水补给量,高分辨率的土壤属性空间分布会使其模拟结果大幅减小,但对土壤蒸发量则影响较小,两种数据模拟的结果及其空间分布都很接近;同时,虽然土壤物理属性空间分布的分辨率对模拟的径流总量影响甚微,但对基流与地表径流的分割却产生了较大影响。     

水文地貌关系正确DEM的建立方法

 水文地貌关系正确DEM(hydrologically correct DEMs,Hc-DEM),是指符合水文地貌学基本原理,正确反映水文要素(水流方向、水流路径、水系网络、流域界线等)与地貌特征发生和位置关系的DEM。区域尺度水文和土壤侵蚀等研究中,地形因子参数只能利用DEM来提取,为了准确反映地面形态,有效提取地貌和水文特征因子,建立Hc-DEM是必需的。笔者对Hc-DEM的概念、建立方法进行了讨论和介绍;以黄土高原为例,提出了利用多种比例尺数字地形图和ANUDEM软件建立DEM的关键参数;通过与TIN方法建立的DEM的比较,对所建立的DEM进行了简要评价。研究表明,利用我国已有的数字地形图和ANUDEM软件,可以建立Hc-DEM,为流域水文和区域尺度水土流失定量分析模拟、区域尺度植被适宜性评价等研究提供更加直接的数据支持。     

广元植烟土壤有效态微量元素的空间变异特征及影响因素

为揭示广元植烟区土壤有效态微量元素含量空间变异特征及影响因素,采用地统计学、相关分析及回归分析等研究方法,结合地理信息系统(GIS)技术对研究区土壤有效铁、锰、铜、锌钼及硼等6种土壤有效态微量元素进行分析。结果表明,研究区土壤有效铁、锰及铜含量丰富,总体处于中等以上水平;有效钼含量适中;有效锌和硼缺乏,总体处于缺乏或极度缺乏水平。半方差分析表明,6种土壤有效态微量元素块金系数均在29.72%~67.59%之间,具有中等强度的空间自相关性,其空间变异受结构性因素和随机性因素共同影响。空间分布上,土壤有效铁、锰、铜及钼含量表现出北高南低的空间分布趋势,土壤有效锌和硼含量呈现出西高东低的空间分布格局。影响因素分析表明,6种土壤有效态微量元素与土壤有机质呈正相关,与p H值呈负相关,相关性总体高于地形因子。土壤有机质对有效铁、铜、锌及硼有极显著影响,其空间变异性为5.5%~27.2%。除有效锌外,土壤p H值对其余5种土壤有效态微量元素含量空间变异均有极显著影响,其空间变异性为5.0%~30.4%。土类对土壤有效铁、锰及铜有极显著影响,其空间变异性8.4%~12.3%。熟制和种植制度对6种土壤有效态微量元素含量空间变异的影响较弱,进一步说明研究区土壤有效态微量元素含量空间变异受结构性因素和随机性因素共同影响,但结构性因素的作用强于随机性因素。本研究结果为广元植烟区土壤微肥施肥管理及优质烤烟栽培提供了可靠的参考依据。     

Spatial pattern consistency and repeatability of proximal soil sensor data for digital soil mapping

Data from proximal soil sensors can facilitate digital soil mapping at high spatial resolutions. However, their use for predicting static soil properties, such as texture, is affected by spatio-temporal changes in environmental and measurement conditions. In this research study, seasonal changes in spatial patterns and repeatability of data provided by a platform that simultaneously measures the red (Red) and near infrared (NIR) reflectance, apparent soil electrical conductivity (ECa), temperature, and volumetric moisture content of topsoil (at 3–6 cm depth) were assessed. Test fields are located in Southern Finland with textures dominated by clay and fine sandy till. During single scans, mean relative differences between the data from duplicated measurement points ranged from ~4% to 6% and were the highest for temperature and Red values. The consistency of spatial patterns across seasons (spring and autumn 2021 and 2022) was the highest for ECa values, and the lowest for NIR. ECa and moisture were significant for predicting the clay contents at a cereal grain crop site, whereas temperature was significant at grass ley sites. Errors were generally lower when using spring data compared with autumn data (RMSE ranging from 4.8% to 11.1% for the data from different fields and measurement dates). For the fields, where static soil properties change at small spatial scales, spatially detailed moisture and temperature data support the understanding of seasonal changes in the spatial patterns derived from multi-sensor data, and the corresponding changes in the performance of calibration models.     

喀斯特峰丛洼地原生林区土壤矿质元素空间异质性研究

运用经典统计学和地统计学方法研究了喀斯特峰丛洼地原生林保护区土壤矿质元素钙、镁、硅、铁、铝、锰的空间变异和相关关系,结果表明,在喀斯特峰丛洼地原生林区土壤矿质元素存在空间上的异质性。从半变异函数的模型拟合来看,块金值与基台值的比均小于25%的标准,表明变量具有强烈的空间相关性,且由结构性因素引起的空间变异占主导因素。Kriging等值线图显示,在不同的坡位出现了高值区和低值区,硅、铁和铝在中上坡出现高值区,而钙、镁和锰在中下坡出现低值区,这6个指标在研究区域均出现围绕1~3个中心点聚集分布出现高值或低值区,说明气候、母质、地形和微地貌对喀斯特原生林区土壤矿质元素空间变异起主导作用。     

区域水土流失地形因子的地图学分析

地形是影响区域水土流失的主要因子,基于中小栅格DEM提取的坡度是区域水土流失地形因子的主要指标之一。根据地貌学和地图制图学论文分析和对地形图及其DEM图形分析表明,在一定比例尺范围内（1∶1万~1∶25万）,多种比例尺的地形图均能表现区域地形的宏观结构特征;随着DEM分辨率的降低,在DEM上量算得到的坡度不断趋于平缓;由于制图综合不直接影响高程的数值,所以中小栅格DEM表现地面起伏的能力是存在的,只是发生了一定的变形而已。     

Mapping soil pH levels across Europe: An analysis of LUCAS topsoil data using random forest kriging (RFK)

Soil pH affects food production, pollution control and ecosystem services. Mapping soil pH levels, therefore, provides policymakers with crucial information for developing sustainable soil use and management policies. In this study, we used the LUCAS 2015 TOPSOIL data to map soil pH at a European level. We used random forest kriging (RFK) to build a predictive model of spatial variability of soil pH, as well as random forest (RF) without co-kriging and boosted regression trees (BRT) modelling techniques. Model accuracy was evaluated using a ten-fold cross-validation procedure. While we found that all methods accurately predicted soil pH, the accuracy of the RFK method was best with regression performance metrics of: R² = 0.81 for pH (H₂O) and pH (CaCl₂); RMSE = 0.59 for pH (H₂O) and RMSE = 0.61 in pH (CaCl₂); MAE = 0.41 for pH (H₂O) and MAE = 0.43 in pH (CaCl₂). Dominant explanatory variables in the RF and BRT modelling were topography and remote sensing variables, respectively. The generated maps broadly depicted similar spatial patterns of soil pH, with an increasing gradient of soil pH from north to south Europe, with the highest values mainly concentrated along the Mediterranean coast. The mapping could provide spatial reference for soil pH assessment and dynamic monitoring.     

Analysis on pedodiversity and spatial subset representativity—the German soil map 1:1,000,000

Knowledge about spatial soil variation in terms of measured pedodiversity, as well as the spatial distribution of soils in terms of spatial subset representativity, offers the possibility to estimate the quality and variance within a soil map. Additionally, it can help to identify representative sample locations. Demonstrated at the German soil map at a scale of 1:1,000,000, this study describes a methodology to analyze the distribution of taxonomical pedodiversity using the Simpson index and a new approach to derive representative spatial subsets based on a modified χ²‐test (χ_m²), which can be used as monitoring areas. To analyze the spatial composition of the soil map and to detect differences in the underlying mapping schemes of the German soil map 1:1,000,000, three different spatial data structures were studied: (1) the entire soil map, (2) the soil map segmented into geomorphological regions, and (3) the soil map segmented into the Federal States of Germany. Representative patches of varying sizes were statistically derived for all spatial subsets as well as the entire soil map ranging from 20 km × 20 km up to 70 km × 70 km. The results show that the measured pedodiversity is linked to both the geomorphology as well as the political borders of the Federal States. On the one hand, this reveals the uncertainty of measuring pedodiversity on the basis of soil‐class maps as the spatial representation of pedodiversity is influenced by the different mapping traditions and methods applied in the 16 Federal States of Germany. On the other hand, it allows the analysis of the aggregation schemes of different landscapes. The presented approach helps to understand large soilscapes and to compare different soil maps of different states and countries as well as to enhance the soil map with additional information. Furthermore, the representative patches can be used to select soil‐monitoring areas.