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1.
Q. ZHU  H. S. LIN 《土壤圈》2010,20(5):594-606
The accuracy between ordinary kriging and regression kriging was compared based on the combined consideration of sample size, spatial structure, and auxiliary variables (terrain indices and electromagnetic induction surveys) for a variety of soil properties in two contrasting landscapes (agricultural vs. forested). When spatial structure could not be well captured by point-based observations (e. g., when the ratio of sample spacing over correlation range was > 0.5), or when a strong relationship existed between target soil properties and auxiliary variables (e. g., their R2 was > 0.6), regression kriging (RK) was more accurate for interpolating soil properties in both landscapes studied. Otherwise, ordinary kriging (OK) was better. Soil depth and wetness condition did not appear to affect the selection of kriging for soil moisture interpolation, because they did not significantly change the ratio of sample spacing over correlation range and the relationship with the auxiliary variables. Because of a smaller ratio of elevation change over total study area (E/A = 1.2) and multiple parent materials in the agricultural land, OK was generally more accurate in that landscape. In contrast, a larger E/A ratio of 6.8 and a single parent material led to RK being preferable in the steep-sloped forested catchment. The results from this study can be useful for selecting kriging for various soil properties and landscapes.  相似文献   

2.
Soil organic matter (SOM) content is one of the main factors to be considered in the evaluation of soil health and fertility. As timing, human and monetary resources often limit the amount of available data, geostatistical techniques provide a valid scientific approach to cope with spatial variability, to interpolate existing data and to predict values at unsampled locations for accurate SOM status survey. Using geostatistical and geographic information system (GIS) approaches, the spatial variability of some physical and chemical soil parameters was investigated under Mediterranean climatic condition in the Abruzzo region of central Italy, where soil erosion processes accelerated by human induced factors are the main causes of soil degradation associated with low SOM content. Experimental semivariograms were established to determine the spatial dependence of the soil variables under investigation. The results of 250 soil sampling point data were interpolated by means of ordinary kriging coupled with a GIS to produce contour maps distribution of soil texture, SOM content related to texture, and C/N ratio. The resulting spatial interpolation of the dataset highlighted a low content of SOM in relation with soil texture in most of the surveyed area (87%) and an optimal C/N ratio for only half of the investigated surface area. Spatial location of degraded area and the assessment of its magnitude can provide decision makers with an accurate support to design appropriate soil conservation strategies and then facilitate a regional planning of agri-environmental measures in the framework of the European Common Agricultural Policy.  相似文献   

3.
Integrating land use type and other geographic information within spatial interpolation has been proposed as a solution to improve the performance and accuracy of soil nutrient mapping at the regional scale.This study developed a non-algorithm approach,i.e.,applying inverse distance weighting (IDW) and ordinary kriging (OK),to individual land use types rather than to the whole watershed,to determine if this improved the performance in mapping soil total C (TC),total N (TN),and total P (TP) in a 200-km2 urbanizing watershed in Southeast China.Four land use types were identified by visual interpretation as forest land,agricultural land,green land,and urban land.One hundred and fifty soil samples (0 10 cm) were taken according to land use type and patch size.Results showed that the non-algorithm approach,interpolation based on individual land use types,substantially improved the performance of IDW and OK for mapping TC,TN,and TP in the watershed.Root mean square errors were reduced by 3.9％ for TC,10.7％ for TN,and 25.9％ for TP by the application of IDW,while the improvements by OK were slightly lower as 0.9％ for TC,7.7％ for TN,and 18.1％ for TP.Interpolations based on individual land use types visually improved depiction of spatial patterns for TC,TN,and TP in the watershed relative to interpolations by the whole watershed.Substantial improvements might be expected with denser sampling points.We suggest that this non-algorithm approach might provide an alternative to algorithm-based approaches to depict watershed-scale nutrient patterns.  相似文献   

4.
High quality, agricultural nutrient distribution maps are necessary for precision management, but depend on initial soil sample analyses and interpolation techniques. To examine the methodologies for and explore the capability of interpolating soil properties based on neural network ensemble residual kriging, a silage field at Hayes, Northern Ireland, UK, was selected for this study with all samples being split into independent training and validation data sets. The training data set, comprised of five soil properties: soil pH, soil available P, soil available K, soil available Mg and soil available S,was modeled for spatial variability using 1) neural network ensemble residual kriging, 2) neural network ensemble and 3) kriging with their accuracies being estimated by means of the validation data sets. Ordinary kriging of the residuals provided accurate local estimates, while final estimates were produced as a sum of the artificial neural network (ANN) ensemble estimates and the ordinary kriging estimates of the residuals. Compared to kriging and neural network ensemble,the neural network ensemble residual kriging achieved better or similar accuracy for predicting and estimating contour maps. Thus, the results demonstrated that ANN ensemble residual kriging was an efficient alternative to the conventional geo-statistical models that were usually used for interpolation of a data set in the soil science area.  相似文献   

5.
Several methods,including stepwise regression,ordinary kriging,cokriging,kriging with external drift,kriging with varying local means,regression-kriging,ordinary artificial neural networks,and kriging combined with artificial neural networks,were compared to predict spatial variation of saturated hydraulic conductivity from environmental covariates.All methods except ordinary kriging allow for inclusion of secondary variables.The secondary spatial information used was terrain attributes including elevation,slope gradient,slope aspect,profile curvature and contour curvature.A multiple jackknifing procedure was used as a validation method.Root mean square error (RMSE) and mean absolute error (MAE) were used as the validation indices,with the mean RMSE and mean MAE used to judge the prediction quality.Prediction performance by ordinary kriging was poor,indicating that prediction of saturated hydraulic conductivity can be improved by incorporating ancillary data such as terrain variables.Kriging combined with artificial neural networks performed best.These prediction models made better use of ancillary information in predicting saturated hydraulic conductivity compared with the competing models.The combination of geostatistical predictors with neural computing techniques offers more capability for incorporating ancillary information in predictive soil mapping.There is great potential for further research and development of hybrid methods for digital soil mapping.  相似文献   

6.
基于不同地表曲面模型预测土壤有机碳含量   总被引：1，自引：0，他引：1  
Local terrain attributes,which are derived directly from the digital elevation model,have been widely applied in digital soil mapping.This study aimed to evaluate the mapping accuracy of soil organic carbon (SOC) concentration in 2 zones of the Heihe River in China,by combining prediction methods with local terrain attributes derived from different polynomial models.The prediction accuracy was used as a benchmark for those who may be more concerned with how accurately the variability of soil properties is modeled in practice,rather than how morphometric variables and their geomorphologic interpretations are understood and calculated.In this study,2 neighborhood types (square and circular) and 6 representative algorithms (Evans-Young,Horn,Zevenbergen-Thorne,Shary,Shi,and Florinsky algorithms) were applied.In general,35 combinations of first-and second-order derivatives were produced as candidate predictors for soil mapping using two mapping methods (i.e.,kriging with an external drift and geographically weighted regression).The results showed that appropriate local terrain attribute algorithms could better capture the spatial variation of SOC concentration in a region where soil properties are strongly influenced by the topography.Among the different combinations of first-and second-order derivatives used,there was a best combination with a more accurate estimate.For different prediction methods,the relative improvement in the two zones varied between 0.30％ and 9.68％.The SOC maps resulting from the higher-order algorithms (Zevenbergen-Thorne and Florinsky) yielded less interpolation errors.Therefore,it was concluded that the performance of predictive methods,which incorporated auxiliary variables,could be improved by attempting different terrain analysis algorithms.  相似文献   

7.
普通克里格法在土壤有机碳制图中的应用   总被引：1，自引：0，他引：1  
The quantification of the pattern and spatial distribution of soil organic carbon (SOC) is fundamental to understand many ecosystem processes.This study aimed to apply ordinary kriging (OK) to model the spatial distribution of SOC in a selected part of Zambia.A total of 100 soil samples were collected from the study area and analyzed for SOC by determining soil oxidizable carbon using the Walkley-Black method.An automated fitting procedure was followed when modeling the spatial structure of the SOC data with the exponential semivariogram.The results indicated that the short range spatial dependence of SOC was strong with a nugget close to zero.The spatial autocorrelation was high to medium with a nugget to sill ratio of 0.25.The root mean square error of the predictions was 0.64,which represented 58.18％ of the mean observed data for SOC.It can be concluded that the generated map could serve as a proxy for SOC in the region where evidence of spatial structure and quantitative estimates of uncertainty are reported.Therefore,the maps produced can be used as guides for various uses including optimization of soil sampling.  相似文献   

8.
Soil salinity and hydrologic datasets were assembled to analyze the spatio-temporal variability of salinization in Fengqiu County, Henan Province, China, in the alluvial plain of the lower reaches of the Yellow River. The saline soil and groundwater depth data of the county in 1981 were obtained to serve as a historical reference. Electrical conductivity (EC) of 293 surface soil samples taken from 2 km × 2 km grids in 2007 and 40 soil profiles acquired in 2008 was analyzed and used for comparative mapping. Ordinary kriging was applied to predict EC at unobserved locations to derive the horizontal and vertical distribution patterns and variation of soil salinity. Groundwater table data from 22 observation wells in 2008 were collected and used as input for regression kriging to predict the maximum groundwater depth of the county in 2008. Changes in the groundwater level of Fengqiu County in 27 years from 1981 to 2008 was calculated. Two quantitative criteria, the mean error or bias (ME) and the mean squared error (MSE), were computed to assess the estimation accuracy of the kriging predictions. The results demonstrated that the soil salinity in the upper soil layers decreased dramatically and the taxonomically defined saline soils were present only in a few micro-landscapes after 27 years. Presently, the soils with relatively elevated salt content were mainly distributed in depressions along the Yellow River bed. The reduction in surface soil salinity corresponded to the locations with deepened maximum groundwater depth. It could be concluded that groundwater table recession allowed water to move deeper into the soil profile, transporting salts with it, and thus played an important role in reducing soil salinity in this region. Accumulation of salts in the soil profiles at various depths below the surface indicated that secondary soil salinization would occur when the groundwater was not controlled at a safe depth.  相似文献   

9.
基于多元统计学和地统计学的土壤重金属源解析   总被引：2，自引：0，他引：2  
The main objectives of this study were to introduce an integrated method for effectively identifying soil heavy metal pollution sources and apportioning their contributions, and apply it to a case study. The method combines the principal component analysis/absolute principal component scores (PCA/APCS) receptor model and geostatistics. The case study was conducted in an area of 31 km2 in the urban-rural transition zone of Wuhan, a metropolis of central China. 124 topsoil samples were collected for measuring the concentrations of eight heavy metal elements (Mn, Cu, Zn, Pb, Cd, Cr, Ni and Co). PCA results revealed that three major factors were responsible for soil heavy metal pollution, which were initially identified as “steel production”, “agronomic input” and “coal consumption”. The APCS technique, combined with multiple linear regression analysis, was then applied for source apportionment. Steel production appeared to be the main source for Ni, Co, Cd, Zn and Mn, agronomic input for Cu, and coal consumption for Pb and Cr. Geostatistical interpolation using ordinary kriging was finally used to map the spatial distributions of the contributions of pollution sources and further confirm the result interpretations. The introduced method appears to be an effective tool in soil pollution source apportionment and identification, and might provide valuable reference information for pollution control and environmental management.  相似文献   

10.
The Soil Land Inference Model(SoLIM) was primarily proposed by Zhu et al.(Zhu A X, Band L, Vertessy R, Dutton B. 1997. Derivation of soil properties using a soil land inference model(SoLIM). Soil Sci Soc Am J. 61: 523–533.) and was based on the Third Law of Geography. Based on the assumption that the soil property value at a location of interest will be more similar to that of a given soil sample when the environmental condition at the location of interest is more similar to that at the location from which the sample was taken, SoLIM estimates the soil property value of the location of interest using the soil property values of known samples weighted by the similarity between those samples and the location of interest in terms of an attribute domain of environmental conditions. However, the current SoLIM method ignores information about the spatial distances between the location of interest and those of the sample. In this study, we proposed a new method of soil property mapping, So LIM-IDW, which incorporates spatial distance information into the SoLIM method by means of inverse distance weighting(IDW). The proposed method is based on the assumption that the soil property value at a location of interest will be more similar to that of a known sample both when the environmental conditions are more similar and when the distance between the location of interest and the sample location is shorter. Our evaluation experiments on A-horizon soil organic matter mapping in two study areas with independent evaluation samples showed that the proposed SoLIM-IDW method can obtain lower prediction errors than the original SoLIM method, multiple linear regression, geographically weighted regression, and regression-kriging with the same modeling points. Future work mainly includes the determination of optimal power parameter values and the appropriate setting of the parameter under different application contexts.  相似文献   

11.
The three most common techniques to interpolate soil properties at a field scale—ordinary kriging (OK), regression kriging with multiple linear regression drift model (RK + MLR), and regression kriging with principal component regression drift model (RK + PCR)—were examined. The results of the performed study were compiled into an algorithm of choosing the most appropriate soil mapping technique. Relief attributes were used as the auxiliary variables. When spatial dependence of a target variable was strong, the OK method showed more accurate interpolation results, and the inclusion of the auxiliary data resulted in an insignificant improvement in prediction accuracy. According to the algorithm, the RK + PCR method effectively eliminates multicollinearity of explanatory variables. However, if the number of predictors is less than ten, the probability of multicollinearity is reduced, and application of the PCR becomes irrational. In that case, the multiple linear regression should be used instead.  相似文献   

12.
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   

13.
结合高光谱信息的土壤有机碳密度地统计模型   总被引：4，自引：2，他引：2  
传统线性回归模型在借助光谱信息进行土壤属性预测时,通常忽略了土壤自身所具有的空间异质性和依赖性,并且未考虑模型残差的空间结构。针对以上不足,该文以江汉平原232个土壤样本为研究对象,以土壤反射光谱为辅助变量,采用偏最小二乘回归、普通克里格、协同克里格以及回归克里格分别构建土壤有机碳密度预测模型,选取决定系数(R~2)、均方根误差、标准差与预测均方根误差比(ratio of performance to deviation,RPD)对模型预测精度进行对比评价。结果显示,结合高光谱信息,且同时考虑残差空间结构的回归克里格模型表现优于其他模型,预测决定系数R~2为0.617,RPD为1.614。鉴于土壤光谱信息同时还具有测定简单、省时、无损等优点,因此土壤光谱是土壤有机碳密度空间插值的理想辅助因子。  相似文献   

14.
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  
王幼奇  张兴  赵云鹏  包维斌  白一茹 《土壤》2020,52(2):421-426
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   

15.
运用多元线性回归、泛克里格和回归克里格三种方法,结合由DEM获取的地形属性因子预测了河北省土壤有机碳密度的空间分布.多元线性回归预测的残差较大,模型对总方差的解释仅18.6%,采用泛克里格方法后,预测残差降低,预测结果的极差范围变宽,低碳密度区的局部变异得以体现,模型对总方差的解释程度提高到53%.而回归克里格方法应用后预测残差和均方根预测误差进一步降低,模型对总方差的解释程度提高到65%,回归克里格方法也能更好地反映碳密度与地形的关系以及局部变异.三种方法中回归克里格预测效果最好,泛克里格次之,而多元线性回归方法最差.  相似文献   

16.
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   

17.
ABSTRACT

Spatial interpolation methods are frequently used to characterize soil attributes’ spatial variability. However, inconclusive results, about the comparative performance of these methods, have been reported in the literature. Therefore, the present study aimed to analyze the efficiency of ordinary kriging (OK) and inverse distance weighting (IDW) methods in estimating the soil penetration resistance (SPR), soil bulk density (SBD), and soil moisture content (SM) using two distinct sampling grids. The soil sampling was performed on a 5.7 ha area in Southeast Brazil. For data collection, a regular grid with 145 points (20 x 20 m) was created. Soil samples were taken at a 0.20 m layer depth. In order to compare the accuracy of OK and IDW, another grid was created from the initial grid (A), by eliminating one interspersed line, which resulted in a grid with 41 sampled points (40 x 40 m). Results showed that sampling grid A presented less errors than B, proving that the more sampling points, the lower the errors that are associated with both methods will be. Overall, the OK was less biased than IDW only for SBD (A) and SM (B) maps, whereas IDW outperformed OK for the other attributes for both sampling grids.  相似文献   

18.
从数据的统计特征分析、异常值筛选及处理、实验半变异函数计算及拟合等方面对普通克里格计算过程中的精度控制问题进行了初步研究,并以天津市东南郊区土壤砷（As）空间分布评估为例,分析了在多步骤精度控制下普通克里格法估计土壤污染物空间分布的可靠性。结果表明,研究区土壤As表现出了明显的累积特征,且监测值为正态分布,异常值较少且为局部异常值,实验半变异函数表现出明显的各向异性。在较严格的精度控制下,普通克里格估值的最优无偏特性可以得到较好的体现,其对土壤As的空间分布的估计精度可以达到较高的水平。  相似文献   

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