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.     

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.     

Investigation and prediction spatial variability in chemical properties of agricultural soil using geostatistics

Determination of the chemical characteristics of soil for balanced fertilization on large scales is an important factor in achieving a precision agriculture. Laboratory analyses of soil properties are usually expensive and time consuming. Surmounting these problems is possible using geostatistics. Therefore, this research aims at selecting a proper interpolation method using 213 soil samples for alfalfa farmland in Hamadan Province, Iran. Various factors such as pH, EC, , , K, P, Fe, Zn, B and Co were measured. Ordinary kriging and co-kriging were assessed to derive maps of soil physico-chemical properties, using mean absolute error (MAE), mean bias error (MBE), root mean squared error (RMSE) and average kriging standard error (AKSE) as statistical criteria. Variography analysis indicated that the ranges of influence for pH, EC, , , K, P, Zn, Fe, B and Co were 65, 55, 78, 79, 75, 60, 50, 65, 70 and 30 km, respectively, and the measuring error varied between 0.366 and 0.843. The results revealed that, based on precision criteria, co-kriging was the best method for interpolating the chemical properties of soil. Finally, using to the co-kriging for each determined variable, a related zoning map for fertility management of the study area was prepared.     

Assessment of soil texture class on agricultural fields using ECa,Amber NDVI,and topographic properties

Soil texture (ST) is relatively stable over time, although it may change due to erosion, clay eluviation, and other processes. Soil texture affects soil quality, productivity and management. Therefore, indirect, accurate methods for assessing of soil texture classes (STCs) are needed in agricultural practice. A study was performed on four production fields in northern and central Poland to compare the fitting performance of STC models based on apparent electrical conductivity (ECa), topographic properties (elevation, slope gradient and wetness index) and Amber NDVI measurements. One common and accurate indicator of STCs was not found for all study fields. On average, ECa was most accurate in indicating areas of different STCs within the fields, but it tended to overestimate the size of sandy areas on loamy fields and vice versa. The accuracy of STC assessment using ECa measurements may be biased due to imperfect soil drainage, high elevations, which increase evaporation and STC variation with depth. STC assessment using Amber NDVI measurements may be useful, particularly on flat and sandy fields, but the results are affected by the same factors as ECa, and additionally by crop growth stages and by the weather conditions in the period preceding the measurements. Despite the good quantitative results of the STC assessment by elevation (one field) and by the topographic wetness index (another field), both terrain attributes failed to accurately indicate the distribution of some STC areas within each field. Therefore, in landscapes developed from deposits of the last glaciation relevant ST differences might not sufficiently be detected by the analysis of terrain attributes alone. The selection of STC predictors and evaluation of the assessment quality must consider both the quantitative indicators such as correlation and determination coefficients describing relationships between ST and ECa, NDVI and topography and percentage of a field area with accurately indicated STC and the distribution of areas with different STCs within a field. The use of ECa, NDVI values, and topographic properties for STC assessment may be useful in reducing costs of soil sampling and analysis, but cannot replace it.     

基于GIS和地统计学的低丘红壤地区三种土壤性质空间变异性研究

以浙江省金华市为例,采用地统计学和GIS相结合的方法,对低丘红壤地区土壤有机质、有效N、有效K3种主要性质的空间变异特性进行研究。研究表明,通过平方根转换后,三者都呈很好的正态分布。有机质的变异函数曲线的理论模型较好地符合球状模型,有效N和有效K则符合指数模型。三者都属于中等空间相关性,同时受到结构性因素和随机性因素的共同影响。通过分析3种土壤性质的各向异性,发现不同的土壤性质有不同的空间分布,同一土壤性质在不同的方向上也有不同的空间变异性。     

Spatial estimation of surface soil texture using remote sensing data

Understanding the spatial distribution and variability of soil texture is essential for land use planning and other activities related to agricultural management and environmental protection. This study was conducted to evaluate Landsat Enhanced Thematic Mapper (ETM) remote sensing data as auxiliary variables for spatial estimation of surface soil texture using a limited number of soil samples taken from a site located in the city of PingduShandong ProvinceChina. Three methods of evaluating variability in surface soil texture were evaluated: (1) multiple stepwise regression (MSR) based on the relationship between surface soil sandsilt and clay contents and remote sensing data; (2) kriging of surface soil sandsilt and clay contents; (3) cokriging with remote sensing data. Correlation analysis showed that surface soil sandsilt and clay contents were significantly correlated with Landsat ETM digital number (DN) of six bands (Bands 1–5 and Band 7)and the DN of Band 7 explained most of the variability in soil sandsilt and clay contents. The DN of Band 7 was selected as auxiliary data for the estimation of surface soil texture. The cross-validation results indicated that both MSR and kriged estimates had low reliability due to the variations in landscape and the low-density sampling in the study area. Cokriging with remote sensing data significantly improves estimates of surface soil texture compared with MSR and kriging.     

结合土地利用的城镇化流域土壤养分非函数空间插值

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.     

缓坡水平梯田土壤水分空间变异性

以江苏省扬州市区北部某梯田为例,分析了小于2°的缓坡水平梯田土壤含水率的空间变异特征,并针对缓坡水平梯田土壤含水率由较高田块到较低田块逐渐增加、同一级梯田内由内侧到外侧含水率逐渐减小的分布特征,提出了适合研究该种地形土壤含水率空间分布的有效方法,即趋势辅助克立格法(简记为KTAI)。该方法同时考虑了不同级梯田高程和同级田块内不同部位对土壤含水率的影响,用它对梯田土壤含水率进行插值,估计方差比传统的普通克立格法(简记为OK)大幅度降低,大大提高了估值精度,减轻了野外采样工作量,对于研究梯田土壤水分空间变异性具有重要意义,同时拓展了地质统计学理论在土壤水分空间变异性研究中的应用范围。     

基于GPS和GIS的田间土壤特性空间变异性的研究

以一块面积约为13.3 hm²的冬小麦田为研究区，利用GPS接收机定位，按50 m×50 m设置网格，共取63个采样点，测定土壤表层(20 cm)内的土壤有机质、全氮、碱解氮、速效磷、速效钾、容重、田间土壤含水率和电导率，研究麦田土壤特性的空间变异规律。采用传统统计学和地统计学相结合的方法对所取的数据进行了分析，利用Arcview3.2软件的空间分析功能，绘制了表达这些土壤特性随机性和结构性的半方差图和空间分布图。研究结果表明：所有土壤特性均服从正态分布；土壤容重具有弱变异强度，其它土壤特性具有中等变异强度；土壤有机质、全氮、碱解氮、速效钾和电导率具有很强的相关性，土壤容重、速效磷和含水率具有中等强度的空间相关性，土壤特性的相关距变化范围为246.8～426.8 m。该成果可为农田的定位施肥、灌溉以及其它的农田精细管理提供依据。     

Statistical and geostatistical techniques for geospatial modeling of soil cation exchange capacity

Soil cation exchange capacity (CEC) is a main criterion of soil quality and pollutant sequestration capacity. This research was carried out to evaluate cokriging prediction map of soil CEC spatial variability with the principal components derived from soil physical and chemical properties. Two hundred and forty-seven soil samples were collected that 75% of them were used for training soil CEC and 25% for testing of prediction. The first principal component (PC1) was highly correlated with soil CEC (r = 0.81, P < 0.01), whiles there was no significant correlation between CEC and PC2 (r = -0.19). Then, the PC1 was used as an auxiliary variable for the prediction of soil CEC in cokriging method. The determination coefficient (R²) of cross-validation for the test dataset was 0.47 for kriging and 0.71 for cokriging. Therefore, according to the results, principal components that have the highest positive and significant correlation with the dependent variable have the most potential for cokriging prediction.     

Comparison of spatial interpolation techniques for mapping soil pH and salinity in agricultural coastal areas,northern Iran

This study attempted to characterize the spatial distributions of soil pH and electrical conductivity (EC_e) of coastal fields in the Miyandoroud region, northern Iran, for three soil layer depths by assessing spatial variability and comparing different interpolation techniques such as inverse distance weighting (IDW), ordinary kriging (OK), and conditional simulations (CS). Three soil composite samples were collected from 0–50, 50–100, and 100–150 cm depths at 105 sampling sites. At all three soil depths, pH and EC_e were best fitted by exponential and spherical models, respectively. Nugget effects were higher for soil EC_e data sets compared with soil pH at all three soil depths showing soil EC_e had a spatial variability in small distances. The prediction accuracy of the interpolation methods indicated that the minimum error for all data sets was achieved with the OK method, except for pH at 50–100 cm depth, and the CS technique revealed the largest error. The effect of different numbers of simulations (100, 500 and 1000) in the CS interpolation method resulted not in a realistic mapping for the soil EC_e and pH. Considering the high importance of irrigated agriculture in the Caspian Sea coastal areas, more subsoil salinity build-up and groundwater salinity monitoring plans are needed as a prerequisite for sustainable agricultural production systems of the future.     

利用遥感和GIS研究兴国县土壤侵蚀的变化

The spatial and temporal dynamics of soil erosion in Xingguo County, Jiangxi Province, China were studied using multi-period remote sensing images and GIS. The results indicated that the soil erosion status of the region has been improving, particularly since the 1980s, with the erosion rate showing an evident decline over the past 30 years. The improvement showed not only in the decline of eroded soil area, but also with the reduction in the extent of erosion. The extent of erosion mainly changed by one level, and the change primarily occurred with the severely or moderately eroded soil types. However, in general, soil erosion was still an overriding problem in the region with some areas becoming more serious, especially those with large quantities of granite.     

模糊逻辑在土壤连续分类和制图表达中的应用及展望

土壤是复杂的历史自然综合体,在属性空间和地理空间上均是连续渐变的,因此基于"双清晰模型"的常规土壤调查制图理论基础明显存在不足,且随着精准农业的快速发展和环境模型模拟精度的提高,客观上对土壤调查输出信息的内容与精度提出了更高的要求。模糊逻辑应用于土壤分类和制图,是继地统计学之后土壤计量学领域又一创新性发展,为国外许多学者所推崇,但是国内在这一领域的研究还较少。因此,本文系统地介绍了模糊逻辑引入土壤分类研究中的必然性和科学意义,以及模糊逻辑在国内外土壤分类和连续制图领域的应用研究进展,并分析了基于模糊逻辑的土壤连续制图表达方法存在的不足及研究展望。     

Sensitivity of simulated hillslope subsurface flow to rainfall patterns,soil texture and land use

Knowledge of the generation of subsurface flow for hillslopes is important for controlling agricultural nonpoint nutrient losses. This study used a physically based hillslope hydrologic model HYDRUS‐2D to assess the sensitivity of simulated subsurface flow to the interactions between precipitation, soil texture and land use. Soil moisture data from 1 January 2013 to 23 August 2014 at two monitoring sites on a tea plantation hillslope were used to calibrate the van Genuchten–Mualem hydraulic parameters for this model. For six different textural classes (loamy sand, loam, silt, silt loam, clay loam and clay) and four land‐use types (tea garden, forest, grassland and bare soil), scenario‐based simulations were carried out for varied precipitation intensities (6.0, 15.0, 30.0, 45.0, 60.0 and 76.0 mm/day) and frequencies (time intervals of 1, 5, 10, 15, 20 and 25 days). Results indicated that the hillslope run‐off was dominated by subsurface flow, which was influenced by precipitation and antecedent moisture conditions. A threshold value of 0.18 m³/m³ of mean hillslope soil moisture was observed for the initiation of subsurface flow. High precipitation intensity (i.e. 75.0 mm/day) substantially increased subsurface flow for all soil textures. In addition, the sensitivity of the bare soil hillslope to rainfall patterns was more than two times higher than that of the vegetated (i.e. grassland, tea garden and forest) hillslope. These findings suggest that extreme precipitation events and land‐use change will increase the risks of subsurface flow on hillslopes. Therefore, optimal fertilizer application strategy and land‐use planning should be proposed for controlling the hillslope nonpoint nutrient losses.     

基于GIS的安塞县土壤水分制图及其数量分析

 以空间图形和数据库为基础,利用GIS安塞县的土壤水分样点数据与地理数据结合起来,建立不同利用类型土地类型坡度分级的浮点型土壤含水率字段,对安塞县域尺度土壤水分制图及其定量化方法进行研究和探讨,对安塞县不同土层土壤水分状态和分布进行定量分析。结果表明:从土壤水分结构看,安塞县土壤水分总体上处于较低水平,0～500 cm土层平均土壤含水量在含水量为8.6%～10.8%的分布面积占到总土地面积的75.34%,在含水量<6.4%和6.4%～8.6%的分布面积占到总土地面积的19.36%,其中含水率<6.4%的土壤干层面积占到总土地面积的9.23%,其在上层分布面积大于下层,分布在一般（≥10.8%）以上水平的面积仅占总土地面积的5.31%。安塞县每2的土壤水库蓄水量在0～120cm土层仅有0.060.07 m3,而其他土层都在0.15m3以下。说明安塞县土壤水分环境极差,土壤水库的调节作用对于林木生长极其有限,大面积植树造林超越了安塞县土壤水库的供水和调水能力,是不适宜的;因此,在以适地适树原则适应土壤水分环境的同时,应加强土壤水分环境的改善。     

雅鲁藏布江中游河岸交错带沙地土壤水分的空间异质性

土壤水分是制约西藏高寒河谷风沙化土地植物群落自然演替和人工促进植被恢复的重要因子之一,准确把握沙地土壤水分的分布状况,对指导正在进行的沙地植被恢复与重建具有重要实践意义。该文采用地统计学与GIS相结合的方法,以雅鲁藏布江中游河谷风沙化土地为对象,研究了河岸交错带沙地土壤水分的空间分布及不同类型沙地和沙丘部位的差异性。结果表明:1)试验地不同深度土壤含水率平均值为6.14%～14.20%,随着湿沙层深度的增加,土壤含水率平均值随之增大。各层土壤含水率均表现为强变异性。2)除0～20cm土壤含水率具有强烈的空间相关性外,其它各层土壤含水率具有中等的空间相关性。随着土层深度的增加,空间相关性减弱。不同深度土壤含水率的空间分布格局存在着较强的相关性,以20～40cm和40～60cm相关性最高。3)流动沙丘迎风坡和河滩地土壤含水率明显高于背风坡、沙砾地、沙丘顶。雅鲁藏布江河水丰枯变化、微地形和风沙运动则是造成不同类型沙地、沙丘部位土壤含水率差异的主要原因。     

基于GIS和RS的土地利用与土壤侵蚀关系研究——以冀北地区为例

 冀北地区土壤侵蚀以水蚀和风蚀为主,根据水利部颁布的《土壤侵蚀分类分级标准》(1997),参考有关学者提出的土壤风蚀强度分级指标,构建研究区土壤侵蚀强度的分级指标。运用GIS和RS技术,提取土地利用、坡度、植被覆盖度、土壤质地等信息,通过GIS空间叠加分析,得出冀北地区土壤侵蚀强度分级图,详细研究土壤侵蚀强度的空间分布特征;进一步与土地利用图叠加,分析不同土壤侵蚀强度下的土地利用特征和不同土地利用类型的土壤侵蚀状况。     

中国禹城土壤盐渍化的时空变异及其预测

This research used both geostatistics and GIS approach to compare temporal change of soil salt between 1980 and 2003, to analyze the spatial distribution of surface soil salt, to developed methods for predicting soil salinization potential based on recent improvements to the Dempster-Shafer theory, and to develop probability maps of potential salinization in Yucheng City, China. A semivariogram model of soil salt content was developed from the spherical model, and then employing kriging interpolation the spatial distribution of salt content in 2003 was obtained utilizing data from 100 soil sampling points. Potential salinization distribution was mapped using an approach that integrated soil data of the second general survey in 1980 in Yucheng City, which included groundwater salinity, groundwater depth, soil texture, soil organic matter content, and geomorphic maps. With the support of Dempster-Shafer theory and fuzzy set technique the factors that affected potential soil salinization were characterized and integrated;and then soil salinization was predicted. Finally a prognosis map of potential salinization distribution in the research area was obtained, with higher probability values indicating higher hazards to salinity processes. The distribution of the potential soil salinization probability was a successive surface.     

Spatial prediction of soil classes using digital terrain analysis and multinomial logistic regression modeling integrated in GIS: Examples from Vestfold County,Norway

The main objectives of this study were to model the relationship between WRB-1998 soil groups and terrain attributes and predict the spatial distribution of the soil groups using digital terrain analysis and multinomial logistic regression integrated in GIS in the Vestfold County of south-eastern Norway. A digital elevation model of 25 meter grid resolution was used to derive fifteen terrain attributes. A digitized soil map of thirteen WRB soil groups at the scale of 1:25,000 was used to obtain the reference soil data for model building and validation. First, the relationships between the soil groups and the terrain attributes were modeled using multinomial logistic regression. Then, the probability that a given soil type is present at a given pixel was determined from the logit models in ARCGIS to continuously map each soil group's spatial distribution. Elevation, flow length, duration of daily direct solar radiation, slope, aspect and topographic wetness index were found to be the most significant terrain attributes correlating with the spatial distribution of the soil groups. The prediction showed higher mean probability values for each soil group in the areas actually covered by that soil group compared to other areas, indicating the reliability of the prediction. However, the prediction performed poorly for soil groups that are not greatly influenced by topography but by other factors such as human activities.