Using scattered hyperspectral imagery data to map the soil properties of a region

Airborne hyperspectral imagery has been recently proved to be a successful technique for predicting soil properties of the bare soil surfaces that are usually scattered in the landscape. This new soil covariate could much improve the digital soil mapping (DSM) of soil properties over larger areas. To illustrate this, we experimented with digital soil mapping in a 24.6‐km² area located in the vineyard plain of Languedoc. As input data, we used 200 points with clay content measurements and 192 bare soil fields representing 3.5% of the total area in which the clay contents of the soil surface were successfully mapped at 5‐m resolution by hyperspectral remote sensing. The clay contents were estimated from CR₂₂₀₆, a spectrometric indicator that quantifies specific absorption features of clay at 2206 nm. We demonstrated by cross‐validation that the co‐kriging procedure based on our co‐regionalization model provided accurate error estimates at the clay measurement sites. Then, we applied a block co‐kriging model to map the mean clay content at increasing resolutions (50 , 100, 250 and 500 m). The results showed the following: (i) using hyperspectral data significantly increased the accuracy of the mean clay content estimations; (ii) a block co‐kriging procedure with reliable estimates of error variance can be used to estimate mean clay contents over larger areas and at coarser resolutions with acceptable and predictable errors and (iii) various maps can be produced that represent different compromises between prediction accuracy and spatial resolution.     

A method to generate soilscapes from soil maps

Digital soil mapping for large areas is challenging if mapping resolution should be as high as possible and sampling should be as sparse as possible. Generally, the more complex the soil associations in a landscape, the more samples are required to systematically cover the entire feature space. Moreover, regions should be modeled separately if the patterns of spatial variation vary on subregion level. A systematic segmentation of a landscape into soilscapes is additionally important for a feasible application of soil‐sensing approaches. In this paper, we introduce a semiautomated approach to segment nominal spatial datasets based on the local spatial frequency distribution of the mapping units. The aim is to provide homogeneous and nonfragmented segments with smoothed boundaries. The methodological framework for the segmentation comprises different spatial and nonspatial techniques and focuses mainly on a moving‐window analysis of the local frequency distribution and a k‐means cluster analysis. Based on an existing soil map (1:50,000), we derived six segments for the Nidda catchment (Central Hesse, Germany), comprising 1600 km². As segmentation is based on a soil map, soilscapes are derived. In terms of the feature space, these soilscapes show a higher homogeneity compared to the entire landscape. Advantages compared to an existing map of landscape units are discussed. Segmenting a landscape as introduced in this study might also be of importance for other disciplines and can be used as a first step in biodiversity analysis or setting up environmental‐monitoring sites.     

Digital soil mapping of available water content using proximal and remotely sensed data

Two‐thirds of all irrigated agriculture in Australia is undertaken within the Murray–Darling Basin. However, climate change predictions for this region suggest rainfall will decrease. To maintain profitability, more will need to be done by irrigators with less water. In this regard, irrigators need to be aware of the spatial distribution of the available water content (AWC) in the root‐zone (i.e. 0.0–0.90 m). To reduce the cost, digital soil mapping (DSM) techniques are being used to map soil properties related to AWC (e.g. soil texture). The purpose of this study was to create a DSM of the AWC at the district scale. This is achieved by determining AWC by the difference between laboratory measured permanent wilting point (PWP) and field capacity (FC) and using pressure plate apparatus. The PWP and FC data are coupled to remote (i.e. gamma‐ray spectrometry) and proximal (i.e. EM38 and EM34) sensed data and two trend surface parameters. Using a hierarchical spatial regression (HSR), we predict PWP and FC across the areas of Warren and Trangie in the lower Macquarie valley, Australia. The reliability of the DSM of PWP and FC were compared using prediction precision (RMSE – root mean square error) and bias (ME – mean error). The best results were achieved using EM38‐v, EM34‐20, eU and eTh. The DSM map of AWC is consistent with known Pedoderms and provides a basis for agricultural water management.     

Hand-feel soil texture observations to evaluate the accuracy of digital soil maps for local prediction of soil particle size distribution: A case study in Central France

Digital maps of soil properties are now widely available. End-users now can access several digital soil mapping (DSM) products of soil properties, produced using different models, calibration/training data, and covariates at various spatial scales from global to local. Therefore, there is an urgent need to provide easy-to-understand tools to communicate map uncertainty and help end-users assess the reliability of DSM products for use at local scales. In this study, we used a large amount of hand-feel soil texture (HFST) data to assess the performance of various published DSM products on the prediction of soil particle size distribution in Central France. We tested four DSM products for soil texture prediction developed at various scales (global, continental, national, and regional) by comparing their predictions with approximately 3 200 HFST observations realized on a 1:50 000 soil survey conducted after release of these DSM products. We used both visual comparisons and quantitative indicators to match the DSM predictions and HFST observations. The comparison between the low-cost HFST observations and DSM predictions clearly showed the applicability of various DSM products, with the prediction accuracy increasing from global to regional predictions. This simple evaluation can determine which products can be used at the local scale and if more accurate DSM products are required.     

Mapping soil salinity and pH across an estuarine and alluvial plain using electromagnetic and digital elevation model data

Increasing pressures from agriculture and urbanization have resulted in drainage of many floodplains along the eastern Australian coastline, which are underlain by sulphidic sediments, to lower water tables and reduce soil salinity. This leads to oxidation of the sediments with a rapid decline in pH and an increase in salinity. Accurately mapping soil salinity and pH in coastal acid sulphate soil (CASS) landscapes is therefore important. One required map is the extent of highly acidic (i.e. pH < 4.5) areas, so that the application of alkaline amendments (e.g. lime) to neutralize the acid produced can be specifically targeted to the variation in pH. One approach is to use digital soil mapping (DSM) using ancillary information, such as an EM38, digital elevation models (DEM – elevation) and trend surface parameters (east and north). We used an EM38 in the horizontal (EM38h) and vertical (EM38v) modes together with elevation data to develop multiple linear regressions (MLR) for predicting EC_1:5 and pH. For pH, best results were achieved when the EM38 EC_a data were log‐transformed. By comparing MLR models using REML analysis, we found that using all ancillary data was optimal for mapping EC_1:5, whereas the best predictors for pH were north, log‐EM38v and elevation. Using residual maximum likelihood (REML), the final EC_1:5 and pH maps produced were consistent with previously defined soil landscape units, particularly CASS. The DSM approach used is amenable for mapping saline soils and identifying areas requiring the application of lime to manage acidic soil conditions in CASS landscape.     

Coping with imbalanced data problem in digital mapping of soil classes

An unsolved problem in the digital mapping of categorical soil variables and soil types is the imbalanced number of observations, which leads to reduced accuracy and the loss of the minority class (the class with a significantly lower number of observations compared to other classes) in the final map. So far, synthetic over- and under-sampling techniques have been explored in soil science; however, more efficient approaches that do not have the drawbacks of these techniques and guarantee retention of the minority classes in the produced map are essentially required. Such approaches suggested in the present study for digital mapping of soil classes include machine learning models of ensemble gradient boosting, cost-sensitive learning and one-class classification (OCC) of the minority class combined with multi-class classification. In this regard, extreme gradient boosting (XGB) as an ensemble gradient learner, a cost-sensitive decision tree (CSDT) within the C5.0 algorithm, and a one-class support vector machine combined with multi-class classification (OCCM) were investigated to map eight soil great groups with a naturally imbalanced frequency of observations in northwest Iran. A total of 453 profile data points were used for mapping the soil great groups of the study area. A data split was done manually for each class separately, which resulted in an overall 70% of the data for calibration and 30% for validation. The bootstrapping approach of calibration (with 10 runs) was performed to produce multiple maps for each model. The 10 bootstraps were evaluated against the hold-out validation dataset. The average values of accuracy measures, including Kappa (K), overall accuracy (OA), producer's accuracy (PA) and user's accuracy (UA), were explored. In addition, the results of this study were compared with a previous study in the same area, in which resampling techniques were used to deal with imbalanced data for digital soil class mapping. The findings show that all three suggested methods can deal well with the imbalanced classification problem, with OCCM showing the highest K (= 0.76) and OA (= 82) in the validation stage. Also, this model can guarantee the retention of the minority classes in the final map. Comparing the present approaches with the previous study approach demonstrates that the three newly suggested methods can remarkably increase both overall and individual class accuracy for mapping.     

基于环境变量的渭干河-库车河绿洲土壤盐分空间分布

土壤属性的数字制图对精准农业生产和环境保护治理至关重要。为了在大尺度上尽可能精确的监测土壤盐分空间变异性,该文使用普通克里格(ordinary kriging,OK)、地理加权回归(geographically weighted regression,GWR)和随机森林(random forest,RF)方法,结合地形、土壤理化性质和遥感影像数据等16个环境辅助变量,绘制渭干河-库车河绿洲表层土壤盐分分布图。基于决定系数(R^2)、均方根误差(RMSE)和平均绝对误差(MAE)验证模型精度。结果表明:不同方法预测的盐分分布趋势没有显著差异,大体上从研究区的西北向东南部方向增加;结合辅助变量的不同预测方法中,RF方法预测精度最高,R^2为0.74,RMSE和MAE分别为9.07和7.90 mS/cm,说明该模型可以有效地对区域尺度的土壤盐分进行定量估算;RF方法对电导率(electric conductivity,EC)低于2 mS/cm时预测精度最高,RMSE为3.96 mS/cm,很好的削弱了植被覆盖对电导率EC的影响。     

基于传统土壤图的土壤—环境关系获取及推理制图研究

在数字土壤制图研究中,从历史资料中提取准确的、详细的土壤—环境关系对于土壤图的更新和修正十分重要。从传统土壤图中提取土壤类型并从地形数据中提取环境参数,采用空间数据挖掘方法建立土壤—环境关系,并进行推理制图和精度验证。以湖北省黄冈市红安县华家河镇滠水河流域为例,首先选取成土母质和基于地形数据提取的高程、坡度、坡向等7个环境因子;然后利用频率分布原理得到包含土壤类型与环境因子信息的典型样本数据1 410个;采用See5.0决策树方法进行空间数据挖掘,建立土壤—环境关系;将其导入So LIM中进行推理制图;最后利用270个实地采样点验证所得土壤图的精度。土壤图的精度提高了约11%,证明了本研究方法对土壤类型和空间分布推理的可靠性。     

平原区土壤质地的反射光谱预测与地统计制图

基于地统计方法的土壤属性制图通常需要大量的采样与实验室测定。本研究提出利用可见光近红外(visible-nearinfrared spectroscopy,VNIR)光谱技术测定替代实验室测定,并与地统计方法相结合预测土壤质地的空间变异。通过建立砂粒(0.02 mm),粉粒(0.002~0.02 mm),黏粒(0.002 mm)含量的VNIR光谱预测模型,将模型预测得到的质地数据和建模点实测质地数据一同用于地统计分析和Kriging插值制图。以江苏北部黄淮平原地区为案例的研究结果表明,砂粒、粉粒、黏粒含量的预测值和实测值的均方根误差(RMSE)分别为8.67%、6.90%3、.51%,平均绝对误差(MAE)分别为6.46%、5.60%、3.05%,显示了较高的预测精度。研究为快速获取平原区土壤质地空间分布提供了新的可能的途径。     

Scope to map soil management units at the district level from remotely sensed γ‐ray spectrometry and proximal sensed EM induction data

The traditional method of soil mapping involves classifying soil into pre‐existing classes using morphological observations and then air‐photograph interpretation to extrapolate the information. To accelerate the process, less costly ancillary data can be used to assist mapping. However, digital soil mapping (DSM) is still affected by the classifications used to identify soil types. One reason is because the morphological characteristics are not mutually exclusive, which causes misclassification. In this study, we used a DSM approach, where ancillary data were surrogate for morphological data, with soil types identified by numerical clustering of remotely and proximally sensed data collected across a farming district near Gunnedah, Australia. Remotely sensed data were obtained from an air‐borne gamma‐ray (γ‐ray) spectrometer survey, including potassium (K), thorium (Th), uranium (U) and total counts. Proximally sensed data were measured using EM38 (i.e. EM38h and EM38v). Using fuzzy k‐means and a linear mixed model with measured physical (e.g. clay) and chemical (e.g. CEC) properties from the topsoil (0–0.30 m) and subsoil (0.9–1.2 m), we found that k = 5 was also optimal given that mean‐squared prediction error (i.e. ) was minimised. The approach highlighted subtle differences in physical and chemical properties in productive areas. The DSM was unsuccessful in identifying small units; however, inclusion of elevation data might overcome this limitation. This research has implications for providing fast, accurate and meaningful DSM at a district scale, where traditional methods are too expensive.     

基于多时相遥感影像和随机森林算法的土壤制图

获取准确的土壤-环境关系是数字土壤制图的关键,目前遥感影像已作为环境因子应用于土壤-环境知识的建立过程,但单幅遥感影像所包含的光谱信息差异难以将不同土壤类型区分开来。因此本文提出了一种基于多时相遥感影像的土壤制图方法：选取红安县滠水河流域为研究区,以母质类型图、等高线数据和多时相哨兵二号遥感影像为基础,提取与土壤形成有关的环境因子,通过随机森林算法获取土壤-环境关系,预测研究区各土壤类型的空间分布并成图,利用野外实地分层采样点验证推理图的精度。结果表明：推理土壤图总体分类精度高达86%,与原始土壤图对比,各土壤类型的空间分布具有一定相似性,展现了更为详细的空间细节信息,该研究成果可为更新土壤图工作提供新方法。     

Explaining and mapping total phosphorus content in French topsoils

Total phosphorus (TP) build‐up in agricultural soils represents both a threat to aquatic ecosystems and a valuable resource for future crop production, given the context of increasing food demand combined with the rapid depletion of the world's phosphate reserves. Therefore, it is crucially important (i) to identify the main factors controlling topsoil TP and (ii) to develop methods for mapping its spatial distribution. Multiple linear regression models were used with two distinct approaches to calculate TP and covariates linked to the P cycle. Firstly, covariates were selected from the Réseau de Mesures de la Qualité des Sols database, the French soil monitoring network, which consists of soil samples collected from 2158 sites on a 16‐km regular grid. Secondly, covariates were selected to map TP from spatially exhaustive datasets in France. The first approach explains 80% of variability in topsoil TP. The variables selected are linked to the autochthonous origin of P (parent material), to allochthonous origin (organic carbon and nitrogen contents) and to the retention capacity of soil (Al, Fe, Ca and clay contents). The predicted map obtained from the second approach provides a mean TP of 0.76 g/kg. This study demonstrates that creating national scale maps of TP, based on detailed soil sampling and many variables, is feasible and can be used to model the P cycle and P transfer processes. Such maps can be used in P erosion and transfer models over river basins, and therefore to predict P exports to surface waters.     

Large‐scale assessment of soil erosion using a neuro‐fuzzy model combined with GIS: A case study of Hubei Province,China

Reliable and cost‐effective soil erosion assessment is an important precondition for soil conservation measures, which remains a major challenge at large scale. Considering that the neuro‐fuzzy model has the special advantage in multi‐index comprehensive assessment and GIS technology is adept at geo‐spatial information processing, through the combination of them, it is possible to provide an effective approach for this difficult problem. Taking Hubei Province as a case study area, five evaluating indicators were selected for the large‐scale assessment, in which the GIS technology was used to construct the classification maps of evaluating indicators and to divide basic assessment units, and the neuro‐fuzzy model was adopted to extract fuzzy rules for individual units assessment from available ground truth data. According to the optimized assessment criteria generated by the neuro‐fuzzy model, the soil erosion state of the entire study area was then assessed. To represent the spatial distribution of soil erosion, a detailed map was produced by statistical mapping, which was represented with six erosion levels (from slight to severe) at a map scale of 1:250 000. The resulting map showed that about 30.1% of the total land area in Hubei was affected by different levels of soil erosion problem. Western high mountains and eastern low mountains suffered from the most serious erosion damage, a strong level of soil erosion was widely observed in these mountains. Large areas of moderate level erosion occurred in the northern hills. In contrast, most of the central plains were characterized as slight level erosion effect. The validation indicated that an overall accuracy of 88% and a κ of 0.89 were achieved, proving that the resulting map was in conformity with actual conditions, which indicates this assessment approach was reasonable and applicable. Copyright © 2009 John Wiley & Sons, Ltd.     

Spatio‐temporal change of soil organic matter content of Jiangsu Province,China, based on digital soil maps

Estimation of spatio‐temporal change of soil is needed for various purposes. Commonly used methods for the estimation have some shortcomings. To estimate spatio‐temporal change of soil organic matter (SOM) in Jiangsu province, China, this study explored benefits of digital soil maps (DSM) by handling mapping uncertainty using stochastic simulation. First, SOM maps on different dates, the 1980s and 2006–2007, were constructed using robust geostatistical methods. Then, sequential Gaussian simulation (SGS) was used to generate 500 realizations of SOM in the area for the two dates. Finally, E‐type (i.e. conditional mean) temporal change of SOM and its associated uncertainty, probability and confidence interval were computed. Results showed that SOM increased in 70% of Jiangsu province and decreased in the remaining 30% during the past decades. As a whole, SOM increased by 0.22% on average. Spatial variance of SOM diminished, but the major spatial pattern was retained. The maps of probability and confidence intervals for SOM change gave more detailed information and credibility about this change. Comparatively, variance of spatio‐temporal change of SOM derived using SGS was much smaller than sum of separate kriging variances for the two dates, because of lower mapping variances derived using SGS. This suggests an advantage of the method based on digital soil maps with uncertainty dealt with using SGS for deriving spatio‐temporal change in soil.     

The influence of input data quality in determining areas suitable for crop growth at the global scale – a comparative analysis of two soil and climate datasets

The assessment of biophysical crop suitability requires datasets on soil and climate. In this study, we investigated the differences in topsoil properties for the dominant soil mapping units between two global soil datasets. We compared the ISRIC World Soil Information Center’s World Inventory of Soil Emissions Potential 5 by 5 arc min Soil Map of the World (ISRIC‐WISE 5by5 SMW ) with the Harmonized World Soil Database (HWSD) in 0.5 arc min. We also incorporated annual mean temperature and mean precipitation from two global climate datasets that were the WorldClim measurement‐based climate dataset and the Kiel Climate Model (KCM) modelled results of global climate from 1960 to 1990. We then applied a fuzzy logic approach using different combinations and resolutions of the datasets to determine the effects on the extent and distribution of suitable areas for 15 crops. We only used the spatially dominant soil class in the mapping units in the soil databases (resampled to the same resolution of 5 arc min), and we found that the estimates of topsoil properties (0–20 cm in ISRIC‐WISE and 0–30 cm in HWSD) of the seven analysed parameters were up to 40% lower in most of the HWSD than in the ISRIC‐WISE 5by5 SMW. Results from the KCM are 0.1 °C (1%) lower in mean global annual temperature and 20% higher in average global annual precipitation compared with the WorldClim data. The HWSD‐based runs resulted in 10% less crop‐suitable land than the ISRIC‐WISE 5by5 SMW‐based results. The KCM simulations predicted 1% less crop‐suitable land than the WorldClim model. Despite generalizations, our results demonstrate that discrepancies in crop suitable areas are largely due to the differences in the soil databases rather than to climate.     

Assessing geopedological soil mapping approach by statistical and geostatistical methods: A case study in the Borujen region,Central Iran

Geopedology, using geomorphologic and supervised classification bases, has been proposed as a soil surveying method. It undertakes a systematic approach to map soilscapes. This approach tries to reduce the budget of accomplishing soil surveys as well as increasing the purity of mapping units. But to what extent it has been succeeded is the question. The main objective of this research was to validate the geopedological mapping methodology by statistical and geostatistical methods in the Borujen region, Central Iran. After a primary interpretation of the study area on air photos (1:20 000 scale), 94 pedons were excavated on a 125 m square sampling grid in the sample area for geostatistical studies. To achieve the statistical aims, a geomorphic unit which encompassed the maximum surface of the sample area (and also the study area) was selected and the biggest delineation of this unit with 19 pedons was considered. The credibility of generalizing the results of the geopedological approach for the studied unit was tested by comparison with 15 pedons in a similar unit outside the sample area, named the validation area. Properties of the soils, including percentage of clay, rock fragments (2–75 mm) and total carbonates in the soil family control section, percentage of organic matter in the A horizon and its thickness, were selected for statistical and geostatistical analyses. Univariate and multivariate statistical analyses obtained from the units in the sample and the validation areas, showed that the means of soil variables were similar. A high difference between A horizon thickness variances in the sample area and the validation area affected the significance of the variation test in the univariate and multivariate results. Our study demonstrated that, when comparing the same factors from sample and validation areas, the spatial distribution and spatial dependency level of soil attributes were different. Therefore, although the geopedological approach tries to distinguish more homogeneous soil mapping units, it is still not able to fully define and represent the real variability of soil properties and show the chaotic behavior or nature of the soils. We recommend further investigations on different techniques of stratifying the landscape in order to better analyze and understand the soil-forming processes and the nature of soil variability and to improve sampling and mapping approaches. We suggest that the additional determination of the phases of landforms and phases of soil series will improve mapping in the future.     

秸秆带状覆盖下培土对马铃薯产量与水分利用效率的影响

气候变化效应评估、土壤固碳潜力和肥力管理等,迫切需要详尽的土壤有机质（soil organic matter, SOM）空间分布信息。该文以江苏省第二次土壤普查的1 519个典型土壤剖面的表层（0～20 cm）SOM含量为例,选择1 217个样本为建模集,302个为验证集,选取年均温度、年均降雨、物理性黏粒和土壤pH值等因子进行SOM的地理加权回归（geographically weighted regression, GWR）建模。从建模集中分别随机抽取100%（1 217个）、80%（973个）、60%（730个）、40%（486个）,20%（243个）的样点,对比不同样点数量下GWR和传统全局回归模型的精度差异,并选择最优模型进行SOM空间预测制图。结果表明：1）江苏省SOM含量在不同空间尺度上存在极显著的空间自相关性。不同样点数量的建模集的全局自相关性和局部空间自相关聚类图结果相似。全局Moran''s I值介于0.25～0.61（P<0.001）。SOM含量空间分布以空间聚集特征为主,"高-高"聚集区主要分布在苏中和苏南地区,"低-低"聚集区主要分布在苏北地区。2）GWR建模结果均优于传统的传统全局回归建模,其残差在不同的空间尺度上均不存在空间自相关性。不同建模集的GWR的R2adj较全局建模均提高0.15～0.20,其AIC和RSS均比全局模型有大幅降低,为56.08～360.19和17.40～76.67。不同建模样本数量的GWR模型对SOM的解释能力差异较小。3）建模样点数量（除建模样本n=243）对GWR预测制图结果的精度影响不大,RMSE介于5.56～5.75 g/kg之间,MAE介于3.87～4.05 g/kg之间,R2介于0.52～0.48之间,均优于全部建模样点的普通克里格插值验证结果。该研究可为样点数较少的省级尺度地区SOM空间建模与制图提供借鉴。     

Soil–landscape variability: mapping and building detail information for soil management

The study of soil–landscape relationships at a detailed scale (1:10 000) and its use for soil management was less common in developing countries. The study was conducted in western Ethiopia with the aim to explain the soil variability across landscapes, classify soils into mapping units and produce a map of these soils. This study was performed based on a discrete model of spatial variation. Five soil reference groups: Vertisols, Cambisols, Fluvisols, Luvisols and Leptosols were identified in the study site. Distribution of the soil reference groups was determined by landscape position. Variation in soil texture, colour, pH , exchangeable acidity, organic carbon, total nitrogen, available phosphorus (av. P), carbon to nitrogen ratio (C/N), exchangeable potassium (K), calcium (Ca), magnesium (Mg), sodium (Na) and cation exchange capacity (CEC ) was observed within and among soil mapping units (SMU s). Variability was considerably high for exchangeable Ca and CEC . Factor analysis result indicated that variation in soil properties within land unit was comparatively highest in Leptosols of SMU 9 (88.87%) and lowest in Vertisols of SMU 1 (60.82%). Moderate‐to‐fine scale mapping of soil properties helps to build detail information for soil management. Grouping fields into mapping units that require more or less similar management measure would be an important soil–landscape concept. As a result, mapping units could be used as cost‐effective means of treating variable field so as to optimize the forecasted benefits.     

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.     

Filling the European blank spot—Swiss soil erodibility assessment with topsoil samples

Soil erodibility, commonly expressed as the K‐factor in USLE‐type erosion models, is a crucial parameter for determining soil loss rates. However, a national soil erodibility map based on measured soil properties did so far not exist for Switzerland. As an EU non‐member state, Switzerland was not included in previous soil mapping programs such as the Land Use/Cover Area frame Survey (LUCAS). However, in 2015 Switzerland joined the LUCAS soil sampling program and extended the topsoil sampling to mountainous regions higher 1500 m asl for the first time in Europe. Based on this soil property dataset we developed a K‐factor map for Switzerland to close the gap in soil erodibility mapping in Central Europe. The K‐factor calculation is based on a nomograph that relates soil erodibility to data of soil texture, organic matter content, soil structure, and permeability. We used 160 Swiss LUCAS topsoil samples below 1500 m asl and added in an additional campaign 39 samples above 1500 m asl. In order to allow for a smooth interpolation in context of the neighboring regions, additional 1638 LUCAS samples of adjacent countries were considered. Point calculations of K‐factors were spatially interpolated by Cubist Regression and Multilevel B‐Splines. Environmental features (vegetation index, reflectance data, terrain, and location features) that explain the spatial distribution of soil erodibility were included as covariates. The Cubist Regression approach performed well with an RMSE of 0.0048 t ha h ha^?1 MJ^?1 mm^?1. Mean soil erodibility for Switzerland was calculated as 0.0327 t ha h ha^?1 MJ^?1 mm^?1 with a standard deviation of 0.0044 t ha h ha^?1 MJ^?1 mm^?1. The incorporation of stone cover reduces soil erodibility by 8.2%. The proposed Swiss erodibility map based on measured soil data including mountain soils was compared to an extrapolated map without measured soil data, the latter overestimating erodibility in mountain regions (by 6.3%) and underestimating in valleys (by 2.5%). The K‐factor map is of high relevance not only for the soil erosion risk of Switzerland with a particular emphasis on the mountainous regions but also has an intrinsic value of its own for specific land use decisions, soil and land suitability and soil protection.