Using scattered hyperspectral imagery data to map the soil properties of a region

Airborne hyperspectral imagery has been recently proved to be a successful technique for predicting soil properties of the bare soil surfaces that are usually scattered in the landscape. This new soil covariate could much improve the digital soil mapping (DSM) of soil properties over larger areas. To illustrate this, we experimented with digital soil mapping in a 24.6‐km² area located in the vineyard plain of Languedoc. As input data, we used 200 points with clay content measurements and 192 bare soil fields representing 3.5% of the total area in which the clay contents of the soil surface were successfully mapped at 5‐m resolution by hyperspectral remote sensing. The clay contents were estimated from CR₂₂₀₆, a spectrometric indicator that quantifies specific absorption features of clay at 2206 nm. We demonstrated by cross‐validation that the co‐kriging procedure based on our co‐regionalization model provided accurate error estimates at the clay measurement sites. Then, we applied a block co‐kriging model to map the mean clay content at increasing resolutions (50 , 100, 250 and 500 m). The results showed the following: (i) using hyperspectral data significantly increased the accuracy of the mean clay content estimations; (ii) a block co‐kriging procedure with reliable estimates of error variance can be used to estimate mean clay contents over larger areas and at coarser resolutions with acceptable and predictable errors and (iii) various maps can be produced that represent different compromises between prediction accuracy and spatial resolution.     

Mapping soil salinity in 3‐dimensions using an EM38 and EM4Soil inversion modelling at the reconnaissance scale in central Morocco

Large areas of Morocco require irrigation and although good quality water is available in dams, farmers augment river water with poorer quality ground water, resulting in salt build‐up without a sufficient leaching fraction. Implementation of management plans requires baseline reconnaissance maps of salinity. We developed a method to map the distribution of salinity profiles by establishing a linear regression (LR) between calculated true electrical conductivity (σ, mS/m) and electrical conductivity of the saturated soil‐paste extract (ECe, dS/m). Estimates of σ were obtained by inverting the apparent electrical conductivity (ECa, mS/m) collected from a 500‐m grid survey using an EM38. Spherical variograms were developed to interpolate ECa data onto a 100 m grid using residual maximum likelihood. Inversion was carried out on kriged ECa data using a quasi‐3d model (EM4Soil software), selecting the cumulative function (CF) forward modelling and S2 inversion algorithm with a damping factor of 3.0. Using a ‘leave‐one‐out cross‐validation' (LOOCV), of one in 12 of the calibration sites, the use of the q‐3d model yielded a high accuracy (RMSE = 0.42 dS/m), small bias (ME = ?0.02 dS/m) and Lin's concordance (0.91). Slightly worse results were obtained using individual LR established at each depth increment overall (i.e. RMSE = 0.45 dS/m; ME = 0.00 dS/m; Lin's = 0.89) with the raw EM38 ECa. Inversion required a single LR (ECe = 0.679 + 0.041 × σ), enabling efficiencies in estimating ECe at any depth across the irrigation district. Final maps of ECe, along with information on water used for irrigation (ECw) and the characterization of properties of the two main soil types, enabled better understanding of causes of secondary soil salinity. The approach can be applied to problematic saline areas with saline water tables.     

土壤水分和养分时空变异性与作物产量的关系

精确农业使得农田土壤水分和养分的时空变异与产量关系的研究显得尤为重要,这就要求在其研究方法上取得新的进展,以加快精确农业的进程。该文运用传统统计理论和地质统计学理论结合分析了冬小麦苗期、灌浆期的土壤水分和养分变异与产量变异之间的关系。提出用“综合指示率”综合评价土壤的水分和养分状况,并分析了综合指示率的变异与作物产量变异之间的关系。结果表明在67.8 m范围内综合指示率与产量之间存在很强的空间相关性,综合指示率越大,产量越高。Kriging估计的空间分布图也表明综合指示率与产量有较好的对应关系,综合指示率能解释产量变异的58.4%,比多元回归结果提高17.4%。多元逐步回归分析和综合指示率与产量的两变量回归分析结果都表明,同时考虑土壤水分和养分的变异能解释产量变异的33.3%～58.4%,比只考虑土壤养分变异时提高0～5.2%,同时考虑苗期和灌浆期土壤水分和养分变异能解释产量变异的41.0%～58.4%,比只考虑其中1个时期提高4.3%～16.5%。综合指示率为表达农田土壤水分和养分时空变异性及其与作物产量的关系提供了一种思路和方法,其分布图不仅是管理者综合评价土壤水分和养分状况的有利工具,而且把它与产量分布图对比,可以为精确农业的实施提供一定参考。     

Prediction of spatial soil property information from ancillary sensor data using ordinary linear regression: Model derivations, residual assumptions and model validation tests

Geospatial measurements of ancillary sensor data, such as bulk soil electrical conductivity or remotely sensed imagery data, are commonly used to characterize spatial variation in soil or crop properties. Geostatistical techniques like kriging with external drift or regression kriging are often used to calibrate geospatial sensor data to specific soil or crop properties. More traditional statistical methods such as ordinary linear regression models are also commonly used. Unfortunately, some soil scientists see these as competing and unrelated modeling approaches and are unaware of their relationship. In this article we review the connection between the ordinary linear regression model and the more comprehensive geostatistical mixed linear model and describe when and under what conditions ordinary linear regression models represent valid spatial prediction models. The formulas for the ordinary linear regression model parameter estimates and best linear unbiased predictions are derived from the geostatistical mixed linear model under two different residual error assumptions; i.e., strictly uncorrelated (SU) residuals and effectively uncorrelated (EU) residuals. The theoretically optimal (best linear unbiased) and computable (linear unbiased) predictions and variance estimates derived under the EU error assumption are examined in detail. Statistical tests for detecting spatial correlation in LR model residuals are also reviewed, in addition to three LR model validation tests derived from classical linear modeling theory. Two case studies are presented that highlight and demonstrate the various parameter estimation, response variable prediction and model validation techniques discussed in this article.     

土地混合使用制度下土壤硝态氮分布的地理空间制图研究

Mapping the spatial distribution of soil nitrate-nitrogen (NO₃-N) is important to guide nitrogen application as well as to assess environmental risk of NO₃-N leaching into the groundwater. We employed univariate and hybrid geostatistical methods to map the spatial distribution of soil NO₃-N across a landscape in northeast Florida. Soil samples were collected from four depth increments (0-30, 30-60, 60-120 and 120-180 cm) from 147 sampling locations identified using a stratified random and nested sampling design based on soil, land use and elevation strata. Soil NO₃-N distributions in the top two layers were spatially autocorrelated and mapped using lognormal kriging. Environmental correlation models for NO₃-N prediction were derived using linear and non-linear regression methods, and employed to develop NO₃-N trend maps. Land use and its related variables derived from satellite imagery were identified as important variables to predict NO₃-N using environmental correlation models. While lognormal kriging produced smoothly varying maps, trend maps derived from environmental correlation models generated spatially heterogeneous maps. Trend maps were combined with ordinary kriging predictions of trend model residuals to develop regression kriging prediction maps, which gave the best NO₃-N predictions. As land use and remotely sensed data are readily available and have much finer spatial resolution compared to field sampled soils, our findings suggested the effcacy of environmental correlation models based on land use and remotely sensed data for landscape scale mapping of soil NO₃-N. The methodologies implemented are transferable for mapping of soil NO₃-N in other landscapes.     

Use of an electromagnetic technique to determine sodicity in saline-sodic soils

Soil sodicity is an increasing problem in arid‐land irrigated soils that decreases soil permeability and crop production and increases soil erosion. The first step towards the control of sodic soils is the accurate diagnosis of the severity and spatial extent of the problem. Rapid identification and large‐scale mapping of sodium‐affected land will help to improve sodicity management. We evaluated the effectiveness of electromagnetic induction (EM) measurements in identifying, characterizing and mapping the spatial variability of sodicity in five saline‐sodic agricultural fields in Navarre (Spain). Each field was sampled at three 30‐cm soil depth increments at 10–30 sites for a total of 267 soil samples. The number of Geonics‐EM38 measurements in each field varied between 161 and 558, for a total of 1258 ECa (apparent electrical conductivity) readings. Multiple linear regression models established for each field predicted the average profile ECe (electrical conductivity of the saturation extract) and SAR (sodium adsorption ratio of the saturation extract) from ECa. Despite the lack of a direct causal relationship between ECa and SAR, EM measurements can be satisfactorily used for characterizing the spatial distribution of soil sodicity if ECe and SAR are significantly auto‐correlated. These results provide ancillary support for using EM measurements to indirectly characterize the spatial distribution of saline‐sodic soils. More research is needed to elucidate the usefulness of EM measurements in identifying soil sodicity in a wider range of salt and/or sodium‐affected soils.     

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.     

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.     

Neural network ensemble residual kriging application for spatial variability of soil properties

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

Prediction of soil salinity in the Yellow River Delta using geographically weighted regression

It is essential to determine the content and spatial distribution of soil salinity in a timely manner because soil salinization can cause land degradation on a regional scale. Geographically weighted regression (GWR) is a local regression method that can achieve the spatial extension of dependent variables based on the relationships between the dependent variables and environment variables and the spatial distances between the sample points and predicted locations. This study aimed to explore the feasibility of GWR in predicting soil salinity because the existing interpolation methods for soil salinity in the Yellow River Delta are still of low precision. Additionally, multiple linear regressions, cokriging and regression kriging were added to compare the accuracy of GWRs. The results showed that GWR predicted soil salinity with high accuracy. Furthermore, the accuracy was improved when compared to other methods. The root mean square error, correlation coefficient, regression coefficient and adjustment coefficients between the observed values and predicted values of the validation points were 0.31, 0.65, 0.57 and 0.42, respectively, which were better than that of other methods, indicating that GWR is an optimal method.     

基于电磁感应的典型干旱区土壤盐分空间异质性

为研究干旱区土壤盐分空间异质性,指导农业生产实践,运用大地电导率仪（EM38、EM31）对研究区域进行移动式磁感调查,获取表观电导率（ECa）。同时,通过27个校准点的采样和ECa测量,建立土壤盐分的电磁感应解译模型。干旱区土壤盐分质量分数与EM38、EM31水平模式读数（H38、H31）显示出良好的相关性（R=0.935）,可以利用ECa结合GIS和地统计学知识研究土壤盐分的空间分布。采用两种方法进行研究：一种是先利用解译模型获取磁感调查点的土壤盐分质量分数,然后进行地统计分析研究其空间分布;另一种是先利用地统计分析研究H38和H31的空间分布,然后利用解译模型通过栅格运算计算盐分质量分数,精度检验显示前者预测值与实测值之间的相关性更好（R2, 0.888＞0.873）;标准差较低（std. 0.414＜0.426）,具有更高的预测精度。研究结果表明,基于电磁感应研究干旱区土壤盐分空间异质性是切实可行的,这对于土壤盐渍化的快速诊断,指导农业生产和促进精准农业的发展具有重要的意义。     

A coherent geostatistical approach for combining choropleth map and field data in the spatial interpolation of soil properties

Information available for mapping continuous soil attributes often includes point field data and choropleth maps (e.g. soil or geological maps) that model the spatial distribution of soil attributes as the juxtaposition of polygons (areas) with constant values. This paper presents two approaches to incorporate both point and areal data in the spatial interpolation of continuous soil attributes. In the first instance, area-to-point kriging is used to map the variability within soil units while ensuring the coherence of the prediction so that the average of disaggregated estimates is equal to the original areal datum. The resulting estimates are then used as local means in residual kriging. The second approach proceeds in one step and capitalizes on: 1) a general formulation of kriging that allows the combination of both point and areal data through the use of area-to-area, area-to-point, and point-to-point covariances in the kriging system, 2) the availability of GIS to discretize polygons of irregular shape and size, and 3) knowledge of the point-support variogram model that can be inferred directly from point measurements, thereby eliminating the need for deconvolution procedures. The two approaches are illustrated using the geological map and heavy metal concentrations recorded in the topsoil of the Swiss Jura. Sensitivity analysis indicates that the new procedures improve prediction over ordinary kriging and traditional residual kriging based on the assumption that the local mean is constant within each mapping unit.     

Determination of Soil Type Boundaries using Electromagnetic Induction Scanning Techniques

Information on the spatial distribution of soil texture and soil water is vital in understanding crop yield variation. Such information allows improved management of all agricultural inputs. One of the limiting factors in the mapping of soil texture information, however, is cost.Confusion matrix analysis was used to determine whether bulk apparent electrical conductivity (ECa) data derived from electro-magnetic induction (EMI) scanning at field capacity, and clustered using a k-means algorithm, accurately delineates soil textural boundaries in a field containing clay loam and sandy loam soils.The ECa map was compared to two soil surveys, the first conducted at one sample per hectare and the second at four to eight samples per hectare. Using confusion matrix analysis a significantly stronger relationship was measured between the ECa map and soil units of the more intensive soil map, than with the lower sampling density soil survey. This relationship was between two classes of soil with a difference in clay content of 12% and two clusters with a difference in mean ECa of 16·9 mS m⁻¹.     

Comparison of Geonics EM38 and Dualem 1S electromagnetic induction sensors for the measurement of salinity and other soil properties

The electromagnetic induction (EMI) Geonics EM38 (G‐EM38) and Dualem 1S (D‐1S) sensors are used frequently for assessment of soil salinity and other soil characteristics in irrigated agriculture. We compared these two sensors to determine whether they could be used interchangeably for the measurement of apparent soil electrical conductivity (ECa) in horizontal (ECa‐h) and vertical (ECa‐v) coil receiver modes. Readings were taken at 201 locations identified in three irrigation districts in both modes, and statistical comparisons were made on the raw data and from maps of a 2‐ha irrigated field made using 1680 horizontal mode readings. Both sensors gave the same ECa‐v readings (mean G‐EM38 and D‐1S difference = 0), whereas the ECa‐h readings were slightly greater with the Geonics EM38 than with the Dualem D‐1S (mean difference = 0.075 and 0.05 dS/m for the 201 and 1680 observations, respectively). The degree of coincidence between both sensors for soil profile ECa classification was acceptable: 82% for normal profiles (i.e. ECa‐h/ECa‐v < 0.9) and 90% for inverted profiles (i.e. ECa‐h/ECa‐v > 1.1). In practical terms, Geonics EM38 and Dualem 1S sensors could be used interchangeably with similar or very close results.     

基于多源数据的中原黄泛区土壤盐分空间变异分析

为研究中原黄泛区土壤盐分空间变异,以河南省封丘县为研究区,综合考虑引起土壤盐渍化的土壤盐分、地形、地下水位及矿化度、植被情况及其他影响因素,基于遥感影像和磁感式探测获得的土壤表观电导率等多源数据建立了区域土壤盐分综合评估模型,并对研究区分层土壤盐分空间变异进行评估。结果表明:对于0~60 cm土层利用多源数据进行模型构建中土壤表观电导率与光谱指数占主要比例,模型对于各层土壤盐分的评价精度0~60 cm土层优于≥60~120 cm土层。土壤盐分含量随着深度的增加而增大,变异系数在0.22~0.28之间,属中等变异强度。土壤盐分主要集中分布在研究区北部与东南部,尤其是东南角黄河沿线区域,且随着土壤剖面显示出从表现到深层逐渐增加的趋势。利用多源数据建立的分层土壤盐分综合评估模型对于区域土壤盐分解析具有较高精度。该研究为中原黄泛区土壤盐化消减与土壤质量提升提供了可靠新方法。     

The apparent electrical conductivity as a surrogate variable for predicting earthworm abundances in tilled soils

Noninvasive geophysical methods have a great potential for improving soil‐biological studies at field or regional scales: they enable the rapid acquisition of soil information which may help to identify potential habitats for soil biota. A precondition for this application is the existence of close relationships between geophysical measurements and the soil organism of interest. This study was conducted to determine whether field measurements of apparent electrical conductivity (ECa) are related to abundances of earthworms in tilled soils. Relationships between ECa and earthworm populations were investigated along transects at 42 plots under reduced and conventional tillage at a 74 ha field on sandy‐loam soil in NE Germany. Relations were analyzed with linear‐regression and spatial analysis. The apparent electrical conductivity (ECa) was quantitatively related to earthworm abundances sampled 5 months after the geophysical measurements. No relationship was found, however, in plots under conventional tillage when analyzed separately. If earthworm abundances were known at every other location along the transects and if the state‐space approach was used for analysis, the analysis of ECa measurements and earthworm abundances indicated that 50% of the earthworm samples could have been substituted by ECa measurements. Further research is needed to fully evaluate the potential of ECa measurements for predicting earthworm habitats in tilled soil.     

河北省表层土壤有机碳和全氮空间变异特征性及影响因子分析

【目的】在陆地生态系统中, 土壤全氮和有机碳是重要的生态因子。本研究基于土壤调查获得大量土壤剖面的空间和属性信息,研究河北的土壤有机碳和全氮的空间分布特征,为河北的土壤养分监测和管理提供科学依据,同时也为其他类似地区土壤采样提供参考,减少采样成本。【方法】运用传统统计学和地统计学分析方法,以变异函数为工具,初步分析了河北土壤全氮和有机碳的空间变异特征,并应用普通克立格法和回归克里格法进行插值, 得出全氮和有机碳含量的分布格局。【结果】研究区土壤有机碳和全氮的平均值分别为15.25 g/kg和1.23 g/kg,变异系数分别为0.73和0.63,属于中等强度变异。经对数转换后,土壤有机碳和全氮均符合正态分布。选择球状模型作为土壤有机碳和全氮的半方差函数理论模型,土壤有机碳和全氮的块金值/基台值的比值分别为1.8%和1.2%,有机碳和全氮的块金系数均小于25%,表明有机碳和全氮具有强烈的空间相关性。有机碳和全氮空间变异的尺度范围不同,分别为50.400 km和59.200 km。研究区的有机碳总体空间分布规律是有机碳在北部较高、南部较低,呈自北向南递减趋势,土壤全氮与有机碳的空间分布趋势相似,但有机碳的空间变异特征较全氮明显,这种空间分布格局主要受环境因子、 土壤质地、 土壤类型以及土地利用类型等的影响,其中环境因子中的气温和海拔对有机碳和全氮的影响较大。通过比较普通克里格和回归克里格的预测结果,回归克里格能较好地反映东南部有机碳和全氮较低地区的局部变异外,对于西北部的山区也能更好地反映碳、 氮与地形及气候等因素的关系。【结论】河北土壤有机碳和全氮的空间变异和分布特征较为类似,受地形地貌、 气候等因素的影响。通过比较普通克里格法和回归克里格法的空间预测结果,回归克里格法可以消除环境因子的影响,从而得到更准确的空间预测结果,因此建议使用回归克里格法进行预测,以期获得一个更为准确的土壤有机碳和全氮的空间预测结果。     

Mapping soil pH levels across Europe: An analysis of LUCAS topsoil data using random forest kriging (RFK)

Soil pH affects food production, pollution control and ecosystem services. Mapping soil pH levels, therefore, provides policymakers with crucial information for developing sustainable soil use and management policies. In this study, we used the LUCAS 2015 TOPSOIL data to map soil pH at a European level. We used random forest kriging (RFK) to build a predictive model of spatial variability of soil pH, as well as random forest (RF) without co-kriging and boosted regression trees (BRT) modelling techniques. Model accuracy was evaluated using a ten-fold cross-validation procedure. While we found that all methods accurately predicted soil pH, the accuracy of the RFK method was best with regression performance metrics of: R² = 0.81 for pH (H₂O) and pH (CaCl₂); RMSE = 0.59 for pH (H₂O) and RMSE = 0.61 in pH (CaCl₂); MAE = 0.41 for pH (H₂O) and MAE = 0.43 in pH (CaCl₂). Dominant explanatory variables in the RF and BRT modelling were topography and remote sensing variables, respectively. The generated maps broadly depicted similar spatial patterns of soil pH, with an increasing gradient of soil pH from north to south Europe, with the highest values mainly concentrated along the Mediterranean coast. The mapping could provide spatial reference for soil pH assessment and dynamic monitoring.