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The evaluation of the impact of additional soil pollutants has to be contrasted against the naturally occurring pollutant concentration, i.e., the background concentration. Because background concentrations have to represent areal entities, point information has to be extrapolated into the area using interpolation methods. Thus, the accuracy of the interpolation method is crucial for the correct designation of background values to the areas. For the area of Bavaria (SE Germany), the actual background values of organic and inorganic soil pollutants were derived from >337,000 data from 5000 horizons based upon 1134 soil profiles. Background values were determined for the different soil depth compartments (O layer, topsoil, subsoil, and parent material) and land uses (agriculture, forestry). For interpolation between the nodes, Indicator Kriging was applied. The kriged total area was subdivided into 6 subareas of different background concentrations using percentile thresholds. To derive representative background concentrations, the reliable segregation of the total area into subareas and, thus, a robust interpolation method is a prerequisite. In this study, the robustness of the applied Indicator Kriging should be tested. Influences of data transformations and different kriging methods upon the demarcation of subareas should be investigated for the organic sum‐parameter EPA‐PAH. Neither a data transformation nor the comparison with Ordinary Kriging yielded significant deviations in the assigned subareas. Furthermore, cross‐validation as well as addition of synthetic noise was used to check the susceptibility of the method to artifacts and changes in the data set. After random splitting of the original data set into 4 subsets and re‐arrangement to 6 half‐sets, subsequent Indicator Kriging produced 6 results with mainly identical subarea configurations. Cross‐validation, i.e., comparison of points from the kriging surface (validation data set) with the calibration data set, yielded considerable residuals between estimates and measurements. Based on these normally distributed residuals, random numbers with identical statistical moments were generated and used as measurement errors for another kriging run. This synthetic noise was added to the corresponding result based on the calibration half‐set. The resulting subareas changed only slightly for the most polluted region, but considerably for the other regions. The chosen interpolation method provides sufficient stability to demarcate the relevant areas with elevated pollution in Bavaria. For other areas, its stability is less clear. Here, additional soil samples are required. 相似文献
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Goovaerts P 《European Journal of Soil Science》2011,62(3):371-380
Information available for mapping continuous soil attributes often includes point field data and choropleth maps (e.g. soil or geological maps) that model the spatial distribution of soil attributes as the juxtaposition of polygons (areas) with constant values. This paper presents two approaches to incorporate both point and areal data in the spatial interpolation of continuous soil attributes. In the first instance, area-to-point kriging is used to map the variability within soil units while ensuring the coherence of the prediction so that the average of disaggregated estimates is equal to the original areal datum. The resulting estimates are then used as local means in residual kriging. The second approach proceeds in one step and capitalizes on: 1) a general formulation of kriging that allows the combination of both point and areal data through the use of area-to-area, area-to-point, and point-to-point covariances in the kriging system, 2) the availability of GIS to discretize polygons of irregular shape and size, and 3) knowledge of the point-support variogram model that can be inferred directly from point measurements, thereby eliminating the need for deconvolution procedures. The two approaches are illustrated using the geological map and heavy metal concentrations recorded in the topsoil of the Swiss Jura. Sensitivity analysis indicates that the new procedures improve prediction over ordinary kriging and traditional residual kriging based on the assumption that the local mean is constant within each mapping unit. 相似文献
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Geospatial measurements of ancillary sensor data, such as bulk soil electrical conductivity or remotely sensed imagery data, are commonly used to characterize spatial variation in soil or crop properties. Geostatistical techniques like kriging with external drift or regression kriging are often used to calibrate geospatial sensor data to specific soil or crop properties. More traditional statistical methods such as ordinary linear regression models are also commonly used. Unfortunately, some soil scientists see these as competing and unrelated modeling approaches and are unaware of their relationship. In this article we review the connection between the ordinary linear regression model and the more comprehensive geostatistical mixed linear model and describe when and under what conditions ordinary linear regression models represent valid spatial prediction models. The formulas for the ordinary linear regression model parameter estimates and best linear unbiased predictions are derived from the geostatistical mixed linear model under two different residual error assumptions; i.e., strictly uncorrelated (SU) residuals and effectively uncorrelated (EU) residuals. The theoretically optimal (best linear unbiased) and computable (linear unbiased) predictions and variance estimates derived under the EU error assumption are examined in detail. Statistical tests for detecting spatial correlation in LR model residuals are also reviewed, in addition to three LR model validation tests derived from classical linear modeling theory. Two case studies are presented that highlight and demonstrate the various parameter estimation, response variable prediction and model validation techniques discussed in this article. 相似文献
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海涂围垦区土壤盐分空间变异模拟的比较研究 总被引：2，自引：0，他引：2
以苏北海涂围垦区为例,利用人工神经网络(ANN)、普通克里格(OK)插值和序贯高斯模拟(SGS)对典型地块土壤盐分空间分布进行了模拟、插值与预测,获取了各方法的优化结构与参数,并就各方法对土壤盐分分布特征与空间结构的预测能力进行了比较分析。结果表明:ANN、OK和SGS法均较好地模拟和预测了土壤盐分的空间分布,达到了较高模拟、插值与预测精度;ANN获得的土壤盐分空间分布最为连续,SGS法整体分布相对离散;ANN能较好地预测盐分较低的样点,但ANN对高盐分样点的预测结果不如SGS和OK;SGS预测结果最符合实测值的波动特点,ANN预测结果波动范围最窄,SGS较ANN和OK更能反应数据随机变量的结构性和波动性,在整体上要优于ANN和OK法。该结果为滨海地区盐渍土壤的精准评估与高效改良提供了参考依据。 相似文献
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应用集成BP神经网络进行田间土壤空间变异研究 总被引：15，自引：4，他引：15
以英国北爱尔兰Hayes的一块牧草地为研究区，将所有样点分为独立的训练和检验数据集，并在训练样点集的基础上设计了其他4种样点布局方案，以研究神经网络集成技术应用于田间土壤性质空间变异性的可能性。与广泛应用的克里格法的试验结果相比，集成BP神经网络的插值结果精度与之基本相当，尤其是在样点分布较稀疏和样点数较少的情况下，集成BP网络表现出明显的优势；由于神经网络集成方法对样本数据的分布没有任何要求，因此具有较广泛的应用前景和潜力，并在不符合克里格法对样本数据分布要求的情况下是一种可行的替代方法。 相似文献
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基于GARBF神经网络的土壤有效锌空间插值方法研究 总被引：7，自引：0，他引：7
以土壤有效锌为研究对象,构建遗传径向基函数(GARBF)神经网络对该元素属性值进行空间插值,以训练样本集的测定值与预测值之间的决定系数、逼近误差及检验样本的插值误差为评判标准,比较GARBF神经网络、径向基函数(RBF)神经网络、普通克里格(Ordinary Kriging)的拟合能力和空间插值能力。结果表明:同一区域两种抽样方案(a、b)下三种插值方法对训练样本的拟合能力为GARBFRBFOr-dinary Kriging。以平均绝对误差和误差均方根作为插值精度的评价指标,GARBF与RBF神经网络相比,训练样本的逼近误差分别降低0.22～0.25(a方案)和0.10～0.11(b方案),检验样本的插值误差分别降低0.13～0.11(a方案)和0.02～0.13(b方案);GARBF神经网络与Ordinary Kriging相比,训练样本的逼近误差分别降低1.12～1.40(a方案)和1.45～1.88(b方案),检验样本的插值误差分别降低0.20～0.24(a方案)和0.14～0.32(b方案),GARBF神经网络的误差最小,插值精度最高。从GARBF神经网络的插值图可以看出,遗传算法避免了神经网络容易陷入局部最优点,扩大了对土壤中相关空间信息的搜索范围,在一定程度上避免了类似克里格插值的"平滑效应"。 相似文献
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Collecting soil data is time-consuming and costly, often exceeding practical possibilities. A methodology for the delineation of soil mapping units in an alluvial plain of Western Peloponnese, Greece, was investigated. A detailed soil survey of an area of 300 ha was used to obtain the basic soil data for evaluating the performance of the proposed methodology. The methodology consists of the following steps: (a) data collection from borings and representative soil profiles, (b) definition of the soil mapping units in the study area, (c) determination of the range of the diagnostic variables for each mapping unit from field observations and statistical analysis of the analytical data from representative soil profiles, (d) determination of the class of each diagnostic variable by observation at a network of boring points, (e) subjective assignment of numerical values to soil variables at the bore points, (f) estimation of the values of each soil variable at the points of a regular grid using the interpolation methods kriging and inverse squared distance, (g) application of the fuzzy set theory to the interpolated data and the production of thematic fuzzy maps, and (h) validation of the results through a number of independent test borings. The results obtained show that the proposed methodology can produce soil maps of recent alluvial plains with acceptable accuracy and cost. 相似文献
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平原区土壤质地的反射光谱预测与地统计制图 总被引：6，自引：3，他引：3
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。 相似文献
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Abstract The co-regionalization between relative elevation and zinc concentration was used to map zinc concentration in the soil of the Geul floodplain in the southern Netherlands by co-kriging from 154 observations. Point co-kriging and point kriging for estimating zinc content in the soil were compared in terms of kriging variances. Another 45 samples were used to compare the precision of the estimated values in terms of squared and absolute estimation errors. Point co-kriging produced better estimates of zinc concentration than either simple point kriging or linear regression from the relative elevation data alone. Moreover, the estimation variances for co-kriging are substantially smaller than those for kriging. The results suggest that knowledge of geomorphological processes can often improve the quality of interpolation maps of properties that are expensive to measure. 相似文献
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《Communications in Soil Science and Plant Analysis》2012,43(8):925-938
Soil organic matter is a very important component of soil that supports the sustainability and quality in all ecosystems, especially in arid and semi-arid regions. A comparison study was carried out to verify when the artificial neural network (ANN) and multiple linear regression (MLR) models are appropriate for the prediction of soil organic matter (SOM) and particulate organic matter (POM). Discussions of advantages and disadvantages are given for both methods. Three different sets of easily available properties (soil properties alone, topographic and vegetation index, a combination of soil and topographic data) were used as inputs and the one affecting the model the most was determined. The smallest prediction errors were obtained by the ANN method; however, the prediction accuracies of the constructed MLR models using different data sets were closed to the ANN models in many cases. 相似文献
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Stratification of a region based on soil map delineations followed by within-stratum interpolation is sometimes used to combine soil maps and spatial interpolation. However, not all delineations are equally suitable to subdivide an area into precisely located mutually exclusive strata. This paper proposes a flow-path to characterize the nature of soil map delineations and a methodology to integrate the properties of map delineations into ordinary kriging.Four types of delineations were distinguished based on three criteria: the nature of transition (discontinuous or gradual), the mapping accuracy, and the structure of the within-unit spatial variation. For each type of delineation the ordinary kriging algorithm was modified to integrate its properties in the interpolation.As a test case, the sand content of the topsoil in the province of West-Flanders (Belgium) was mapped, using independent test data for validation. Inside the mapping units and at delineations identified as gradual transitions, our procedure, termed ordinary kriging integrating properties of map delineations (OKPD) , performed similarly to stratified ordinary kriging (SOK). However, close to the delineations identified as inaccurately mapped discontinuities the mean square prediction error of OKPD was 0.64 times that of SOK. Moreover, near these delineations, the prediction variance was largely underestimated by SOK (relative variance = 5.1), whereas OKPD produced a more realistic value (relative variance = 1.5). 相似文献
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Qi-Quan Li Xin Zhang Bing Li Xue-Song Gao Da-Gang Yuan 《Archives of Agronomy and Soil Science》2016,62(11):1541-1553
It is widely recognized that using correlated environmental factors as auxiliary variables can improve the prediction accuracy of soil properties. In this study, a radial basis function neural network (RBFNN) model combined with ordinary kriging (OK) was proposed to predict spatial distribution of four soil nutrients based on the same framework used by regression kriging (RK). In RBFNN_OK, RBFNN model was used to explain the spatial variability caused by the selected auxiliary factors, while OK was used to express the spatial autocorrelation in RBFNN prediction residuals. The results showed that both RBFNN_OK and RK presented prediction maps with more details. However, RK does not always obtain mean errors (MEs) which were closer to 0 and lower root mean square errors (RMSEs) and mean relative errors (MREs) than OK. Conversely, MREs of RBFNN_OK were much closer to 0 and its RMSEs and MREs were relatively lower than OK and RK. The results suggest that RBFNN_OK is a more unbiased method with more stable prediction performance as well as improvement of prediction accuracy, which also indicates that artificial neural network model is more appropriate than regression model to capture relationships between soil variables and environmental factors. Therefore, RBFNN_OK may provide a useful framework for predicting soil properties. 相似文献
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OPTIMAL INTERPOLATION AND ISARITHMIC MAPPING OF SOIL PROPERTIES III CHANGING DRIFT AND UNIVERSAL KRIGING 总被引：2，自引：0，他引：2
Universal kriging is a form of interpolation that takes account of local trends in data when minimizing the error associated with estimation. The presence of such trends, or drifts as they are known, is identified qualitatively, and their form found quantitatively by structural analysis, which simultaneously estimates semi-variances of the differences between the drift and actual data. The resulting semi-variograms are then used for the interpolation. The method was applied to measurements of electrical resistivity made in the soil at 1 m intervals at Bekesbourne, Kent. Analysis showed that the data could be adequately represented as a series of linear drifts over distances of 4 m to 8 m and with negligible nugget variance. Semi-variances of residuals from the drift were computed, and used to krige missing values and so complete an isarithmic map of the site. The method is by no means universally applicable in soil survey, mainly because of the large nugget variances usually encountered. These effectively prevent any distinction between constant and changing drift. They arise in part because measurements are made on small widely separated volumes of soil. Universal kriging is likely to be profitable only where measurements are made on contiguous volumes of soil or after substantial bulking. 相似文献
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R. M. Lark 《European Journal of Soil Science》2000,51(1):137-157
The standard estimator of the variogram is sensitive to outlying data, a few of which can cause overestimation of the variogram. This will result in incorrect variances when estimating the value of a soil property by kriging or when designing a sampling grid to map the property to a required precision. Several robust estimators of the variogram, based on location and scale estimation, have been proposed as improvements. They seem to be suitable for analysis of soil data in circumstances where the standard estimator is likely to be affected by outliers. Robust estimators are based on assumptions about the distribution of the data which will not always hold and which need not be made in kriging or in estimating the variogram by the standard estimator. The estimators are reviewed. Simulation studies show that the robust estimators vary in their susceptibility to moderate skew in the underlying distribution, but that the effects of outliers are generally greater. The estimators are applied to some soil data, and the resulting variograms used for ordinary kriging at sites in a separate validation data set. In most cases the variograms derived from the standard estimator gave kriging variances which appeared to overestimate the mean squared error of prediction (MSEP). Kriging with variograms based on robust estimators sometimes gave kriging variances which underestimated the MSEP or did not differ significantly from it. Estimates of kriging variance and the MSEP derived from the validation data were generally close to estimates from cross‐validation on the prediction set used to derive the variograms. This indicates that variogram models derived from different estimators could be compared by cross‐validation. 相似文献
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基于GIS和地理加权回归的砂田土壤阳离子交换量空间预测 总被引：2，自引：1，他引：2
土壤阳离子交换量(CEC)反映土壤保水保肥能力,研究CEC空间分布可为土壤改良和田间施肥提供理论依据。本文以宁夏香山地区砂田淡灰钙土为研究对象,在土壤CEC和理化性质相关分析基础上以普通克里格(OK)为对照,探索回归克里格(RK)和地理加权回归克里格(GWRK)在CEC空间插值上的应用,并对三者的插值精度及制图效果进行评价。描述统计表明研究区土壤CEC含量均值为10.145cmol/kg,CEC与有机质含量呈显著正相关,与砂粒含量呈显著负相关;地统计分析表明CEC实测值、OLS残差和GWR残差块金系数分别为8.50%、6.36%和7.02%,比值均小于25%,具有强烈空间自相关;对验证点进行插值精度分析,RK和GWRK的相对模型改进值(RI)分别为40.49%、41.50%,插值精度GWRKRKOK;从成图效果看,GWRK中辅助变量参与了局部回归,成图效果更加精细,揭示了更多空间变化细节。本研究结论可为土壤CEC空间预测研究提供可靠的方法借鉴。 相似文献
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基于RBF神经网络的土壤有机质空间变异研究方法 总被引：11，自引：4，他引：7
通过研究土壤性质的空间变异和空间插值方法,快速准确获取土壤性质的空间分布是精确农业和环境保护的基础。该文以四川眉山一块约40 km2的区域为试验区,采集表层土壤（0～20 cm）样点80个,利用径向基函数(RBF)神经网络建立空间坐标和邻近样点与土壤有机质间的非线性映射关系（RBF2）,模拟土壤有机质的空间分布。与普通克里法（OK）和仅以坐标为网络输入的神经网络方法（RBF1）相比,RBF2的插值精度有显著的提高;相同样点密度下其相对预测误差分别较OK和RBF1减小了9.87%、1.97%（样本A）和13.09%、2.36%（样本B）;即使样点数减半的情况下RBF2的相对预测误差也分别较OK和RBF1减小了10.23%和2.33%,并且插值图差异相对较小,可以更好地反映土壤有机质空间分布的异质性。因此,利用以坐标和邻近样点为输入的神经网络方法可以相对准确、快速地获取区域土壤性质空间分布的异质性信息。 相似文献