Spatial prediction of USDA‐ great soil groups in the arid Zarand region,Iran: comparing logistic regression approaches to predict diagnostic horizons and soil types

The main objectives of this study were to compare binary logistic regression as an indirect approach and multinomial logistic regression as a direct approach to produce soil class maps in the Zarand region of southeast Iran. With indirect prediction, the occurrence of relevant diagnostic horizons was first mapped, and subsequently, various maps were combined for a pixel‐wise classification by combining the presence or absence of diagnostic horizons. In direct prediction, the dependent variable was the great group itself, so the probability distribution of the great soil groups was directly predicted. Among the predictors, the geomorphology map was identified as an important tool for digital soil mapping approaches as it helped to increase the accuracy. The results of prediction showed larger mean probability values for each great soil group in the areas actually covered by the great soil groups compared with other areas, indicating the reliability of the prediction. In most predictions, the global purity was slightly better than the actual purity for the models; however, both models provided poor predictions for Haplocambids and Calcigypsids. The results showed that soils with better prediction were those much influenced by topographical and geomorphological characteristics and soils with very poor accuracy of prediction were only slightly influenced by topographical and geomorphological characteristics. An advantage of the indirect method is that it gives insight into the causes of errors in prediction at the scale of diagnostic horizons, which helps in the selection of better covariates.     

结合统计和数字地形数据的可视化方法预测土壤深度

Information about the spatial distribution of soil attributes is indispensable for many land resource management applications; however, the ability of soil maps to supply such information for modern modeling tools is questionable. The objectives of this study were to investigate the possibility of predicting soil depth using some terrain attributes derived from digital elevation models (DEMs) with geographic information systems (GIS) and to suggest an approach to predict other soil attributes. Soil depth was determined at 652 field observations over the Al-Muwaqqar Watershed (70 km²) in Jordan. Terrain attributes derived from 30-m resolution DEMs were utilized to predict soil depth. The results indicated that the use of multiple linear regression models within small watershed subdivisions enabled the prediction of soil depth with a difference of 50 cm for 77% of the field observations. The spatial distribution of the predicted soil depth was visually coincided and had good correlations with the spatial distribution of the classes amalgamating three terrain attributes, slope steepness, slope shape, and compound topographic index. These suggested that the modeling of soil-landscape relationships within small watershed subdivisions using the three terrain attributes was a promising approach to predict other soil attributes.     

基于环境因子和联合概率方法的土壤有机质空间预测

<正>土壤连续属性(如土壤中养分含量、重金属含量等)的空间分布特征和定量分布信息是进行土壤质量评价和区域环境综合评估的基础。精准农业战略的实施和各种区域生态评价均需要更详细更精确的土壤属性信息作为依据[1-2]。因此,土壤属性空间预测一直是土壤学研究的热点问题。经典地统计学以各种克里格插值法为代表,是土壤属性空间预测中的常用方法。但该方法缺乏对辅助信息(如环境信息)的有效利用[3-4],导致预测精度降低[5]。而土壤景观定量模型的理论依据就是土壤与环境的关系,但该法忽略了采样点之间的空间相     

基于环境相关法和地统计学的土壤属性空间分布预测

土壤属性是土壤质量的重要决定因素,并强烈影响土地利用和生态过程。正确理解并充分考虑土壤空间变异,对于在景观尺度上建立生态、环境过程模型是必不可少的。在黄土高原横山县采集了254个样点,应用数字地形与遥感影像分析技术,获取相关地形因子与遥感指数,分析土壤属性（土壤容重、有机质和全磷）与环境因子相互关系,并利用环境变量进行空间预测。结果表明,土壤容重、有机质与地形因子和遥感指数之间存在较好相关性,而全磷与地形因子相关性不大;多元线性逐步回归模型对于土壤容重和有机质拟合较好,而对于全磷,预测结果较差;回归－克里格预测有效地减小了残差,消除了平滑效应,与实测值较为接近。     

空间回归模型在区域数字化土壤制图中的应用——以河南封丘县为例

以河南省封丘县为研究区,以环境协变量信息和先期获得的土壤数值化分类结果为基础数据源,在土壤分类距离空间自相关性分析的基础上,构建土壤分类距离—环境协变量空间回归模型,实施土壤分类距离空间预测,并最终实现研究区25 m分辨率数字化土壤制图.输出结果表明,研究区5种主要土壤类型中,普通底锈干润雏形土分布面积最大、弱盐灌於干润雏形土次之,分布比例分别为36％和24％.结合确定性趋势距离和非确定性残差的空间变异特征,阐释了研究区土壤空间分布格局的发生学背景和随机性因素的影响.与基于随机模型的土壤预测制图相比,基于环境协变量空间回归模型的数字化土壤制图输出结果展示了相似的研究区土壤空间分布整体格局,且具有细节清晰、图斑边界自然的特点.一方面能更好地诠释土壤空间分布的连续性和渐变性特征;另一方面能较好地反映微域成土环境对土壤发生学特性空间变异特征的影响.     

基于不同地表曲面模型预测土壤有机碳含量

Local terrain attributes,which are derived directly from the digital elevation model,have been widely applied in digital soil mapping.This study aimed to evaluate the mapping accuracy of soil organic carbon (SOC) concentration in 2 zones of the Heihe River in China,by combining prediction methods with local terrain attributes derived from different polynomial models.The prediction accuracy was used as a benchmark for those who may be more concerned with how accurately the variability of soil properties is modeled in practice,rather than how morphometric variables and their geomorphologic interpretations are understood and calculated.In this study,2 neighborhood types (square and circular) and 6 representative algorithms (Evans-Young,Horn,Zevenbergen-Thorne,Shary,Shi,and Florinsky algorithms) were applied.In general,35 combinations of first-and second-order derivatives were produced as candidate predictors for soil mapping using two mapping methods (i.e.,kriging with an external drift and geographically weighted regression).The results showed that appropriate local terrain attribute algorithms could better capture the spatial variation of SOC concentration in a region where soil properties are strongly influenced by the topography.Among the different combinations of first-and second-order derivatives used,there was a best combination with a more accurate estimate.For different prediction methods,the relative improvement in the two zones varied between 0.30％ and 9.68％.The SOC maps resulting from the higher-order algorithms (Zevenbergen-Thorne and Florinsky) yielded less interpolation errors.Therefore,it was concluded that the performance of predictive methods,which incorporated auxiliary variables,could be improved by attempting different terrain analysis algorithms.     

基于地形因子和随机森林的丘陵区农田土壤有效铁空间分布预测

为了掌握丘陵地区农田土壤有效铁含量及其空间分布,本文以重庆市江津区永兴镇内同源成土母质的典型丘陵(2 km2)为研究区,采集309个土壤样点,利用普通克里格(Ordinary Kriging,OK)、多元线性回归(Multiple Linear Regression,MLR)、随机森林(Random Forest,RF)模型,结合高程、坡度、坡向、谷深、平面曲率、剖面曲率、汇聚指数、相对坡位指数、地形湿度指数等地形因子对土壤有效铁进行空间分布预测,并通过85个验证点评价、筛选预测模型。结果表明:1)土壤有效铁与谷深、地形湿度指数存在极显著水平正相关关系,与坡度、平面曲率、剖面曲率、汇聚指数、相对坡位指数存在极显著水平负相关关系。2)随机森林模型的预测精度明显高于多元线性回归和普通克里格插值,其平均绝对误差为22.33 mg·kg-1、均方根误差为27.98 mg·kg-1、决定系数为0.76,是研究区土壤有效铁含量空间分布的最适预测模型。3)地形湿度指数和坡度是影响该区域土壤有效铁含量空间分布的主要地形因子。土壤有效铁与坡度、谷深、平面曲率、剖面曲率、汇聚指数、相对坡位指数、地形湿度指数均达到极显著水平相关关系。4)研究区土壤有效铁含量范围为3.00~276.97 mg?kg-1,水田有效铁含量大于旱地;土壤有效铁具有较强的空间相关性,土壤有效铁含量空间变异主要受到结构性因素的影响。可见,基于地形因子的随机森林预测模型可以较好地解释丘陵区农田土壤有效铁含量的空间变异,研究结果为丘陵区土壤中、微量元素含量及空间分布预测提供方法借鉴和理论依据。     

Quantitative mapping of soil types based on regression kriging of taxonomic distances with landform and land cover attributes

Nowadays, French soil scientists tend to gather new and existing soil data into a common database. The use of this database potentially allows for resolving environmental issues, largely through soil mapping. The purpose of this study is to present a methodology for mapping soil types illustrated by typical observations in the soil database, in this case from the La Rochelle area on the French Mid-Atlantic Coast. The main hypothesis underlying the method is that soil types result from environmental factors such as landform, parent material, and land cover. The method can be divided into four stages. The first step is to construct a local soil type classification from the database by a two-stage continuous classification procedure. The result of this procedure is that at each observation point, the soil is described by a vector of taxonomic distances to each of k centroidal soil types. In the example given, k=18. The second step involves fitting soil–environment equations, one for each centroidal soil type, by regressing taxonomic distances on layers of multivariate environmental data observed on a fine 20-m grid, by multiple linear regression. In this case, the layers are terrain attributes derived from a digital elevation model and land cover attributes derived from three bands of a SPOT image. The third step is to predict k maps or raster GIS layers representing taxonomic distances to soil types on the 20-m grid, using the soil–environment equations and the kriging of the residuals from the regressions. This results in many potential maps: a summary map depicting the nearest centroidal soil type (the soil type for which the taxonomic distance is least) at each location is possibly the most useful, and another one representing the minimum taxonomic distance which, if considered too large, might suggest locations for further field survey to refine the soil types. A map of standard errors of the kriged taxonomic distance residuals to the nearest centroidal soil type can be made to indicate spatial uncertainty. Continuous fuzzy membership maps can also be constructed from the distances. The fourth step involves validation with an independent soil data set allowing discovery of the nature of the actual prediction errors. Thirty-eight percent of sites in a validation sample of 1234 sites was unequivocally validated, 23% was equivocally validated, and the remainder was predicted wrongly by the method.     

基于环境变量的渭干河-库车河绿洲土壤盐分空间分布

土壤属性的数字制图对精准农业生产和环境保护治理至关重要。为了在大尺度上尽可能精确的监测土壤盐分空间变异性,该文使用普通克里格(ordinary kriging,OK)、地理加权回归(geographically weighted regression,GWR)和随机森林(random forest,RF)方法,结合地形、土壤理化性质和遥感影像数据等16个环境辅助变量,绘制渭干河-库车河绿洲表层土壤盐分分布图。基于决定系数(R^2)、均方根误差(RMSE)和平均绝对误差(MAE)验证模型精度。结果表明:不同方法预测的盐分分布趋势没有显著差异,大体上从研究区的西北向东南部方向增加;结合辅助变量的不同预测方法中,RF方法预测精度最高,R^2为0.74,RMSE和MAE分别为9.07和7.90 mS/cm,说明该模型可以有效地对区域尺度的土壤盐分进行定量估算;RF方法对电导率(electric conductivity,EC)低于2 mS/cm时预测精度最高,RMSE为3.96 mS/cm,很好的削弱了植被覆盖对电导率EC的影响。     

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.     

农田土壤质地空间分布的三维随机模拟及其不确定性评价

定量描述土体三维构型对于土地利用及农田水肥管理研究极其重要.本研究根据华北山前冲积平原区一块农田内的109个土壤剖面观测数据,运用马尔科夫地质统计学方法构建了土壤质地种类的三维空间分布模型,在100次随机模拟的基础上,分别得到了土壤质地种类的优化分布图及其概率分布图.结果表明,一维嵌入转移概率模型能很好地描述农田水平和垂直方向上各土壤质地种类的空间连续性及毗邻转移趋势.优化分布图虽能直观反映土壤质地种类的空间分布特征,但存在明显的平滑效应,不能刻画土壤质地种类空间分布的不确定性.而采用概率分布的方式来描述土壤质地种类空间分布的不确定性,能够有效地克服该缺点.     

黑土区田块尺度土壤有机质含量遥感反演模型

为了对田块尺度土壤有机质进行空间反演并提高模型精度和稳定性,该文以黑龙江省黑土带41.3 hm~2田块为例,获取2016年5月中下旬两期(受限于拍摄周期和天气原因而选择不同卫星影像,2016年5月17日Landsat 8影像和5月25日Sentinel-2A影像)裸土时期遥感影像和4 m分辨率DEM数据;分析单期影像与土壤有机质(soil organic matter,SOM)的关系,两期影像所包含的土壤含水量变化信息与地形因素对SOM预测模型精度的影响,建立基于BP神经网络的SOM遥感反演模型。结果表明:该田块内SOM含量差异较大;利用单期影像预测SOM时,基于红波段和785~899 nm波段建立的预测模型精度(建模均方根误差RMSE 1.033,检验RMSE 1.079)和稳定性(建模决定系数R2 0.677,检验R20.644)较高;两期影像时,基于红波段和1 570~1 650 nm波段建立的预测模型精度(建模RMSE 0.855,检验RMSE 0.898)和稳定性(建模R2 0.792,检验R2 0.797)显著提高;在两期影像模型基础上,加入地形因子作为输入量,模型精度(建模RMSE 0.492,检验RMSE 0.499)和稳定性(建模R2 0.917,检验R2 0.928)进一步提高。研究成果可为土壤碳库估算和农田精准施肥提供理论与技术支持。     

环境敏感变量优选及机器学习算法预测绿洲土壤盐分

基于机器学习预测干旱区(如新疆)土壤盐分的研究目前较少涉及且敏感变量的筛选还需深入探讨。该研究比较5种机器学习算法(套索算法,The Least Absolute Shrinkage and Selection Operator-LASSO;多元自适应回归样条函数,Multiple Adaptive Regression Splines-MARS;分类与回归树,Classification and Regression Trees-CART;随机森林,Random Forest-RF;随机梯度增进算法,Stochastic Gradient Treeboost-SGT)在3个不同地理区域(奇台绿洲,渭-库绿洲和于田绿洲)的性能表现;参与的变量被分为6组:波段,植被相关变量集,土壤相关变量集,数字高程模型(digital elevation model,DEM)衍生变量集,全变量组,优选变量组(全变量组经过算法筛选后的变量集合)。通过算法筛选,以示不同研究区的盐度敏感变量。同时借助以上述6组结果评判算法的性能。结果表明:综合分析6个变量组的R2和RMSE,预测精度排名如下:优选变量组植被指数变量组土壤相关变量组波段DEM衍生变量组。由于结果不稳定,全变量组未参与排名。在所有变量中,植被指数(EEVI,ENDVI,EVI2,CSRI,GDVI)和土壤盐度指数(SIT,SI2和SAIO)与土壤盐度相关性高于其他变量。综合评价以上5种算法,Lasso和MARS的预测结果出现极端异常值,但其预测结果能基本呈现土壤盐分空间分布格局。CART的结果能清晰分辨灌区和非灌区土壤盐分的分布态势,但二者内部并无太多变化且稳定性较差。RF和SGT的结果显示,二者在3个绿洲的土壤盐分值域范围和土壤盐分空间分布格局相似,纹理信息相对其他3个算法更为丰富。更为重要的是,算法在各个地区的结果都较为稳定。二者相比,SGT验证精度相对最高,其次为RF。     

The ConMap approach for terrain-based digital soil mapping

We present a new digital terrain analysis framework for digital soil mapping, referred to as contextual elevation mapping (ConMap). In contrast to common regression approaches based on features from digital terrain analysis, ConMap is not based on standard terrain attributes, but on elevation differences from the centre pixel to each pixel in circular neighbourhoods only. These differences are used as features in random forest regressions. We applied and validated the framework by predicting topsoil silt content in a loess region of 1150 km² in Rhineland-Palatinate and Hesse, Germany. Three hundred and forty-two samples and a 20-m resolution digital elevation model were used for this illustration and validation. We compared ConMap with standard and multi-scale terrain analysis approaches as well as with ordinary kriging interpolations. Cross-validation root mean square error ( RMSE) decreased from 16.1 when the standard digital terrain analysis was used to 11.2 when ConMap was used. This corresponds to an increase in variance explained ( R ²) from 15 to 61%. Even though ordinary kriging out-performed standard terrain analysis as well, the variance explained was 6% smaller compared with that using ConMap. The results show that the geomorphic settings in the study area must have induced the spatial trend, which can be accounted for by ConMap over different scales. We conclude that ConMap shows great potential for digital soil mapping studies.     

Digital soil mapping using artificial neural networks

In the context of a growing demand of high‐resolution spatial soil information for environmental planning and modeling, fast and accurate prediction methods are needed to provide high‐quality digital soil maps. Thus, this study focuses on the development of a methodology based on artificial neural networks (ANN) that is able to spatially predict soil units. Within a test area in Rhineland‐Palatinate (Germany), covering an area of about 600 km², a digital soil map was predicted. Based on feed‐forward ANN with the resilient backpropagation learning algorithm, the optimal network topology was determined with one hidden layer and 15 to 30 cells depending on the soil unit to be predicted. To describe the occurrence of a soil unit and to train the ANN, 69 different terrain attributes, 53 geologic‐petrographic units, and 3 types of land use were extracted from existing maps and databases. 80% of the predicted soil units (n = 33) showed training errors (mean square error) of the ANN below 0.1, 43% were even below 0.05. Validation returned a mean accuracy of over 92% for the trained network outputs. Altogether, the presented methodology based on ANN and an extended digital terrain‐analysis approach is time‐saving and cost effective and provides remarkable results.     

紫色土丘陵地区农田土壤养分空间分布预测

为深入研究紫色土丘陵区农田土壤养分空间分布规律,在GIS技术的支持下,利用研究区450个土壤实测数据,结合地形因子和土地利用类型,运用多重线性回归构建了土壤养分预测模型,对养分的空间分布进行预测。结果表明,土壤有机质和碱解氮含量与地形因子之间的相关性较强,有效磷和速效钾含量与地形因子之间的相关性较弱。土壤水田和旱地中有机质、碱解氮和有效磷含量均值间的差异显著(P<0.01),速效钾之间不显著(P=0.34)。基于地形因子的土壤养分预测模型与基于地形因子和土地利用方式组合的土壤养分预测模型预测结果精度对比表明,在预测变量中增加土地利用类型对提高预测模型的拟合度和预测精度作用非常微小,且仅用地形因子预测土壤养分的空间分布更方便,因此选用该模型对验证集数据进行预测。以验证集数据进行预测结果与实测值进行比较,结果显示预测值与实测值之间的差异甚小,有机质、碱解氮、有效磷和速效钾的相对偏差分别为0.09、0.19、0.08和0.12,均方根误差分别为1.38、3.42、1.03和1.57,说明基于地形因子的土壤养分预测模型的精度较高,可以很好地预测土壤养分分布规律。该研究结果可为丘陵地区农田合理施肥提供理论依据。     

Modelling soil erodibility in mountain rangelands of southern Kyrgyzstan

Soil erosion in mountain rangelands in Kyrgyzstan is an emerging problem due to vegetation loss caused by overgrazing. It is further exacerbated by mountain terrain and high precipitation values in Fergana range in the south of Kyrgyzstan. The main objective of this study was to map soil erodibility in the mountainous rangelands of Kyrgyzstan. The results of this effort are expected to contribute to the development of soil erodibility modelling approaches for mountainous areas. In this study, we mapped soil erodibility at two sites, both representing grazing rangelands in the mountains of Kyrgyzstan and having potentially different levels of grazing pressure. We collected a total of 232 soil samples evenly distributed in geographical space and feature space. Then we analyzed the samples in laboratory for grain size distribution and calculated soil erodibility values from these data using the Revised Universal Soil Loss Equation (RUSLE) K-factor formula. After that, we derived different terrain indices and ratios of frequency bands from ASTER GDEM and LANDSAT images to use as auxiliary data because they are among the main soil forming factors and widely used for prediction of various soil properties. Soil erodibility was significantly correlated with channel network base level (geographically extrapolated altitude of water channels), remotely sensed indices of short-wave infrared spectral bands, exposition, and slope degree. We applied multiple regression analysis to predict soil erodibility from spatially explicit terrain and remotely sensed indices. The final soil erodibility model was developed using the spatially explicit predictors and the regression equation and then improved by adding the residuals. The spatial resolution of the model was 30 m, and the estimated mean adjusted coefficient of determination was 0.47. The two sites indicated different estimated and predicted means of soil erodibility values (0.035 and 0.039) with a 0.05 significance level, which is attributed mainly to the considerable difference in elevation.     

河南省地形、土壤和地表水体多样性格局特征

在水土资源多样性的研究中,地形要素对其空间分布形式和内在联系有重要影响。选取河南省作为研究区,在1 km×1 km网格尺度下以变形仙农熵公式计算河南省的地形空间分布多样性和土壤空间分布多样性(土类级别)及关联性,并将河南省划分为6个面积相近的次级区域,用空间分布面积指数(Y_h)计算地形、土壤的构成组分多样性,用空间分布长度指数(MSHDLI)计算地表水体多样性,对以上特征及关联性进行研究。结果表明:平原和潮土是河南省面积最大且空间分布离散性最高的地形类型和土类。地形与土壤之间关系密切,有76%以上的相关系数r值大于0.50,相关性高;6个分区中,东部分区为单一的平原地形但MSHDLI值次高,平原地形水系发育好,西部分区地形复杂且以山地为主但地表水体多样性指数MSHDLI值最小,山地条件下水系发育较简单;研究区面积相近的情况下,土类构成组分多样性值主要取决于土类间面积大小的均衡程度,与土类面积比例平均变化量之间呈负相关,R~2值为0.94;面状的地形和土壤构成组分多样性指数与线状的地表水体多样性指数间无明显相关性。综上所述,地形、土壤和地表水体三要素间关系密切,共同影响地多样性的空间格局。     

Digital soil mapping in Germany—a review

Digital soil mapping as a tool to generate spatial soil information provides solutions for the growing demand for high‐resolution soil maps worldwide. Even in highly developed countries like Germany, digital soil mapping becomes essential due to the decreasing, time‐consuming, and expensive field surveys which are no longer affordable by the soil surveys of the individual federal states. This article summarizes the present state of soil survey in Germany in terms of digitally available soil data, applied digital soil mapping, and research in the broader field of pedometrics and discusses future perspectives. Based on the geomorphologic conditions in Germany, relief is a major driving force in soil genesis. This is expressed by the digital–soil mapping research which highlights the great importance of digital terrain attributes in combination with information on parent material in soil prediction. An example of digital soil mapping using classification trees in Thuringia is given as an introduction in digital soil‐class mapping based on correlations to environmental covariates within the scope of the German classification system.     

基于地形属性使用回归, 克里格和人工神经网络方法估算饱和导水率空间分布

Several methods,including stepwise regression,ordinary kriging,cokriging,kriging with external drift,kriging with varying local means,regression-kriging,ordinary artificial neural networks,and kriging combined with artificial neural networks,were compared to predict spatial variation of saturated hydraulic conductivity from environmental covariates.All methods except ordinary kriging allow for inclusion of secondary variables.The secondary spatial information used was terrain attributes including elevation,slope gradient,slope aspect,profile curvature and contour curvature.A multiple jackknifing procedure was used as a validation method.Root mean square error (RMSE) and mean absolute error (MAE) were used as the validation indices,with the mean RMSE and mean MAE used to judge the prediction quality.Prediction performance by ordinary kriging was poor,indicating that prediction of saturated hydraulic conductivity can be improved by incorporating ancillary data such as terrain variables.Kriging combined with artificial neural networks performed best.These prediction models made better use of ancillary information in predicting saturated hydraulic conductivity compared with the competing models.The combination of geostatistical predictors with neural computing techniques offers more capability for incorporating ancillary information in predictive soil mapping.There is great potential for further research and development of hybrid methods for digital soil mapping.