Improved Prediction and Reduction of Sampling Density for Soil Salinity by Different Geostatistical Methods

The spatial estimation for soil properties was improved and sampling intensities also decreased in terms of incorporated auxiliary data. In this study, kriging and two interpolation methods were proven well to estimate auxiliary variables： cokriging and regression-kriging, and using the salinity data from the first two stages as auxiliary variables, the methods both improved the interpolation of soil salinity in coastal saline land. The prediction accuracy of the three methods was observed under different sampling density of the target variable by comparison with another group of 80 validation sample points, from which the root-mean-square error （RMSE） and correlation coefficient （r） between the predicted and measured values were calculated. The results showed, with the help of auxiliary data, whatever the sample size of the target variable may be, cokriging and regression-kriging performed better than ordinary kriging. Moreover, regression-kriging produced on average more accurate predictions than cokriging. Compared with the kriging results, cokriging improved the estimations by reducing RMSE from 23.3 to 29% and increasing r from 16.6 to 25.5%, regression-kriging improved the estimations by reducing RMSE from 25 to 41.5% and increasing r from 16.8 to 27.2%. Therefore, regression-kriging shows promise for improved prediction for soil salinity and reduction of soil sampling intensity considerably while maintaining high prediction accuracy. Moreover, in regression-kriging, the regression model can have any form, such as generalized linear models, non-linear models or tree-based models, which provide a possibility to include more ancillary variables.     

Soil Phosphorus and Potassium Mapping Using a Spatial Correlation Model Incorporating Terrain Slope Gradient

Variable-rate fertilizer application requires knowledge of the spatial distribution of soil nutrients within fields. Grid soil sampling might be used for acquiring this information, but is often too expensive for resolving spatial patterns in soil nutrients at the scale of precision fertilizer application. The objective of this study was to determine whether grid sampling efficiency can be improved using cokriging estimates with slope gradient as a secondary variable, which is easily obtained from high-resolution digital elevation models. Soils in two northern Montana wheat fields were sampled at the nodes of a 100-m diagonal grid. Soil test phosphorus and potassium maps were constructed with kriging and cokriging. Co-kriging uses the spatial correlation between two variables to predict for the less intensively sampled variable of interest, often with less estimation error than a univariate method such as kriging. The average estimation variance for cokriging compared to kriging was reduced for all values of the correlation considered. The additional complexity of cokriging might be justified provided a secondary variable exists that is spatially cross correlated with the primary variable of interest.     

Improving Map Accuracy of Soil Variables Using Soil Electrical Conductivity as a Covariate

Accurate characterization of soil properties across a field can be difficult, especially when compounded with the diverse landscapes used for pastureland. Indirect methods of data collection have the advantage of being rapid, noninvasive, and dense; they may improve mapping accuracy of selected soil parameters. The objective of this study was to determine if the use of soil electrical conductivity (EC) as a covariate improved mapping accuracy of five soil variables across four sampling schemes and two sampling densities in a central Iowa, USA pasture. In this study, cokriging methods were compared to kriging methods for the measured soil properties of soil pH, available P and K, organic matter and moisture. Maps resulting from cokriging each of the soil variables with soil EC exhibited more local detail than the kriged maps of each soil variable. A small, but inconsistent, improvement occurred in kriging variance and prediction accuracy of non-sampled sites when cokriging was implemented. The improvement was generally greater for soil variables more highly correlated with soil EC. This work indicates that cokriging of EC with less densely and invasively collected soil parameters of P, K, pH, organic matter (OM) and moisture does not consistently and substantially improve the characterization accuracy of pasture soil variability.     

Weed Mapping with Co-Kriging Using Soil Properties

Our aim is to build reliable weed maps to control weeds in patches. Weed sampling is time consuming but there are some shortcuts. If an intensively sampled variable (e.g. soil property) can be used to improve estimation of a sparsely sampled variable (e.g. weed distribution), one can reduce weed sampling. The geostatistical estimation method co-kriging uses two or more sampled variables, which are correlated, to improve the estimation of one of the variables at locations where it was not sampled. We did an experiment on a 2.1ha winter wheat field to compare co-kriging using soil properties, with kriging based only on one variable. The results showed that co-kriging Lamium spp. from 96 0.25m2 sample plots ha–1 with silt content improved the prediction variance by 11 % compared to kriging. With 51 or 18 sample plots ha–1 the prediction variance was improved by 21 and 15 %.     

Integrating Remote Sensing and Proximal Sensors for the Detection of Soil Moisture and Salinity Variability in Coastal Areas

Soil moisture and salinity are two crucial coastal saline soil variables, which influence the soil quality and agricultural productivity in the reclaimed coastal region. Accurately characterizing the spatial variability of these soil parameters is critical for the rational development and utilization of tideland resources. In the present study, the spatial variability of soil moisture and salinity in the reclaimed area of Hangzhou gulf, Shangyu City, Zhejiang Province, China, was detected using the data acquired from radar image and the proximal sensor EM38. Soil moisture closely correlates radar scattering coefficient, and a simplified inversion model was built based on a backscattering coefficient extracted from multi-polarization data of ALOS/PALSAR and in situ soil moisture measured by a time domain reflectometer to detect soil moisture variations. The result indicated a higher accuracy of soil moisture inversion by the HH polarization mode than those by the HV mode. Soil salinity is reflected by soil apparent electrical conductivity (ECa). Further, ECa can be rapidly detected by EM38 equipment in situ linked with GPS for characterizing the spatial variability of soil salinity. Based on the strong spatial variability and interactions of soil moisture and salinity, a cokriging interpolation method with auxiliary variable of backscattering coefficient was adopted to map the spatial variability of ECa. When compared with a map of ECa interpolated by the ordinary kriging method, detail was revealed and the accuracy was increased by 15.3%. The results conclude that the integrating active remote sensing and proximal sensors EM38 are effective and acceptable approaches for rapidly and accurately detecting soil moisture and salinity variability in coastal areas, especially in the subtropical coastal zones of China with frequent heavy cloud cover.     

Estimating soil organic matter using interpolation methods with a electromagnetic induction sensor and topographic parameters: a case study in a humid region

Soil organic matter (SOM) is a key indicator of soil quality although, usually, detailed data for a given area is difficult to obtain at low cost. This study was conducted to evaluate the usefulness of soil apparent electrical conductivity (EC_a), measured with an electromagnetic induction sensor, to improve the spatial estimation of SOM for site-specific soil management purposes. Apparent electrical conductivity was measured in a 10-ha prairie in NW Spain in November 2011. The EC_a measurements were used to design a sampling scheme of 80 locations, at which soil samples were collected from 0 to 20 cm depth and from 20 cm to the boundary of the A horizon (ranging from 25 to 48 cm). The SOM values determined at the two depths considered were weighted to obtain the results for the entire A Horizon. SOM distribution maps were obtained by inverse distance weighting and geostatistical techniques: ordinary kriging (OK), cokriging (COK), regression kriging either with linear models (LM-RK) or with random forest (RF-RK). SOM ranged from 46.3 to 78.0 g kg^?1, whereas EC_a varied from 6.7 to 14.7 mS m^?1. These two variables were significantly correlated (r = ?0.6, p < 0.05); hence, EC_a was used as an ancillary variable for interpolating SOM. A strong spatial dependence was found for both SOM and EC_a. The maps obtained exhibited a similar spatial pattern for SOM; COK maps did not show a significant improvement from OK predictions. However, RF-RK maps provided more accurate spatial estimates of SOM (error of predictions was between four and five times less than the other interpolators). This information is helpful for site-specific management purposes at this field.     

Cokriging of Soil Cation Exchange Capacity Using the First Principal Component Derived from Soil Physico-Chemical Properties

As soil cation exchange capacity (CEC) is a vital indicator of soil quality and pollutant sequestration capacity,a study was conducted to evaluate cokriging of CEC with the principal components derived from soil physico-chemical properties.In Qingdao,China,107 soil samples were collected.Soil CEC was estimated by using 86 soil samples for prediction and 21 soil samples for test.The first two principal components (PC1 and PC2) together explained 60.2％ of the total variance of soil physico-chemical properties.The PC1 was highly correlated with CEC (r=0.76,P＜0.01),whereas there was no significant correlation between CEC and PC2 (r=0.03).The PC1 was then used as an auxiliary variable for the prediction of soil CEC.Mean error (ME) and root mean square error (RMSE) of kriging for the test dataset were -1.76 and 3.67 cmolc kg-1,and ME and RMSE of cokriging for the test dataset were -1.47 and 2.95 cmolc kg-1,respectively.The cross-validation R2 for the prediction dataset was 0.24 for kriging and 0.39 for cokriging.The results show that cokriging with PC1 is more reliable than kriging for spatial interpolation.In addition,principal components have the highest potential for cokriging predictions when the principal components have good correlations with the primary variables.     

Combined use of hyperspectral VNIR reflectance spectroscopy and kriging to predict soil variables spatially

Hyperspectral visible near infrared reflectance spectroscopy (VNIRRS) and geostatistical methods are considered for precision soil mapping. This study evaluated whether VNIR or geostatistics, or their combined use, could provide efficient approaches for assessing the soil spatially and associated reductions in sample size using soil samples from a 32 ha area (800 × 400 m) in northern Turkey. Soil variables considered were CaCO₃, organic matter, clay, sand and silt contents, pH, electrical conductivity, cation exchange capacity (CEC) and exchangeable cations (Ca, Mg, Na and K). Cross-validation was used to compare the two approaches using all grid data (n = 512), systematic selections of 13, 25 and 50% of the data and random selections of 13 and 25% for calibration; the remaining data were used for validation. Partial least squares regression (PLSR) analysis was used for calibrating soil properties from first derivative VNIR reflectance spectra (VNIRRS), whereas ordinary-, co- and regression-kriging were used for spatial prediction. The VNIRRS-PLSR method provided better prediction results than ordinary kriging for soil organic matter, clay and sand contents, (R ² values of 0.56–0.73, 0.79–0.85, 0.65–0.79, respectively) and smaller root mean squared errors of prediction (values of 2.7–4.1, 37.4–43, 46.9–61, respectively). The EC, pH, Na, K and silt content were predicted poorly by both approaches because either the variables showed little variation or the data were not spatially correlated. Overall, the prediction accuracy of VNIRRS-PLSR was not affected by sample size as much as it was for ordinary kriging. Cokriging (COK) and regression kriging (RK) were applied to a combination of values predicted by VNIR reflectance spectroscopy and measured in the laboratory to improve the accuracy of prediction of the soil properties. The results showed that both COK and RK with VNIRRS estimates improved the predictions of soil variables compared to VNIRRS and OK. The combined use of VNIRRS and multivariate geostatistics results in better spatial prediction of soil properties and enables a reduction in sampling and laboratory analyses.     

Optimised Spatial Sampling Scheme for Soil Electriclal Conductivity Based on Variance Quad-Tree （VQT） Method

The acquisition of precise soil data representative of the entire survey area,is a critical issue for many treatments such as irrigation or fertilization in precision agriculture.The aim of this study was to investigate the spatial variability of soil bulk electrical conductivity(ECb)in a coastal saline field and design an optimized spatial sampling scheme of ECb based on a sampling design algorithm,the variance quad-tree(VQT)method.Soil ECb data were collected from the field at 20m interval in a regular grid scheme.The smooth contour map of the whole field was obtained by ordinary kriging interpolation,VQT algorithm was then used to split the smooth contour map into strata of different number desired,the sampling locations can be selected within each stratum in subsequent sampling.The result indicated that the probability of choosing representative sampling sites was increased significantly by using VQT method with the sampling number being greatly reduced compared to grid sampling design while retaining the same prediction accuracy.The advantage of the VQT method is that this scheme samples sparsely in fields where the spatial variability is relatively uniform and more intensive where the variability is large.Thus the sampling efficiency can be improved,hence facilitate an assessment methodology that can be applied in a rapid,practical and cost-effective manner.     

用高程辅助提高土壤属性的空间预测精度

 【目的】探讨土壤属性变量与高程之间在何种条件下，可利用高程变量来辅助提高土壤变量的预测精度。【方法】用两种将高程作为辅助变量的克里格插值方法（协克里格法和简单克里格加变化局部平均值法）与没有考虑高程的普通克里格插值方法进行对比分析，用均方根预测误差和预测精度的相对提高值作为标准对3种方法的预测结果进行评价。【结果】对于交换性钾和pH值，协克里格法获得最精确的预测；对于Olsen-P、土壤有机质和有效锌，简单克里格加变化局部平均值法得到最精确的预测；而有效铜、有效铁和有效锰的最精确的预测结果则由普通克里格法产生。【结论】高程数据能够用来提高土壤特征的空间预测精度，但并不是对所有的土壤属性都适合；在利用高程数据来提高土壤属性空间预测之前，应该先对高程和土壤特征变量之间的线性相关关系、结构相关关系和全局趋势等进行仔细地分析，然后再选择适宜的方法。     

Variograms of Ancillary Data to Aid Sampling for Soil Surveys

To provide reliable estimates for mapping soil properties for precision agriculture requires intensive sampling and costly laboratory analyses. If the spatial structure of ancillary data, such as yield, digital information from aerial photographs, and soil electrical conductivity (EC) measurements, relates to that of soil properties they could be used to guide the sampling intensity for soil surveys. Variograms of permanent soil properties at two study sites on different parent materials were compared with each other and with those for ancillary data. The ranges of spatial dependence identified by the variograms of both sets of properties are of similar orders of magnitude for each study site. Maps of the ancillary data appear to show similar patterns of variation and these seem to relate to those of the permanent properties of the soil. Correlation analysis has confirmed these relations. Maps of kriged estimates from sub-sampled data and the original variograms showed that the main patterns of variation were preserved when a sampling interval of less than half the average variogram range of ancillary data was used. Digital data from aerial photographs for different years and EC appear to show a more consistent relation with the soil properties than does yield. Aerial photographs, in particular those of bare soil, seem to be the most useful ancillary data and they are often cheaper to obtain than yield and EC data.     

罗江县农田土壤全氮协同克里格插值和采样数量优化研究

[目的]研究县域土壤全氮含量的空间分布和采样数量,为紫色土丘陵区采样提供参考。[方法]利用协同克里格法,以初始的1 777个土壤全氮含量数据为随机抽取的数据,分别随机抽取1 599、1 421和1 243个数据为目标变量,并以初始的1 777个土壤有机质数据为辅助变量,对四川省罗江县土壤全氮含量进行插值分析,从而利用协同克里格法对县域尺度下农田土壤全氮含量在不同样点数量下空间分布中的适用性进行评价。[结果]在相同取样数量下,全氮协同克里格法的均方根误差相对于普通克里格法降低0.019 6%～0.072 5%,预测值和实测值之间的相关系数提高0.69%～0.90%。利用协同克里格法,土壤全氮含量数据在缩减30%情况下,其估值精度高于1 777个样点下的普通克里格估值,且二者的分布图都具有较高的拟合度。[结论]协同克里格法是一种经济、精准的方法,可为县域土壤养分含量的空间分布提供基础信息。     

Spatial Interpolation and Sample Size Optimization for Soil Copper （Cu） Investigation in Cropland Soil at County Scale Using Cokriging

Knowledge on spatial distribution and sampling size optimization of soil copper （Cu） could lay solid foundations for environmetal quality survey of agricultural soils at county scale. In this investigation, cokriging method was used to conduct the interpolation of Cu concentraiton in cropland soil in Shuangliu County, Sichuan Province, China. Based on the original 623 physicochmically measured soil samples, 560, 498, and 432 sub-samples were randomly selected as target variable and soil organic matter （SOM） of the whole original samples as auxiliary variable. Interpolation results using Cokriging under different sampling numbers were evaluated for their applicability in estimating the spatial distribution of soil Cu at county sacle. The results showed that the root mean square error （RMSE） produced by Cokriging decreased from 0.9 to 7.77%, correlation coefficient between the predicted values and the measured increased from 1.76 to 9.76% in comparison with the ordinary Kriging under the corresponding sample sizes. The prediction accuracy using Cokriging was still higher than original 623 data using ordinary Kriging even as sample size reduced 10%, and their interpolation maps were highly in agreement. Therefore, Cokriging was proven to be a more accurate and economic method which could provide more information and benefit for the studies on spatial distribution of soil pollutants at county scale.     

A comparison of different algorithms for the delineation of management zones

One approach to the application of site-specific techniques and technologies in precision agriculture is to subdivide a field into a few contiguous homogenous zones, often referred to as management zones (MZs). Delineating MZs can be based on some sort of clustering, however there is no widely accepted method. The application of fuzzy set theory to clustering has enabled researchers to account better for the continuous variation in natural phenomena. Moreover, the methods based on non-parametric density estimation can detect clusters of unequal size and dispersion. The objectives of this paper were to: (1) compare different procedures for creating management zones and (2) determine the relation of the MZs delineated with potential yield. One hundred georeferenced point measurements of soil and crop properties were obtained from a 12 ha field cropped with durum wheat for two seasons. The trial was carried out at the experimental farm of CRA-CER in Foggia (Italy). All variables were interpolated on a 1 × 1 m grid using the geostatistical techniques of kriging and cokriging. The techniques compared to identify MZs were: (1) the ISODATA method, (2) the fuzzy c-means algorithm and (3) a non-parametric density algorithm. The ISODATA method, which was the simplest, subdivided the field into three distinct classes of suitable size for uniform management, whereas the other two methods created two classes. The non-parametric density algorithm characterized the edge properties between adjacent clusters more efficiently than the fuzzy method. The clusters from the non-parametric density algorithm and yield maps for three seasons (2005–2006, 2006–2007 and 2007–2008) were compared and agreement measures were computed. The kappa coefficients for the three seasons were negative or small positive values which indicate only slight agreement. These results illustrate the importance of temporal variation in spatial variation of yield in rainfed conditions, which limits the use of the MZ approach.     

Geostatistical modelling of within-field soil and yield variability for management zones delineation: a case study in a durum wheat field

The paper proposes a geostatistical approach for delineating management zones (MZs) based on multivariate geostatistics, showing the use of polygon kriging to compare durum wheat yield among the different MZs (polygons). The study site was a durum wheat field in southern Italy and yield was measured over three crop seasons. The first regionalized factor, calculated with factorial cokriging, was used to partition the field into three iso-frequency classes (MZs). For each MZ, the expected value and standard deviation of yield were estimated with polygon kriging over the three crop seasons. The yield variation was only in part related to soil properties but most of it might be ascribable to different patterns of meteorological conditions. Both components of variation (plant and soil) in a cropping system should then be taken into account for an effective management of rainfed durum wheat in precision agriculture. The proposed approach proved multivariate Geostatistics to be effective for MZ delineation even if further testing is required under different cropping systems and management.     

Data fusion techniques for delineation of site-specific management zones in a field in UK

Fusion of different data layers, such as data from soil analysis and proximal soil sensing, is essential to improve assessment of spatial variation in soil and yield. On-line visible and near infrared (Vis–NIR) spectroscopy have been proved to provide high resolution information about spatial variability of key soil properties. Multivariate geostatistics tools were successfully implemented for the delineation of management zones (MZs) for precision application of crop inputs. This research was conducted in a 18 ha field to delineate MZs, using a multi-source data set, which consisted of eight laboratory measured soil variables (pH, available phosphorus (P), cation exchange capacity, total nitrogen (TN), total carbon (TC), exchangeable potassium (K), sand, silt) and four on-line collected Vis–NIR spectra-based predicted soil variables (pH, P, K and moisture content). The latter set of data was predicted using the partial least squares regression (PLSR) technique. The quality of the calibration models was evaluated by cross-validation. Multi-collocated cokriging was applied to the soil and spectral data set to produce thematic spatial maps, whereas multi-collocated factor cokriging was applied to delineate MZ. The Vis–NIR predicted K was chosen as the exhaustive variable, because it was the most correlated with the soil variables. A yield map of barley was interpolated by means of the inverse distance weighting method and was then classified into 3 iso-frequency classes (low, medium and high). To assess the productivity potential of the different zones of the field, spatial association between MZs and yield classes was calculated. Results showed that the prediction performance of PLSR calibration models for pH, P, MC and K were of excellent to moderate quality. The geostatistical model revealed good performance. The estimates of the first regionalised factor produced three MZs of equal size in the studied field. The loading coefficients for TC, pH and TN of the first factor were highest and positive. This means that the first factor can be assumed as a synthetic indicator of soil fertility. The overall spatial association between the yield classes and MZs was about 40 %, which reveals that more than 50 % of the yield variation can be attributed to more dynamic factors than soil parameters, such as agro-meteorological conditions, plant diseases and nutrition stresses. Nevertheless, multivariate geostatistics proved to be an effective approach for site-specific management of agricultural fields.     

基于对数比转换的成分数据空间插值研究

【目的】以新疆玛纳斯河绿洲表层土壤质地数据为例,研究对数比转换方法在成分数据空间插值中的应用。【方法】采用加法、中心化和等角3种不同对数比转换方法,对土壤颗粒含量数据进行转换,针对数据中的零值不能进行对数比转换问题引入了零值替换方法,空间插值采用普通克里格法。【结果】零值替换后土壤颗粒之和仍为100%。基于对数比转换的插值结果满足土壤质地颗粒组成定和100%的要求,而对土壤颗粒单独插值不满足定和100%的要求。插值结果精度评价表明基于等角对数比转换方法的插值结果最优,但3种方法的结果差别甚小。【结论】零值替换方法的引入在不改变成分数据定和的前提下避免了零值不能进行对数比转换。基于对数比转换的普通克里格法满足成分数据空间插值的非负、定和、误差最小和无偏估计4个要求。     

Multi-scale assessment of the risk of soil salinization in an area of south-eastern Sardinia (Italy)

The assessment and mapping of the risk of soil salinization can contribute to sustainable land planning aimed at mitigating soil degradation and increasing crop production. A probabilistic approach, based on multivariate geostatistics was used to model the spatial variation of soil salinization risk at the landscape scale and to delineate the areas at high risk. The study site is a citrus growing area in south-eastern Sardinia (Italy). Electrical conductivity (EC_e), exchangeable sodium percentage (ESP), pH and ‘total clay + fine silt content’ (FIN), were measured in the topsoil (0–40 cm). The method requires indicator coding, which transforms measured data values into a binary variable according to critical thresholds. These latter were set to: 4 dS m^?1 for EC_e, 10% for ESP, 8 for pH, and 40% for ‘total clay + fine silt content’. To determine the probability of exceeding these critical values, multi-collocated indicator cokriging was used. Factorial kriging was also applied to identify one regionalized factor that summarizes the effects of the selected variables on soil salinization. Maps of each soil indicator and regionalized factor were produced to show the areas at risk of salinization. The results are valuable for planning the management of salinity.     

基于逐步回归模型和EM38电磁感应分析的干旱区人工水库区域土壤盐分测定——以新疆八户地水库为例

通过电磁感应仪EM38与传统采样测定方式,建立了磁感式表观电导率和土壤盐分之间的回归模型,运用该模型对新疆厅台县八户地水库大坝外侧的土壤盐分进行了预测,并利用Kriging插值方法,绘制了0～20、20～40、40～60、60～80cm各层土壤盐分的空间分布图.结果表明:0～20、20～40、40～60、60～80cm土壤盐分预测模型的复相关系数分别达到0.62、0.73、0.76、0.82,可以看出随着土壤深度的加深,拟合效果也越好;各层土壤含盐量在10g/kg以上的分布面积占调查区域的80%以上,属于盐土;土壤盐分在垂直方向上大致呈"反C"型分布,在20～60cm土层上有明显积聚;土壤盐分在空间上由南向北呈逐渐升高的趋势,在水库大坝附近和靠近水渠方向的土壤含盐量明显降低.     

Interpreting Soil Electrical Conductivity and Terrain Attribute Variability with Soil Surveys

Utilizing soil electrical conductivity (EC) measurements and terrain attributes for precision management will require secondary soil information for adequate interpretation. The objective of this study was to determine whether readily available second-order soil surveys were of adequate quality to aid with interpreting soil EC and terrain data. For three locations in Kentucky, USA, first-order soil surveys were created, second-order surveys reports were obtained, elevation was measured and used to calculate terrain attributes (slope, aspect, plan curvature, profile curvature), and bulk soil electrical conductivity was measured. Three analytical methods (an ordinary least squares analysis and two random field analyses), visual map assessment, and examination of least-squares means were used to assess the relationships between soil EC measurements, terrain attributes and first- and second-order soil surveys. The OLS and random field analyses were problematic. However, the ranking of the OLS F-statistics appeared to reflect the general relationship between landscape variables and first-order soil surveys. The landscape variables related particularly well with soil properties that had been impacted by past soil erosion. Unfortunately, however, second-order soil surveys in this study were not created at suitable scales to adequately interpret EC and terrain data regarding erosion history or other attributes. While these surveys may provide some useful information, field measurements, sampling, and observations will likely be required to develop high quality interpretations of soil EC and terrain attribute data.