基于World View-3多光谱和关键环境变量的土壤镉含量反演

快速准确获取农田土壤重金属含量对区域土地质量评估和粮食安全至关重要。该研究以江西省仙槎河流域小龙钨矿周边农田土壤为研究对象,采用WorldView-3多光谱影像提取光谱反射率并进行光谱变换处理,同时考虑了地形、人类活动和土壤属性等影响农田土壤镉(Cd)含量空间分布的关键环境因子,将光谱、环境变量、光谱与环境变量分别作为模型的自变量,选取了偏最小二乘(Partial Least Squares Regression,PLSR)、支持向量机(Support Vector Machines,SVM)、BP神经网络(Back Propagation Neural Network,BPNN)和随机森林(RandomForest,RF)4种回归算法构建土壤Cd含量预测模型,并利用精度评价指标优选出最佳反演模型。结果表明：仅输入多光谱特征进行Cd含量反演的模型精度总体偏低,R²低于0.2。相比之下,单独输入环境变量的模型精度结果最为理想,最优模型(RF)精度R²可达0.782。然而,融合光谱信息与环境变量共同建模后并未显著提高模型精度,反而导致较优模型(R...     

黄土高原水蚀风蚀交错带小流域土壤矿质氮空间变异性

为探明小流域土壤养分特性空间变异规律及其与环境因子的关系,以非均匀取样方法测定了黄土高原水蚀风蚀交错带小流域土壤表层（0～10、10～20、20～40 cm）的硝态氮和铵态氮,采用经典统计和地统计学方法分析了其空间分布特征及变异结构。结果表明：土地利用空间配置成混合利用结构拦截和减少径流侵蚀,形成了土壤矿质氮坡面斑块镶嵌格局。小流域内硝态氮和铵态氮呈中等的空间变异和自相关性,其变异性由土壤系统内部因素包括土壤质地、矿物、成土过程、地形特征和人类活动造成的外部因素包括施肥和耕作等共同控制。建立了土壤矿质氮多元回归预测模型,各回归模型的自变量不同,表明不同土层硝态氮和铵态氮的变异受不同环境因子控制。分析认为合理配置土地利用形成斑块状结构和增加养分投入可以改善研究区土壤质量。     

轮作模式在农耕区土壤有机质推测制图中的应用

人类活动近年成为数字土壤制图亟需考虑的要素。本文以农业活动中轮作模式为例,将轮作信息应用于数字土壤制图,探讨其对土壤空间变异刻画的有效性。以安徽宣城两个县市的耕地平区为研究区,通过野外调查获得近年三种主要轮作模式,基于监督分类对多期遥感影像解译得到轮作类型空间分布图,使用方差分析探讨轮作对土壤表层有机质空间变异是否有显著性影响,采用随机森林重要性指标对自然环境因子、轮作模式、土地利用方式和归一化植被指数进行重要性排序,并构建不同的环境因子组合,利用基于相似度的土壤推测模型和随机森林模型进行制图和交叉验证。结果表明,轮作模式对土壤表层有机质有显著性影响,其重要性排序为第二,引入轮作使得基于相似度的土壤推测模型和随机森林模型制图精度分别提高4.8%~65.9%和1.9%~2.7%。     

基于GARBF神经网络的耕地土壤有效磷空间变异分析

为了调整耕地管理措施、合理施用磷肥、减少磷素流失、降低水体非点源污染,该研究以高州市为例,在全市各区镇共采集了664个耕作层(0～20cm)土样,利用遗传算法优化的径向基函数(radial basis function network optimized by geneti calgorithm,GARBF)神经网络和普通克里金法(Ordinary Kriging)等方法,分析了县域耕地土壤有效磷在不同采样尺度下的空间变异特征及其空间分布格局与成因。结果表明,高州市耕地表层土壤有效磷存在半方差结构,半方差函数曲线与指数和球状模型曲线拟合较好;5种采样尺度下(训练样点数分别为100、200、300、400和500)耕地表层土壤有效磷均表现出弱的结构空间相关,在较大范围内空间自相关性较差。GARBF神经网络空间插值能力在整体上要有优于基于邻近点RBF神经网络和普通克里金法。300样本下GARBF神经网络空间插值结果表明,高州市耕地表层土壤有效磷的盈余现象比较严重,并且盈余有效磷的流失对该地区水环境会产生严重的威胁。该研究结果可以为土壤属性空间估测、合理施肥以及降低水体非点源污染提供理论依据和技术支持。     

基于不同模型的区域尺度耕地表层土壤有机质空间分布预测

研究不同模型对土壤有机质空间预测的性能差异对制定更加科学合理的采样策略、提升采样效率和提高土壤空间预测精度有着重要的指导意义。本研究将6496个土壤样点按8∶2的比例分层随机分成训练集与验证集,应用普通克里格、随机森林以及随机森林－回归克里格三种有代表性的数字化土壤制图（Digital Soil Mapping,DSM）模型,对河南省许昌市耕地表层土壤有机质含量及空间分布进行预测,对三种模型性能表现进行综合评价。三种模型输出的预测结果显示:研究区耕地表层土壤有机质含量水平一般,均值为18.70 ~ 18.81 g kg?1,变异系数0.15 ~ 0.17,属中等强度变异;空间分布总体格局为西北与西南部分山地褐土区、东南部砂姜黑土区表层有机质含量高,中北部脱潮土、石灰性潮土区表层有机质含量低。验证结果表明:三种模型性能表现无明显差距,预测精度基本一致,输出结果对研究区耕地表层土壤有机质变异解释百分比在33% ~ 34%之间,在相同和相近尺度土壤有机质空间预测案例研究里属中等水平。在协变量有限且样点分布较为均匀的情况下,普通克里格模型便于快速获得研究区目标变量的空间分布;如果协变量比较丰富且易于收集利用,或是进行空间预测的同时还需要甄别不同因素对目标变量的影响大小,则建议采用随机森林模型;协变量有限,但样点密度较大时,随机森林－回归克里格模型可能是对目标变量进行空间预测的不错选择。     

Estimating Uncertainty in the Revised Universal Soil Loss Equation via Bayesian Melding

Deterministic simulation models are used to understand environmental processes and guide policy development by decision makers. In order to make informed decisions, uncertainty about input and output of these models needs to be incorporated into the modeling. We use a method known as Bayesian melding to quantify the uncertainty in the Revised Universal Soil Loss Equation (RUSLE), an important component of water quality models. This technique allows for this uncertainty through prior distributions on both the input parameters and the outcomes of interest. There have been relatively few applications of this methodology to complex problems and none to date in soil loss modelling. Moreover, land based spatial data, which are now commonly available in environmental research as well as many other disciplines, have not previously been used to inform Bayesian melding. The results demonstrate that the slope steepness factor of the RUSLE is the main contributor to total uncertainty. We conclude that Bayesian melding provides a good method for exploring the sources of uncertainty in a deterministic model.     

The spatial variability of soil nitrates in arable and pasture landscapes: implications for the development of geographical information system models of nitrate leaching

Abstract. In response to the European Community Nitrate Directive (91/676) a catchment scale Geographical Information System (GIS) model of nitrate leaching has been developed to map nitrate vulnerability and predict average weekly fluxes of nitrate from agricultural land units to surface water. This paper presents a pilot study which investigated the spatial variability of soil nitrates in order to: (1) define an appropriate pixel size for modelling N leaching; (2) quantify the within-unit variability of soil nitrate concentrations for pasture and arable fields; and (3) assist in the design of an efficient sampling strategy for estimating mean nitrate concentrations. Soil samples, taken from two 800 m transects in early September 1994, were analysed for water soluble nitrate. The arable soils had a mean nitrate-nitrogen concentration of 0.693 μg/g (S.E. 0.054 μg/g) and the pasture soils had a higher mean nitrate-nitrogen concentration of 0.86 μg/g (S.E. 0.085 μg/g). Spatial variability was investigated using variograms. The pasture data had a weak spatial relationship, whereas the arable data exhibited a strong spatial relationship which fitted a spherical variogram model (r² 0.87), with a range of 40 m. A pixel size of 40 m is suggested for nitrate modelling within the GIS based on the arable variogram and an improved sampling strategy for model validation is suggested, involving bulking sub-samples over a 40 m grid for estimating mean nitrate concentrations in combined land use and soil units.     

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.     

东北黑土区农林混合利用坡面土壤水分空间异质性及主控因素

针对黑土区坡面尺度上土壤水分在土地利用结构(从坡顶到坡脚,即沿着坡长方向,不同土地利用类型的排列方式)、土地利用类型(农地和林地)及地形要素的协同作用下的空间分异规律及影响机制尚不清楚的现状,以黑龙江省黑土区的农林混合利用典型坡面(克山县)为研究对象,应用植被数量生态学中的冗余分析方法(RDA)分析0~20、20~40、40~60 cm土壤水分剖面变异特征、不同土地利用结构下(农地-农地-农地-农地-农地,农地-农地-林地-林地-农地,农地-农地-林地-林地-林地,林地-林地-农地-林地-农地)坡面土壤水分异质性及其与环境因子的定量关系。结果表明:研究区坡面土壤含水率介于5.77%~45.57%,农地土壤含水率显著高于林地(P0.05),纵向上不同土地利用类型层间土壤含水率差异均不显著;土壤水分呈中等变异,纵向上农地各土层的变异系数(35.9%~39.6%)均高于林地(30.0%~36.5%),农林混合利用加强了土壤水分的空间变异程度;4种土地利用结构下,坡面土壤水分沿坡长方向呈不同的变化趋势,与土地利用镶嵌分布规律有关;冗余分析结果显示土地利用类型是影响黑土区坡面土壤水分异质性的主控因素,坡度次之,坡位和海拔高度对坡面土壤水分异质性也有影响。对于黑龙江黑土区坡面,需要结合土地利用结构配置等土地管理措施与不同的农业措施来防止坡面土壤侵蚀、提高东北区土壤肥力,实现经济效益、生态效益的协调统一。     

Analysis of Variability of Some Properties of a Semideciduous Forest Soil

Abstract

The formation of soils in any region is influenced by many factors such as the parent materials and the secondary materials derived from them, the vegetation and the history of land use. These factors vary from place to place, and they contribute to the spatial variation in properties of the soil. Quantification of the magnitude, location, and causes of spatial variability is an essential, but insufficient ingredient of soil surveys. Soil samples from the 0‐ to 20‐cm depth were taken covering soils in the Asuansi‐Akroso‐Nta‐Ofin compound association (Lixisol, Cambisol, and Fluvisol association) at the study site by following the nested balanced hierarchical sampling technique. This covered distances between 100 and 0.80 m. Standard laboratory analyses were performed to quantify the selected properties, namely, pH, organic carbon, total nitrogen, total phosphorus, exchangeable potassium, and content of sand, silt, and clay. Classical statistics and geostatistical procedures were performed on the data and models fitted to the variability patterns. Physical and the more stable properties, such as sand, silt, and clay, were fitted with spherical variogram models. These models indicate a high level of spatial dependence, therefore, such properties may be said to be fairly stable in the field. On the contrary, chemical properties, such as exchangeable potassium, were fitted with exponential variogram models, indicating that these properties were less stable and showed dependence over longer distances. The scale of variation of the properties ranged between 35 and 62 m. The degree of uncertainty associated with time and space can be reduced by improved documentation of field variability using the tools of geostatistics.     

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.     

干热河谷不同土地利用类型坡面土壤水分时空变异

为探究干热河谷区不同土地利用类型坡面土壤水分的时空变化规律,以元谋干热河谷老城小流域水土保持综合治理示范区内的银合欢人工林地、扭黄茅草丛地和坡耕地为研究对象,采用经典统计学和地统计学克里格插值相结合的分析方法,对3种土地类型坡面土壤水分的时间和空间异质性进行研究。结果表明:元谋干热河谷区土壤含水量较低(林地旱季7.56%,雨季12.80%;草地旱季8.05%,雨季12.66%;坡耕地旱季19.37%,雨季22.95%),雨季显著大于旱季。旱、雨季均表现为坡耕地草地林地,呈中等至强度变异(0.14~0.72之间);不同土地利用类型下各层土壤水分的自相关系数均由正向负转化的相同趋势,但拐点有所不同,且雨季大于旱季;不同土地利用类型下旱、雨季土壤水分的最佳拟合模型林地与草地相同(林地与草地旱雨季均为球状模型,坡耕地旱雨季为指数模型),均呈中等或强等空间相关性(0.05~0.39之间),且旱季大于雨季;同一土地类型下旱、雨季不同土层的土壤水分空间分布相似,不同土地利用类型下相同土层分布格局则不同。     

县域农田土壤有机质空间变异及其影响因素分析

研究县域农田空间变异特征可以为培肥地力,增加作物产量提供指导。本文运用地统计学和 GIS相结合的方法,研究了四川省双流县土壤有机质的空间变异特征及其影响因素。结果表明： ①研究区域土壤有机质含量处于中等偏高水平,平均值为 29.72 g/kg,变异系数为 30.11%,属中等变异强度。②有机质变异函数的理论最佳模型为球状模型,块金值与基台值之比为12.67%,表明有机质含量具有强烈的空间相关性,空间相关距离为 91.10 km,普通Kriging插值表明土壤有机质含量呈现北部向东南部减少的趋势。③影响有机质空间变异的主要因素为土壤类型、地貌类型等结构性因子,而土地利用方式、施肥等随机性因子也对有机质空间变异产生重要影响,其中秸秆还田是有机质含量普遍升高的原因。     

Prediction of soil depth using environmental variables in an anthropogenic landscape, a case study in the Western Ghats of Kerala, India

Soil (regolith) depth is a crucial input for modeling earth surface phenomena. However, most studies ignore its spatial variability. Techniques that map the spatial variability of soil depth are of three types: (1) physically-based; (2) empirico-statistical from environmental correlates; and (3) interpolation from point observations. In an anthropogenic landscape, soil depth does not depend primarily on natural processes, making it difficult to apply a physically-based approach. The present study compares empirico-statistical methods with geostatistical methods for predicting soil depth in such a landscape: Aruvikkal catchment (9.5 km²) in the Western Ghats of Kerala, India. Regression kriging applied on blocks of 20 m by 20 m using the environmental covariates elevation, slope, aspect, curvature, wetness index, land use and distance from streams, proved to be the best predictor of soil depth. This model explains 52% of the variability of soil depth in the catchment; with a prediction variance of 0.05 to 0.19. A Gaussian simulation was attempted for a more realistic visualization of the depth, as opposed to the smooth kriging prediction. The most important explanatory variable of soil depth in this landscape is land use, as expected from the strong human intervention.     

不同水质灌溉土壤磁化率空间变异性研究

采用地统计学和GIS相结合的方法,对太行山山前平原城郊区不同水源灌溉条件下土壤磁化率的空间变异性进行研究,并探讨了土壤磁畴状态。结果表明：不同水源灌溉条件下,土壤磁化率均服从正态分布;在一定范围内均存在空间相关性,半方差函数均符合球状模型,具有中等变异程度;变程变化在5.14～10.08 km。污灌区和清灌区的土壤磁化率差异明显,东西(EW)和西北—东南(NW-SE)方向的变异最大,土壤磁性物质主要为细的超顺磁颗粒。     

An assessment of Sentinel-1 synthetic aperture radar,geophysical and topographical covariates for estimating topsoil particle-size fractions

Data derived from synthetic aperture radar (SAR) are widely employed to predict soil properties, particularly soil moisture and soil carbon content. However, few studies address the use of microwave sensors for soil texture retrieval and those that do are typically constrained to bare soil conditions. Here, we test two statistical modelling approaches—linear (with and without interaction terms) and tree-based models, namely compositional linear regression model (LRM) and random forest (RF)—and both nongeophysical (e.g., surface soil moisture, topographic, etc) and geophysical-based (electromagnetic, magnetic and radiometric) covariates to estimate soil texture (sand %, silt % and clay %), using microwave remote sensing data (ESA Sentinel-1). The statistical models evaluated explicitly consider the compositional nature of soil texture and were evaluated with leave-one-out cross-validation (LOOCV). Our findings indicate that both modelling approaches yielded better estimates when fitted without the geophysical covariates. Based on the Nash–Sutcliffe efficiency coefficient (NSE), LRM slightly outperformed RF, with NSE values for sand, silt and clay of 0.94, 0.62 and 0.46, respectively; for RF, the NSE values were 0.93, 0.59 and 0.44. When interaction terms were included, RF was found to outperform LRM. The inclusion of interactions in the LRM resulted in a decrease in NSE value and an increase in the size of the residuals. Findings also indicate that the use of radar-derived variables (e.g., VV, VH, RVI) alone was not able to predict soil particle size without the aid of other covariates. Our findings highlight the importance of explicitly considering the compositional nature of soil texture information in statistical analysis and regression modelling. As part of the continued assessment of microwave remote sensing data (e.g., ESA Sentinel-1) for predicting topsoil particle size, we intend to test surface scattering information derived from the dual-polarimetric decomposition technique and integrate that predictor into the models in order to deal with the effects of vegetation cover on topsoil backscattering.     

Review of modelling crop growth, movement of water and chemicals in relation to topsoil and subsoil compaction

Soil compaction influences crop growth, movement of water and chemicals in numerous ways. Mathematical modelling contributes to better understanding of the complex and variable effects. This paper reviews models for simulating topsoil and subsoil compaction effects. The need for including both topsoil and subsoil compaction results from still increasing compactive effect of vehicular pressure which penetrates more and more into the subsoil and which is very persistent. The models vary widely in their conceptual approach, degree of complexity, input parameters and output presentation. Mechanistic and deterministic models were most frequently used. To characterise soil compactness, the models use bulk density and/or penetration resistance and water content data. In most models root growth is predicted as a function of mechanical impedance and water status of soil and crop yield—from interactions of soil water and plant transpiration and assimilation. Models for predicting movement of water and chemicals are based on the Darcy/Richards one-dimensional flow equation. The effect of soil compaction is considered by changing hydraulic conductivity, water retention and root growth. The models available allow assessment of the effects of topsoil and subsoil compaction on crop yield, vertical root distribution, chemical movement and soil erosion. The performance of some models was improved by considering macro-porosity and strength discontinuity (spatial and temporal variability of material parameters). Scarcity of experimental data on the heterogeneity is a constraint in modelling the effects of soil compaction. Suitability of most models was determined under given site conditions. Few of the models (i.e. SIBIL and SIMWASER) were found to be satisfactory in modelling the effect of soil compaction on soil water dynamics and crop growth under different climate and soil conditions.     

黄土高原雨养区坡面土壤水力学性质空间特征及影响因素

土壤水力学性质在建立水分运动模型及水土保持措施配置中具有重要作用。以网格采样测定了黄土高原雨养区坡面土壤水分特征曲线,拟合了Van Genuchten和Gardner模型参数,并利用经典统计和地统计方法分析了其空间分布特征及影响因子。结果表明:在黄土高原雨养区复杂的土地利用结构下,坡面表层土壤水力学性质具有明显的空间变异性,Van Genuchten模型参数n不存在空间相关情况,为纯随机变量,参数a,A,B,A·B和饱和导水率的空间变异受到系统变异和随机变异的共同作用。Gardner模型参数A和B值受到有机质含量的影响,饱和含水量、田间持水量和容重与参数A,A·B及有效孔隙度之间的相关性均达到极显著水平。比重与坡面土壤水力学性质之间的相关关系不显著。土地利用和地形因子对水分特征曲线的影响明显,在高吸力阶段,上坡位比下坡位土壤保持的水分多,农田的持水能力不如草地和林地。     

Predicting soil organic carbon density using auxiliary environmental variables in northern Iran

In the present study, artificial neural networks (ANNs) were employed to develop models to predict soil organic carbon density (SOCD) at different depths of soil layers. Selected environmental variables such as vegetation indices, soil particle size distribution, land use type, besides primary and secondary terrain attributes were considered as the input variables. According to the results, the ANN models explained 77% and 72% of the variability in SOCD at soil layer depths of 0–20 cm and 20–40 cm, respectively, at the site studied. Sensitivity analyses showed that the most considerable positive contribution of variables for predicting SOCD included the land use type, normalized difference vegetation index (NDVI) > normalized difference water index (NDWI) > silt > clay > elevation in the 0–20 cm soil layer. On the other hand, for the 20–40 cm soil layer, the land use type following NDVI > NDWI > clay > silt were identified as the most powerful predictive factors. In the Deylaman region, in both soil layers, sand had a considerable negative effect on SOCD and most of the terrain attributes had no significant impact on the SOCD prediction. Therefore, these results provide valuable information for sustainable management and decision-making on a landscape scale for governors and other users.     

沙地整治下榆林土地利用及土壤有机质时空分异特征

毛乌素沙地是典型的生态脆弱区,近年来针对其在榆林境内的沙地整治利用取得显著成效,也对土壤环境产生了深刻影响。为了探究沙地不同整治利用方式对土壤有机质的影响,该研究选取榆林市显性沙地,利用多光谱遥感影像及相关光谱指数,结合沙地土地利用变化特征,通过XGBoost机器学习方法,反演1990—2020年土壤有机质含量;分析不同土地类型下土壤有机质含量变化,通过半变异函数揭示了其空间变异性,厘清人为因素和自然环境的影响程度。结果表明,30 a间榆林5 460 km2沙地中超过半数得到整治和利用,沙地-草地是最主要的地类转变方式,建设用地面积增长最迅速;沙区土壤有机质含量上升,但整体呈现先增加后降低的趋势,有机质均值由0.34%增长至0.79%,近10年降低至0.51%;榆林沙区土壤有机质具有较强的空间自相关性。起初,人为利用对其有积极作用,但随着沙地的利用强度增大,对土壤有机质产生负向作用,进而致使其含量下降,面临土地退化危机。建议加强退化林草的修复改良,放缓建设用地开发力度,研究以期为沙地整治提供理论和实践借鉴意义,保护榆林沙地土壤环境安全。