Upgrading a 1/20,000 soil map with an apparent electrical conductivity survey

Despite their shortcomings, choropleth soil maps remain the most widespread source of information on soil resources. Since most nationwide soil surveys were conducted in the second half of the previous century, a need for upgrading emerges. We evaluated the potential of detailed observations made by a mobile, non-invasive proximal soil sensor to upgrade a part of the 1/20,000 choropleth soil map of Belgium. This study was conducted on a 14 ha area which had been mapped twice in the 1950s: first, during the national soil survey yielding a 1/20,000 soil map, and second, during a detailed investigation resulting in a 1/5000 map. The first map failed to identify the presence of a Tertiary clay substratum at variable depths, while the second map indicated this substratum to be present within 1.2 m below the soil surface for about a third of the area. A recent survey with the EM38DD soil sensor provided 9192 measurements of the apparent electrical conductivity (EC_a) within the study area. The depth of the substratum (D_ts) was noted at 60 calibration locations by augering and the relationship between EC_a and D_ts was modelled by an exponential curve with an R² of 0.80. This allowed the detailed mapping of D_ts by regression kriging. The predictions were validated using 46 independent observations of D_ts indicating a reasonable average error of 0.24 m and a very good correlation coefficient between observed and predicted values of 0.94. A map accuracy assessment indicated that even after classification, the D_ts classes were better predicted by the sensor data than the 1/5000 map which was based on many more auger observations. Finally an upgraded 1/20,000 soil map was presented, illustrating the potential of combining existing soil maps with proximal soil sensing technology.     

Relationship between apparent electrical conductivity and soil physical properties in a Malaysian paddy field

Site-specific crop management, well-established in some developed countries, is now being considered in developing countries such as Malaysia. The apparent electrical conductivity (EC_a) of the soil can be used as an indirect indicator of a number of soil physical properties and even crop yield. Commercially available EC_a sensors can efficiently develop the spatially dense data sets desirable in describing within-field spatial soil variability for precision farming. The main purpose of this study was to generate a variability map of soil EC_a within a Malaysian paddy field using a VerisEC sensor. The EC_a values were then compared with some soil variables within classes after delineation. Measured parameters were mapped using the kriging technique and their correlation with soil EC_a was determined. The study showed that the VerisEC can determine soil spatial variability, and can acquire soil EC_a information quickly. Spatial variability of shallow and deep EC_a showed the same patterns. Estimation of soil properties based on EC_a varied from one soil parameter to another and all could be estimated better by deep EC_a. Cross-validation results showed that shallow and deep EC_a, and also bulk density, gave more accurate estimates compared with other variables.     

松嫩平原盐渍化水田土壤表观电导率空间变异研究

运用电磁感应仪EM38结合GPS定位,以盐渍化水田为研究对象,通过经典统计学和地统计学相结合的方法研究了盐渍土区新开水田表观电导率的空间变异特征,分析了土壤表观电导率与土壤盐碱指标关系。结果表明,经典统计分析土壤水平方向表观电导率(ECh)与垂直方向表观电导率(ECv)均为中度空间变异强度,且符合正态分布。地统计分析表明,ECh和ECv均具有强空间相关性,其变异特征主要是由结构性因素引起的,半方差拟合最优模型为指数模型。且ECh和ECv空间分布在一定范围内存在相似性,均表现为不同表观电导率的土壤呈斑块和条带状镶嵌分布。Pearson分析显示,土壤表观电导率与盐碱化指标土壤电导率(EC1︰5)和碱化度(ESP)呈正相关关系(P0.05),相关系数大于0.8。回归分析表明,土壤表观电导率与EC1︰5和ESP均为指数函数关系,决定系数大于0.76。ECh与土壤盐碱化指标相关系数和决定系数均大于ECv,因此可以用水平方向土壤表观电导率ECh来反映土壤的盐碱化程度。该研究可以为土壤盐分空间变异理论与盐碱地改良实践相结合的研究思路提供理论基础,为盐碱地实施定位、定区清除或消减土壤盐碱的均质化改良技术提供数据支持。     

Prediction of topsoil organic matter and clay content from measurements of spectral reflectance and electrical conductivity

Soil organic matter (SOM) and clay content of a soil characterized as a coarse sandy loam were modelled using hyperspectral reflectance data acquired with a spectrometer and soil electrical conductivity (SEC) data acquired with an EM38 instrument manufactured by Geonics Ltd. The partial least squares (PLS) regression method was applied and the results validated using cross validation. First, the models were calibrated using only spectral reflectance data; then EM38 data were included in the X-matrix of predictors. Although SEC is significantly correlated with clay content, the results showed that EM38 data did not improve model performance for the estimation of soil organic matter content and clay content, despite the fact that EM38 showed significant correlation with clay content.     

Analysis of soil fertility and its anomalies using an objective model

In this work, the use of an objective method, the formulation of the Rasch measurement model, which synthesizes data with different units into a uniform analytical framework, is considered to get representative measures of soil fertility potential in an experimental field. Thus, two types of information about the soil were obtained from soil samples taken at 70 locations: first, the textural components were determined, and, secondly, deep (ECa‐90) and shallow (ECa‐30) soil apparent electrical conductivity, approximately 0–90 and 0–30 cm depths, respectively, were measured. A latent variable, denominated soil fertility potential, was defined. It is supposed, and later it is verified, that all soil properties previously indicated have a marked influence on the latent variable. The adequate assignment of categorical values across properties measures and the good fit of the data are checked as a previous phase to properly compute the Rasch measures. After applying the Rasch methodology, it was obtained that both electrical conductivities are the most influential properties on soil fertility potential, getting moreover a ranking of all soil samples according to their fertility potential and the unexpected behaviors, called misfits, of some soil samples, which constitute a very useful information to better match soil and crop requirements as they vary in the field, being a rational basis for a site‐specific crop management.     

基于VRML的土壤电导率三维空间变异性虚拟实现建模研究

如何表达土壤属性的三维空间变异性对传统的土壤剖面采样、空间变异分析和三维可视化表达提出了挑战。本研究以浙江省围垦海涂水稻田土壤盐分为例,采用EM38电磁感应线性模型结合二阶Tikhonov正则化方法反演剖面0~110 cm范围内10个土层深度的土壤电导率作为三维空间变异性研究的数据源;然后利用三维反距离权重方法进行土壤盐分的三维空间插值;最后分别采用虚拟现实建模语言（VRML）的球体、切片、地柱模型对土壤电导率剖面离散点、二维空间变异切片、三维变异土体模型进行三维虚拟现实可视化建模,并实现模型的网络发布。结果表明,在田间尺度上,三维反距离权重方法可较好的对土壤电导率在三维空间的分布进行预测插值;通过VRML方法进行可视化建模可较好的展示及解析土壤电导率的三维空间分布规律;水平方向上,土壤盐分从西北面向东南面逐渐增大,垂直方向上,土壤盐分随土层深度的增加而增大,且东南角土壤盐分最大;另外,用户可利用IE浏览器实现虚拟现实模型的可视化及对模型进行平移、放大、缩小、旋转等基本操作。基于VRML的虚拟建模方法可为土壤属性的三维变异性可视化及网络共享提供新途径。     

黄河三角洲盐碱地混交林土壤电导率的空间异质性

为了研究黄河三角洲盐碱地混交林土壤盐分在旱季和雨季的空间分布特征,以黄河三角洲新造的由11个树种组成的混交林为研究对象,利用经典统计学和地统计学相结合的方法,绘制上层（0 ～ 15 cm）和下层（15～30 cm）土壤电导率的随机性和结构性的半方差图以及空间分布图,研究春季和秋季不同深度土壤电导率的空间变异特征.结果表明：1）土壤电导率平均值在春季和秋季有显著差异,不论是上层还是下层,春季均大于秋季.春季上层和下层土壤电导率变化范围分别为1.12 ～5.18和0.83～3.72 mS/cm,变度分别为4.06和2.89 mS/cm.秋季上层和下层土壤电导率变化范围分别为0.85 ～2.91和0.62～3.05 mS/cm,变度分别为2.06和2.43 mS/cm.春季土壤电导率变异系数较大,而秋季则较小.2）春季混交林上层和下层土壤电导率均有强烈的自相关性,变程分别为13.47和12.72 m.秋季上层和下层土壤电导率变异函数的半方差图几乎为直线,变程分别为213和71m,超出了取样范围,表明在该取样尺度下没有自相关性.3）Kriging插值结果表明,混交林地春季土壤电导率斑块化程度高于秋季.     

Field level digital mapping of soil mineralogy using proximal and remote‐sensed data

Primary (e.g., quartz) and secondary (clay) minerals are key factors determining the physical and chemical characteristics of soil. Understanding spatial distribution of minerals at the field scale would, therefore, be of potential benefit for soil management. However, current analysis requires time‐consuming laboratory procedures and computational quantification analysis (e.g., SIROQUANT). Furthermore, mineral composition (e.g., quartz, kaolinite, illite and expandable clay minerals) must sum to 100. We aimed to add value to laboratory data by developing multiple linear regression (MLR) relationships between mineralogy and ancillary data such as digital numbers (DNs) (i.e., Red [R], Green [G] and Blue [B]) acquired from remotely sensed air‐photographs and soil apparent electrical conductivity (EC_a – mS/m) measured from proximal sensing electromagnetic (EM) instruments (i.e., EM38 and EM31). To account for composition, we compare results from the MLR approach with those from additive log‐ratio (ALR) transformation of mineralogy prior to MLR modelling. This approach together with various ancillary data and trend surface parameters (i.e., scaled Easting and Northing) has greater precision and less bias of prediction than the MLR approach using untransformed data. Our approach also enables predictions to sum to 100. We conclude that the most useful ancillary data to predict the abundance of quartz, kaolinite and illite are B DNs and EM31, while expandable clays are best predicted with R DNs, EM38 and scaled Northing. The use of ancillary data to map mineralogical components combined with ALR‐MLR is an effective approach, with resulting maps providing insights into soil and water management issues consistent with farmer experience.     

Spatial Distribution and Management Zones for Sulphur and Micronutrients in Shiwalik Himalayan Region of India

Agricultural land degradation due to nutrient deficiencies is a threat to agricultural sustainability. As nutrients availability is influenced by soil heterogeneity, climatic conditions and anthropogenic activities; hence, delineation of nutrient management zones (MZs) based on spatial variability could be an effective management option at regional scale. Thus, the present study was carried out to delineate MZs in the Shiwalik Himalayan region of India by capturing spatial variability of soil properties and secondary and micronutrients status because of the emerging nutrient deficiencies. For the study, a total of 2575 geo‐referenced representative surface (0–15 cm depth) soil samples were collected from the study region covering an area of 53,483 km². The soils were analysed for pH, electrical conductivity, soil organic carbon, available sulphur (S) and micronutrients (Zn, Fe, Cu, Mn, B and Mo) concentrations. There was a wide variation in soil properties with coefficient of variation values of 14 (for pH) to 86% for available Mo. Geostatistical analysis revealed spherical, Gaussian, exponential, stable, circular and K‐Bessel best‐fit models for soil properties. Most of the soil properties were having moderate spatial dependence except soil pH and S (strong spatial dependence) and Zn (weak spatial dependence). About 49%, 10%, 2%, 13%, 11%, 12% and 8% area of the study region were found to be deficient (including acute and marginal deficiency) in S, Zn, Fe, Cu, Mn, B and Mo, respectively. The principal component analysis and fuzzy c‐mean clustering were performed to develop the MZs. Four principal components with eigenvalues greater than 1 and accounting 65·4% of total variance were retained for further analysis. On the basis of fuzzy performance index and normalized classification entropy, four potential MZs were identified. Analysis of variance confirmed the heterogeneity in most of the studied soil properties among the MZs. The study indicated that the methodology of delineating MZs can be effectively used in site‐specific S and micronutrients management in the Shiwalik Himalayan region of India. Copyright © 2016 John Wiley & Sons, Ltd.     

基于Android的车载式土壤电导率和光谱反射率检测系统

This paper aims to develop a vehicular integrated system collecting soil electrical conductivity(EC) and spectral reflectance.The system could collect soil EC and spectral reflectance automatically when the vehicle moves and save these measurement results with GPS.The information could reflect the characteristics of soil parameters such as soil salinity and water content.Hence, an Android-based vehicular detection system for soil conductivity and spectral reflectance information was developed.Soil electrical conductivity measurement was performed based on improved "current-voltage" four-terminal method.A STS-NIR spectrometer was used for collecting near-infrared spectral reflectance.The system collected the information of soil electrical conductivity and soil spectral reflectance while collecting GPS, which could be used for precision agriculture.The system was tested in a farm of Beijing on March 25, 2014.Soil electrical conductivity of the farmland was measured and soil samples were collected.Water content and soil electrical conductivity were measured in the laboratory.The result of these experiments showed that the system could work stably in farmland and had good prospects.     

Relating soil electrical conductivity to remote sensing and other soil properties for assessing soil salinity in northeast Thailand

Mapping and monitoring of soil salinity is required to establish its areal extent and also to keep track of changes in salinity in order to formulate appropriate and timely management strategies for reclamation and rehabilitation of such soils. Remote sensing data have been increasingly used in soil‐salinity studies as they are not only quicker but are also useful for making realistic predictions. A study was conducted in northeast Thailand to understand the relationship of spectral reflectance and physico‐chemical soil properties to electrical conductivity (EC) by using remote sensing data (Landsat® ETM+) and laboratory analysis of soil sample data. Multiple regression analysis was used to examine the relationships between EC and spectral/soil properties and to generate several models. In the case of spectral properties, mid‐infrared band (Landsat® band 7) and near‐infrared (band 4) were found to be most correlated with the observed EC values of the surface layer of the soil. For the soil properties, chloride (Cl), sodium (Na), phosphorus (P), and sodium adsorption ratio (SAR) were found to be most correlated with observed EC values. Similarly, for the subsurface layer, Cl and P were found to be significant predictors of observed EC values. Copyright © 2006 John Wiley & Sons, Ltd.     

基于遥感与电磁感应仪数据的土壤盐分空间变异性

针对目前黄淮海平原存在的土壤盐渍化问题,以河南省封丘县为研究区,利用遥感影像与磁感应电导率仪 （EM38）在田间定位、定点测量,对研究区土壤盐分的空间变异性进行研究。利用Kriging、指数回归和回归-Kriging 3种不同的方法对土壤盐分空间变异性进行分析。结果表明：土壤电导率在一定的区域范围内具有空间结构特征,符合指数模型分布,具有极强的空间自相关性。通过3种不同的方法对土壤盐分的空间变异性进行分析,结果表明Kriging法、指数回归法及回归-Kriging法在描述土壤盐分空间分布的总体趋势上相似,但指数回归法与回归-Kriging法对于盐分的分布表达更具体,更细致。综合运用多种数据源的方法,即遥感影像数据与EM38测量数据相结合,经典统计学与地统计学方法相结合的方法,能够提高土壤盐分空间预测的精度,结果表明研究区土壤盐分由北向南、自西向东有逐渐升高的趋势,具有明显的趋势效应。     

Measurement of soil water and nutrients by its electrical properties

Abstract

An instrument for measurement of soil dielectric constant ε _r , electrical conductivity EC _a, and soil temperature was tested on soils under potato crop to investigate contents of soil volumetric water θ and nutrients for eventual use in a field crop model.

To approximate the dependence of θ on ε _r , a logarithmic equation was chosen. Satisfactory results were obtained on stone-free areas, with the mean relative variance between θ-values determined by dielectric constant and converted from a gravimetric method remaining within the limits of measuring error. However, variances were higher for stony soils, with ε _r -values at the same θ being considerably higher. To reconcile data from stony and stone-free soils, a formula was composed.

Salinity, calculated by a semi-empirical model based on Hilhorst's theory using measured values of EC _a, ε _r , and soil temperature, correlated well with contents of K and Mg in the soil. A lower correlation resulted for P, and was practically absent for Ca. Inequality of these regression equations at different measuring sites demonstrates the necessity of considering soil pH when assessing plant nutrients in the soil.     

Properties of soils from the Swedish long-term fertility experiments: VI. Mapping soil electrical conductivity with different geophysical methods

Abstract

Swedish long-term soil fertility experiments were used to investigate the effect of texture and fertilization regime on soil electrical conductivity. In one geophysical approach, fields were mapped to characterize the horizontal variability in apparent electrical conductivity down to 1.5 m soil depth using an electromagnetic induction meter (EM38 device). The data obtained were geo-referenced by dGPS. The other approach consisted of measuring the vertical variability in electrical conductivity along transects using a multi-electrode apparatus for electrical resistivity tomography (GeoTom RES/IP device) down to 2 m depth. Geophysical field work was complemented by soil analyses. The results showed that despite 40 years of different fertilization regimes, treatments had no significant effects on the apparent electrical conductivity. Instead, the comparison of sites revealed high and low conductivity soils, with gradual differences explained by soil texture. A significant, linear relationship found between apparent electrical conductivity and soil clay content explained 80% of the variability measured. In terms of soil depth, both low and high electrical conductivity values were measured. Abrupt changes in electrical conductivity within a field revealed the presence of ‘deviating areas’. Higher values corresponded well with layers with a high clay content, while local inclusions of coarse-textured materials caused a high variability in conductivity in some fields. The geophysical methods tested provided useful information on the variability in soil texture at the experimental sites. The use of spatial EC variability as a co-variable in statistical analysis could be a complementary tool in the evaluation of experimental results.     

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。     

Evaluation of a local regression kriging approach for mapping apparent electrical conductivity of soil (ECa) at high resolution

Apparent electrical conductivity of soil (ECa) is a property frequently used as a diagnostic tool in precision agriculture, and is measured using vehicle‐mounted proximal sensors. Crop‐yield data, which is measured by harvester‐mounted sensors, is usually collected at a higher spatial density compared to ECa. ECa and crop‐yield maps frequently exhibit similar spatial patterns because ECa is primarily controlled by the soil clay content and the interrelated soil moisture content, which are often significant contributors to crop‐yield potential. By quantifying the spatial relationship between soil ECa and crop yield, it is possible to estimate the value of ECa at the spatial resolution of the crop‐yield data. This is achieved through the use of a local regression kriging approach which uses the higher‐resolution crop‐yield data as a covariate to predict ECa at a higher spatial resolution than would be prudent with the original ECa data alone. The accuracy of the local regression kriging (LRK) method is evaluated against local kriging (LK) and local regression (LR) to predict ECa. The results indicate that the performance of LRK is dependent on the performance of the inherent local regression. Over a range of ECa transect survey densities, LRK provides greater accuracy than LK and LR, except at very low density. Maps of the regression coefficients demonstrated that the relationship between ECa and crop yield varies from year to year, and across a field. The application of LRK to commercial scale ECa survey data, using crop yield as a covariate, should improve the accuracy of the resultant maps. This has implications for employing the maps in crop‐management decisions and building more robust calibrations between field‐gathered soil ECa and primary soil properties such as clay content.     

Spatial pattern consistency and repeatability of proximal soil sensor data for digital soil mapping

Data from proximal soil sensors can facilitate digital soil mapping at high spatial resolutions. However, their use for predicting static soil properties, such as texture, is affected by spatio-temporal changes in environmental and measurement conditions. In this research study, seasonal changes in spatial patterns and repeatability of data provided by a platform that simultaneously measures the red (Red) and near infrared (NIR) reflectance, apparent soil electrical conductivity (ECa), temperature, and volumetric moisture content of topsoil (at 3–6 cm depth) were assessed. Test fields are located in Southern Finland with textures dominated by clay and fine sandy till. During single scans, mean relative differences between the data from duplicated measurement points ranged from ~4% to 6% and were the highest for temperature and Red values. The consistency of spatial patterns across seasons (spring and autumn 2021 and 2022) was the highest for ECa values, and the lowest for NIR. ECa and moisture were significant for predicting the clay contents at a cereal grain crop site, whereas temperature was significant at grass ley sites. Errors were generally lower when using spring data compared with autumn data (RMSE ranging from 4.8% to 11.1% for the data from different fields and measurement dates). For the fields, where static soil properties change at small spatial scales, spatially detailed moisture and temperature data support the understanding of seasonal changes in the spatial patterns derived from multi-sensor data, and the corresponding changes in the performance of calibration models.     

土壤质地三角图的规范制作及自动查询

通过分析国内涉及土壤质地三角图的主要参考资料及存在的问题,本文提出了制作规范的土壤质地三角图的基本要求,并新绘制了中文版美国农部制土壤质地三角图。针对人工方式查询土壤质地容易出错以及大批量样品查询效率较低等问题,建立了一种在Excel中通过自定义函数实现土壤质地快速自动的查询方法。