利用数字高程模型改进高山灰岩坑土壤pH值预测

Among spatial interpolation techniques,geostatistics is generally preferred because it takes into account the spatial correlation between neighbouring observations in order to predict attribute values at unsampled locations.A doline of approximately 15 000 m 2 at 1 900 m above sea level (North Italy) was selected as the study area to estimate a digital elevation model (DEM) using geostatistics,to provide a realistic distribution of the errors and to demonstrate whether using widely available secondary data provided more accurate estimates of soil pH than those obtained by univariate kriging.Elevation was measured at 467 randomly distributed points that were converted into a regular DEM using ordinary kriging.Further,110 pits were located using spatial simulated annealing (SSA) method.The interpolation techniques were multi-linear regression analysis (MLR),ordinary kriging (OK),regression kriging (RK),kriging with external drift (KED) and multi-collocated ordinary cokriging (CKmc).A cross-validation test was used to assess the prediction performances of the different algorithms and then evaluate which methods performed best.RK and KED yielded better results than the more complex CKmc and OK.The choice of the most appropriate interpolation method accounting for redundant auxiliary information was strongly conditioned by site specific situations.     

The indicator approach to categorical soil data

In this paper, the first of two, we present the indicator approach to describe the spatial variability of categorical soil data. Indicator kriging is used to obtain conditional probabilities of soil data classes at unsampled locations. A new concept of map purity is defined. Using Sequential Indicator Simulation (SIS), equiprobable realizations of classified maps can be drawn which reflect the probability of occurrence of each class and honour the observed spatial connectivity patterns of the classes and the classes found at the observation sites. When categorical data are used for land resources assessment, the uncertainties accruing from map impurities can be assessed by performing the analysis on each of the maps generated by SIS. In Part I1 of this series, the methods are demonstrated using a case study on the mapping of water table classes and a land use suitability analysis for pasture.     

A practical two‐step approach for mixed model‐based kriging,with an application to the prediction of soil organic carbon concentration

Soil scientists often use prediction models to obtain values at unsampled locations. The spatial variation in the soil is best captured by using the empirical best linear unbiased predictor (EBLUP) based on a restricted maximum likelihood (REML) approach that efficiently exploits available data on both mean trends and correlation structures. We proposed a practical two‐step implementation of the REML approach for model‐based kriging, exemplified by predicting soil organic carbon (SOC) concentrations in mineral soils in Estonia from the large‐scale digital soil map information and a previously established prediction model. The prediction model was a linear mixed model with soil type, physical clay content (particle size < 0.01 mm) and A‐horizon thickness as fixed effects and site, transect, plot, year, year‐transect random intercepts and site‐specific random slopes for clay content. We used only the site‐specific intercept EBLUPs for estimating spatial correlation parameters as they described most of the variation in the random effects (86.8%). Fitting an exponential correlation model to these EBLUPs resulted in an estimated range of 10.5 km and the estimated proportion of the variance from the nugget effect was 0.23. The results of a simulation study showed a downwards bias that decreased with sample size. The results were validated through an external dataset, resulting in root mean square errors (RMSE) of 1.06 and 1.07% for the two‐step approach for kriging and the model with only fixed effects (no kriging), respectively. These results indicate that using the two‐step approach for kriging may improve prediction.     

利用地统计学研究土壤有机质空间分布：评估意大利中部地区土地退化的重要指标

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.     

Spatial Variability of Soil Organic Matter and Cation Exchange Capacity in an Oxisol under Different Land Uses

ABSTRACT

Soil properties may exhibit large spatial variability. Frequently this variability is auto-correlated at a certain scale. In addition to soil-forming factors, soil management, land cover, and agricultural system may affect the spatial variability of agricultural soils. Soil organic matter (OM) is an important soil property contributing toward soil fertility and a key attribute in assessing soil quality. Increasing soil OM increases cation exchange capacity (CEC) and enhances soil fertility. We analyzed the impact of land use on the spatial variability of OM and CEC in a tropical soil, an Oxisol, within São Paulo state, Brazil. Land uses were prairie, maize, and mango. Soil samples were taken at 0–10 and 10–20 cm depths at 84 points within 1-ha plots, i.e., 100 m × 100 m. Statistical variability was higher for soil OM than for CEC. The mango plot contained the highest soil OM, whereas prairie the lowest. Also, soil OM and CEC were significantly related at all land use treatments and depths studied. All soil OM data sets and most of the CEC data sets (with two exceptions) exhibited spatial dependence. When spatial variability was present, the semivariograms showed a nugget effect plus a spherical or an exponential structure. Patterns of soil OM and CEC spatial variability (i.e., model type, ranges of spatial dependence, and nugget effects) were different between land uses and soil depths. In general, CEC exhibited a lower spatial autocorrelation and a weaker spatial structure than soil OM. Moreover, soil OM displayed a higher autocorrelation and was more strongly structured at the 0–10 cm depth than at the 10–20 cm depth. Interpolation by kriging or inverse distance weighting (IDW) allowed to illustrate how the spatial variability of soil OM and CEC differed due to land cover and sampling depth. Modeling and mapping the spatial distribution of soil OM and CEC provided a framework for spatially implicit comparisons of these soil properties, which may be useful for practical applications.     

Determining and mapping some soil physico-chemical properties using geostatistical and GIS techniques in the Naqade region,Iran

The distribution variability of soil electrical conductivity (EC), pH, clay, sand, CaCO₃, organic carbon (OC) and available potassium (K) in the Naqade region was investigated using a geostatistical method and Geographical Information System (GIS) technique. Two hundred and eighty-two topsoil (0–30 cm) samples were randomly collected and analyzed. pH and clay followed a normal distribution, whereas sand EC, CaCO₃, OC and K were log-transformed. The highest variation was observed for soil EC, and the lowest for soil pH. In the variography analysis, spherical, exponential and gaussian models were best fit on experimental semivariograms. The minimum effective spatial autocorrelation was 1500 m for OC and the maximum effective spatial autocorrelation was 4000 m for sand and K. Strong spatial correlations were noted with sand and CaCO₃ (<25%), whereas values were moderate for clay, EC, OC and K (25–75%). Ordinary kriging was utilized for the interpolation of estimated variable determinations in unsampled sites. It was found that soil properties in this study area were strongly influenced by both environmental and natural factors. The results can be used as a source of information for the development and implementation of any further land management and soil and water conservation plans.     

低丘红壤肥力的时空变异

利用地统计学方法研究了我国中亚热带低丘红壤区土壤肥力的时空变异。1 985年和 1 997年 ,在 1 .35km2 的区域内以 1 0 0m× 1 0 0m的网格采集了 1 0 5个表层土壤样品。分析结果表明土壤肥力性质具有较大的空间变异 ,速效磷的变异系数最高 ,而pH的变异系数最低。土壤pH空间相关性强 ,而有机质、速效磷和速效钾空间相关性中等。未开垦前 ,土壤肥力性质的空间相关间距大于等于红壤丘陵的直径 ;开垦 1 2年后 ,土壤肥力性质的相关间距减少 ,其中pH和速效钾的相关间距与红壤丘陵的半径相当。土壤肥力性质及其变化值的克立格插值结果显示出一定的空间相似性 ,说明地统计学方法可以同时分析土壤肥力在时间和空间上的变异     

Modeling Spatial Variability of Soil Chemical Parameters for Site-Specific Farming Using Stochastic Methods

Spatial variability of soil properties affects nutrient transport in the field. The purpose of this study was to examine the extent of spatial variability in the soil chemical parameters, and to develop stochastic models to represent these variations. The parameters selected include concentration levels of Ca, Mg, Mn, NO3-N, P, K and Zn, and OM and pH of soil. Data were collected from 55 grids of 20 × 20 m size from a field within the Coastal Plains of Virginia. Analyses were performed based on the deterministic and stochastic components of the chemical parameters. All the parameters had different degrees of variability in the spatial domain. NO3-N, P, K and Zn exhibited greater degrees of variability compared to other parameters. Among the nine parameters, NO3-N and Zn had the greatest spatial variation with coefficient of variation (CV) of 40 and 49%, respectively, while pH had the lowest variation with a CV of only 4%. The spatial variations of each parameter were not random, but were mutually correlated with their values at the adjoining grids. The analysis showed that the deterministic component of these parameters could be represented by a Fourier series containing sine and cosine functions, while different types of models were required to describe their stochastic component. A second-order autoregressive model, AR(2), for Ca, Mg, Mn and OM; a first-order autoregressive model, AR(1), for pH; and a mixed autoregressive-moving average, ARMA(2,1), for NO3-N, P, K and Zn parameters were found suitable. These models were capable of describing the spatial structure of chemical parameters, and hence can be used to determine their values at any unsampled locations to develop site-specific nutrient management plans for the study site.     

应用土壤质地预测干旱区葡萄园土壤饱和导水率空间分布

田间表层土壤饱和导水率的空间变异性是影响灌溉水分入渗和土壤水分再分布的主要因素之一,研究土壤饱和导水率的空间变化规律,有助于定量估计土壤水分的空间分布和设计农田的精准灌溉管理制度。为了探究应用其他土壤性质如质地、容重、有机质预测土壤饱和导水率空间分布的可行性,试验在7.6 hm2的葡萄园内,采用均匀网格25 m×25 m与随机取样相结合的方式,测定了表层(0～10 cm)土壤饱和导水率、粘粒、粉粒、砂粒、容重和有机质含量,借助经典统计学和地统计学,分析了表层土壤饱和导水率的空间分布规律、与土壤属性的空间相关性,并对普通克里格法、回归法和回归克里格法预测土壤饱和导水率空间分布的结果进行了对比。结果表明:1)土壤饱和导水率具有较强的变异性,平均值为1.64 cm/d,变异系数为1.17;2)表层土壤饱和导水率60%的空间变化是由随机性或小于取样尺度的空间变异造成;3)土壤饱和导水率与粘粒、粉粒、砂粒和有机质含量具有一定空间相关性,而与土壤容重几乎没有空间相关性;4)在中值区以土壤属性辅助的回归克里格法对土壤饱和导水率的预测精度较好,在低值和高值区其与普通克里格法表现类似。研究结果将为更好地描述土壤饱和导水率空间变异结构及更准确地预测其空间分布提供参考。     

地统计学在土壤科学中的应用及展望

地统计学应用于土壤科学中，探索其空间分布特征及其变异规律，已为越来越多的学者所推崇。半方差函数提供了一个定量工具，可以将土壤某一性质的变异与成土因子和成土过程联系起来，使人们加深对土壤的作用后果的理解，而克立格可以对未采样区的区域化变量的取值进行无偏最优估计，在对地统计学的理论进行简要阐述的基础上，介绍了其近几年的发展，回顾了它在土壤科学研究中取得的一些成果，并就地统计学在土壤科学中的应用前景作了展望。     

Integration of multiple soil parameters to evaluate soil quality: a field example

Development of a method to assess and monitor soil quality is critical to soil resource management and policy formation. To be useful, a method for assessing soil quality must be able to integrate many different kinds of data, allow evaluation of soil quality based on alternative uses or definitions and estimate soil quality for unsampled locations. In the present study we used one such method, based on non-parametric geostatistics. We evaluated soil quality from the integration of six soil variables measured at 220 locations in an agricultural field in southeastern Washington State. We converted the continous data values for each soil variable at each location to a binary variable indicator transform based on thresholds. We then combined indicator transformed data for individual soil variables into a single integrative indicator of soil quality termed a multiple variable indicator transform (MVIT). We observed that soil chemical variables, pools of soil resources, populations of microorgansims, and soil enzymes covaried spatially across the landscape. These ensembles of soil variables were not randomly distributed, but rather were systematically patterned. Soil quality maps calculated by kriging showed that the joint probabilities of meeting specific MVIT selection were influenced by the critical threshold values used to transform each individual soil quality variable and the MVIT selection criteria. If MVIT criteria adequately reflect soil quality then the kriging can produce maps of the probabilty of a soil being of good or poor quality.     

Relationships between spatial pattern of Locusta migratoria manilensis eggpods and soil property variability in coastal areas

Spatial distribution of Locusta migratoria manilensis eggpods and soil properties (water content at 5 cm depth, salinity, organic matter and pH) was studied by integrating geostatistical analysis and GIS techniques. During 2 years of surveys over the entire study area (6000 ha), extensive data (292 regularly grids with 450-m intervals), coupled with intensive data (2601 regularly grids of 0.5-m separation) were used to characterize spatial patterns of L. m. manilensis eggpods and soil property variability and to explore the relationship between them. Semivariograms indicated the eggpods and four soil properties showed high spatial heterogeneity. The spatial distribution of eggpods, at distances ranging from 50-452 m in spatial autocorrelation, was best described using the spherical model. Spatial autocorrelation in total spatial heterogeneity in soil water content at 5 cm, salinity, organic matter and pH were 76.15, 78.04, 57.19 and 61.85%, respectively, and the scales of spatial heterogeneity were 621, 594, 1014 and 1368 m, respectively. GIS assessment maps of eggpods and soil properties, derived by block kriging, displayed patterns of the locust eggpods and soil property variability at an area-wide scale. Most eggpods (66.27% and 72.24% for 2002 and 2003, respectively) were found at the areas with low salinity (<2.0% approximately), suitable water content at 5 cm (10.1-20.0%). No eggpods were found at such areas with high soil water content at 5 cm (>30%) and salinity (>3%). In a way, the spatial distribution pattern of locust eggpods mainly depended on the soil heterogeneity at the study area. t-Tests indicated that sites between with eggpods and without eggpods were significantly different only in soil water content at 5 cm depth and salinity. The results may provide useful information on sampling in the field, forecasting and monitoring locust plague and reclaiming coastal locust breeding areas in China.     

Geostatistical modelling and mapping of nematode-based soil ecological quality indices in a polluted nature reserve

Nematodes are indicators of soil quality and soil health. Knowledge of the relationships between nematode-based soil quality indices and environmental properties is beneficial for assessing environmental threats on soil biota. This study evaluated the spatial distribution of nematode-based soil quality indices in a 23-ha heavy metal-polluted nature reserve using geostatistical methods. We expected that a selection of abiotic soil properties (pH and moisture, clay, organic matter, cadmium (Cd), and zinc (Zn) contents) could explain a significant portion of the spatial variation of the indices and that regression kriging could more accurately model their spatial distribution than ordinary kriging. A stratified simple random sampling scheme was used to select 80 locations where soil samples were taken to extract nematodes and derive the indices. The area had a distinct gradient in soil properties with Cd and Zn content ranging from 0.07 to 68.9 and 5.3 to 1329 mg kg^-1, respectively. Linear regression models were fitted to describe the relationships between the indices and soil properties. By also modelling the spatial correlation structure of regression residuals using spherical semivariograms, regression kriging was used to produce maps of the indices. The regression models explained between 21% and 44% of the total original variance in the indices. Soil pH was a significant explanatory variable in almost all cases, while heavy metal conent had a remarkably low effect. In some cases, the regression residuals had spatial structure. Independent validation indicated that in all cases, regression kriging performed slightly better because of having lower values of the root mean square prediction error and a mean prediction error closer to zero than ordinary kriging. This study showed the importance of soil properties in explaining the spatial distribution of biological soil quality indices in ecological risk assessment.     

Apparent Electrical Conductivity in Correspondence to Soil Chemical Properties and Plant Nutrients in Soil

Spatial variability and relationship between soil apparent electrical conductivity (EC_a), soil chemical properties, and plant nutrients in soil have not been well documented in Malaysian paddy fields. For this reason precision farming has been used for assessing field conditions. EC_a technique for describing soil spatial variability is used for soil data acquisition. Soil sampling provides the data used to make maps of the spatial patterns in soil properties. Maps are then used to make recommendations on the variation of application rates. The main purpose of the authors in this study was to generate variability map of soil EC_a within a Malaysian rice cultivation area using VerisEC sensor. The EC_a values were compared to some soil properties after delineation. Measured parameters were mapped using kriging technique and their correlation with soil EC_a was determined. Through this study the authors showed that the EC sensor can determine soil spatial variability, where it can acquire the soil information quickly.     

OPTIMAL INTERPOLATION AND ISAR1THMIC MAPPING OF SOIL PROPERTIES

Kriging is a means of spatial prediction that can be used for soil properties. It is a form of weighted local averaging. It is optimal in the sense that it provides estimates of values at unrecorded places without bias and with minimum and known variance. Isarithmic maps made by kriging are alternatives to conventional soil maps where properties can be measured at close spacings. Kriging depends on first computing an accurate semi-variogram, which measures the nature of spatial dependence for the property. Estimates of semi-variance are then used to determine the weights applied to the data when computing the averages, and are presented in the kriging equations. The method is applied to three sets of data from detailed soil surveys in Central Wales and Norfolk. Sodium content at Plas Gogerddan was shown to vary isotropically with a linear semi-variogram. Simple punctual kriging produced a map with intricate isarithms and fairly large estimation variance, attributed to a large nugget effect. Sloniness on the same land varied anisotropically with a linear semi-variogram. and again the estimation error of punctual kriging was fairly large. At Hole Farm. Norfolk, the thickness of cover loam varied isotropically, but with a spherical semi-variogram. Its parameters were estimated and used to krige point values and produce a map showing substantial short-range variation.     

Optimal interpolation and isarithmic mapping of soil properties: I The semi‐variogram and punctual kriging

Kriging is a means of spatial prediction that can be used for soil properties. It is a form of weighted local averaging. It is optimal in the sense that it provides estimates of values at unrecorded places without bias and with minimum and known variance. Isarithmic maps made by kriging are alternatives to conventional soil maps where properties can be measured at close spacings. Kriging depends on first computing an accurate semi‐variogram, which measures the nature of spatial dependence for the property. Estimates of semi‐variance are then used to determine the weights applied to the data when computing the averages, and are presented in the kriging equations. The method is applied to three sets of data from detailed soil surveys in Central Wales and Norfolk. Sodium content at Plas Gogerddan was shown to vary isotropically with a linear semi‐variogram. Ordinary punctual kriging produced a map with intricate isarithms and fairly large estimation variance, attributed to a large nugget effect. Stoniness on the same land varied anisotropically with a linear semi‐variogram, and again the estimation error of punctual kriging was fairly large. At Hole Farm, Norfolk, the thickness of cover loam varied isotropically, but with a spherical semi‐variogram. Its parameters were estimated and used to krige point values and produce a map showing substantial short‐range variation.     

Field-scale variability of topsoil dehydrogenase and cellulase activities as affected by variability of some physico-chemical properties

We have studied spatial field-scale variability of soil dehydrogenase (DH) and cellulase activities (CEL) and their relationship with variability of some physico-chemical properties at the surface horizon of the agricultural field. Soil samples were collected at 50 points from the upper 20 cm of soil. The activity of DH ranged between 0.77 and 1.5 μM TPP·g⁻¹·h⁻¹ while CEL activity ranged from 0.8 to 1.94 μM glucose·g⁻¹·24 h⁻¹. Concentrations of C_ORG and TN varied from 8.5 to 31.7 g·kg⁻¹ and from 0.94 to 3.56 g·kg⁻¹, respectively. The soil data showed that spatial variability and semivariograms describe spherical and linear models with the nugget effect (DH, CEL, C_ORG and TN). Dehydrogenase activity was in the strong variability class, while cellulase activity was situated in the week variability class. Both C_ORG and TN concentrations and pH_KCl values were strongly spatially dependent with the percentage of total variance (sill) presents as nugget variance ranging from 8.9% to 16.1%. Kriged maps displayed the lowest values of CEL activities in the north-east of the area, while the south area showed the highest CEL activity. The DH activity values were irregularly distributed in the surface horizon of the studied soil and this behaviour did not correspond with the spatial distribution of other properties.     

水稻土物理性质空间变异性研究

本文应用时间序列分析方法和随机统计理论研究了水稻土物理性质田间实际观测值的变异特性,着重讨论了它们在二维平面上的空间变异结构。结果表明,各项性质在田间的变异和分布不是完全随机的,在一定的范围内,各测点的观测值之间存在着空间相关现象。因此,根据统计学原理,土壤观测样点的选取,除了需有合适的数目以外,还应确定观测点的合适大小以及观测点之间的合适间距。同时,本文通过土壤大团聚体含量变异原因的分析,表明了各性质的变异是相互关联、相互作用的。对以空间变异结构为基础的Kriging内插技术作了初步的尝试,取得了较好的效果,其內插精度比趋势面法有明显提高,内插值与真值之间的方差平均降低了32%。     

Spatial variability of the chemical characteristics of a trace-element-contaminated soil before and after remediation

This study addresses the spatial and temporal variability of soil properties before and after the application of organic and inorganic amendments in a trace-element-polluted soil using statistics and geostatistical methods. The experiment took place in a plot (20×50 m) affected by the acid toxic pyritic sludge from the Aznalcóllar mine (Seville, Spain) in April 1998. Soil samples from 0- to 15-cm depth were collected within 48 locations, on a 14×45 m grid in 2002 and 2003, respectively. The samples were analysed for pH, total organic carbon, total sulphur and total, available and soluble As, Cd, Cu, Pb and Zn concentrations. Classic statistical and geostatistical methods were used to assess variability in contamination levels.All soil properties determined in the plot showed a large variability with high coefficients of variation. In both years, mean values of total concentrations of As, Cd, Cu, Pb and Zn were higher than the background values in the area. In general, amendment application increased soil pH and total organic carbon content and decreased heavy metal solubility, however it did not have a clear effect on total and available trace-element contents. Experimental semivariograms were developed to determine the spatial dependence of soil properties and were adjusted to spherical and linear models with nugget effect. Then, the spatial distribution of the different variables was estimated by kriging to design contour maps. These contour maps can help to identify the pollution patterns and delineate the range of contamination. A spatial similarity pattern among total As and Pb (the lesser mobile elements) and total S content was found in both samplings revealing a correspondence between the contamination and spots of residual sludge. Levels of metal pollution were influenced by soil pH. Despite those clean-up efforts the soil still presents significant levels of pollution related to the presence of remaining sludge in the soil.The kriging-interpolated maps were a very valuable tool in studying pollution and monitoring soil parameters after amendment application at field scale.