四川省土壤温度状况空间分布特征

土壤温度状况(STR)在现代土壤系统分类中是确定土壤分类单元的重要诊断特性。利用四川省160个气象站点的多年年均和月均地面气候资料与数字高程模型数据,分析不同气象因子与地形因素对土壤温度(土温)的影响,然后以显著影响的因子为辅助变量,采用回归克里格法预测四川省STR的空间分布,依据中国土壤系统分类高级与基层分类划分标准中有关STR的定量诊断指标,对四川省STR及其空间分布特征进行分析。结果表明,气温、经度、纬度与海拔对土温有显著影响,在高级分类划分标准下,四川省STR以热性、温性、寒性为主,部分区域为永冻、冷性、高热;在基层分类划分标准下,四川省STR以热性、温性、冷性为主,部分地区为高寒性、近寒性、亚寒性、高热性。四川省STR分析为四川土壤系统分类与土壤资源的合理利用提供了科学依据。     

不同方法预测河北省土壤有机碳密度空间分布特征的研究

运用多元线性回归、泛克里格和回归克里格三种方法,结合由DEM获取的地形属性因子预测了河北省土壤有机碳密度的空间分布.多元线性回归预测的残差较大,模型对总方差的解释仅18.6%,采用泛克里格方法后,预测残差降低,预测结果的极差范围变宽,低碳密度区的局部变异得以体现,模型对总方差的解释程度提高到53%.而回归克里格方法应用后预测残差和均方根预测误差进一步降低,模型对总方差的解释程度提高到65%,回归克里格方法也能更好地反映碳密度与地形的关系以及局部变异.三种方法中回归克里格预测效果最好,泛克里格次之,而多元线性回归方法最差.     

不同方法预测河北省土壤有机碳密度空间分布特征的研究

运用多元线性回归、泛克里格和回归克里格三种方法,结合由DEM获取的地形属性因子预测了河北省土壤有机碳密度的空间分布。多元线性回归预测的残差较大,模型对总方差的解释仅18.6%,采用泛克里格方法后,预测残差降低,预测结果的极差范围变宽,低碳密度区的局部变异得以体现,模型对总方差的解释程度提高到53%。而回归克里格方法应用后预测残差和均方根预测误差进一步降低,模型对总方差的解释程度提高到65%,回归克里格方法也能更好地反映碳密度与地形的关系以及局部变异。三种方法中回归克里格预测效果最好,泛克里格次之,而多元线性回归方法最差。     

基于协同克里格插值和地理加权回归模型的土壤属性空间预测比较

选取宜昌市红花套镇作为研究区域,研究土壤pH、有机质、有效磷、速效钾、碱解氮与土壤属性指标变量之间的关系,选择与预测变量之间具有较高相关性的变量作为辅助变量用以提高预测精度,本文试图将地理加权回归模型应用于土壤属性空间模拟中,以此与协同克里格插值的预测结果进行对照,从而比较它们的预测精度以提出更适合土壤属性预测的模型。结果表明:协同克里格插值和地理加权回归模型对土壤属性的空间模拟均有较高的预测精度,在辅助变量较多的情况下地理加权回归模型具有比协同克里格插值更为简单的算法,并且比较预测值相对误差的范围跨度和标准差以及均方根误差等方面,地理加权回归模型在土壤属性指标预测方面具有更高的预测精度,也具有更大的优势。     

不同方法预测苏南农田土壤有机质空间分布对比研究

准确把握土壤有机质(SOM)的空间分布规律对于土壤资源的高效持续利用具有重要意义。以江苏南部为研究区,以辅助因子与SOM的相关性强弱及辅助因子的可获取性为切入点,运用普通克里格(OK)、回归克里格(RK)和随机森林(RF)方法,结合地形、气候、土壤类型、土壤理化性质和施肥、碳投入等辅助数据预测了苏南地区农田SOM含量(0~20 cm)的空间分布。结果表明,三种方法预测的SOM空间分布总体趋势相似,表现为东高西低,但局部分异还存在差异;OK预测的精度最低,100次预测的均方根误差(RMSE)均值为6.97 g·kg~(-1)。RK和RF的预测精度则均高于OK方法,表现为整合与SOM相关性最强的辅助因子全氮(TN)时,RK和RF预测的RMSE分别降低至5.25 g·kg~(-1)和4.97 g·kg~(-1),而移除相关性最强的辅助因子TN后,RK和RF预测的RMSE亦较OK方法低,分别为6.21 g·kg~(-1)和6.29 g·kg~(-1);移除TN后,RK的预测精度稍高于RF,表明在其他辅助数据与SOM相关性相对较弱的条件下,RK方法有助于提高本研究区SOM预测精度;同时,尽管RK和RF的预测精度依然较OK高,但RK和RF对SOM方差的解释度则分别由51%和55%降低至了29%和28%。这表明,目前容易获取且相对廉价的辅助数据,对本研究区的SOM空间预测方面,还面临着数据质量低、预测精度不足等问题。     

陕西省土壤温度和水分状况估算

土壤温度和水分状况是中国土壤系统分类的两个重要诊断特性。土壤的水热状况受近地表温度和降水的直接影响,可以通过气候因素来估算土壤水分和温度状况。以陕西省85个气象台站连续30年的地面气象数据为基础,采用地统计与纽荷模型(JNSM)相结合的方法研究了陕西省土壤温度和水分状况及其空间分布。结果表明,土壤温度状况表现为:秦岭山区及其以北为温性,汉江河谷地区及其以南为热性;土壤水分状况表现为:长城以北为干旱,陕北黄土高原地区和关中盆地为半干润,秦岭以南为湿润,常湿润仅在最南端镇巴县和镇坪县的小部分南部地区存在。     

土壤温度时间序列预测的BP神经网络模型研究

针对滨海盐溃区表层土壤温度时序变化复杂、高度非线性的特点,以江苏省苏北典型滩涂区域为研究对象,综合运用BP神经网络和时间序列多维拓展的方法,对长期定位监测点表土层土壤温度时间序列数据进行了分析和预测,为土壤溶质运移研究与当地作物合理布局提供理论基础和参考依据。结果表明,输入层、隐含层和输出层神经元数目分别为7、7和1的3层BP神经网络模型用于土壤温度时间序列训练仿真时效果最优,其误差平方和达最小值18．017。选定的此结构BP神经网络模型简单、实用,有良好的推广泛化能力,经独立测试样本检验,预测值与实测值的相对误差均在20％以内,平均相对误差仅为2．94％,可满足土壤温度日常预报的需要。     

基于协同克里格插值和地理加权回归模型的土壤属性空间预测比较

选取宜昌市红花套镇作为研究区域,研究土壤pH、有机质、有效磷、速效钾、碱解氮与土壤属性指标变量之间的关系,选择与预测变量之间具有较高相关性的变量作为辅助变量用以提高预测精度,本文试图将地理加权回归模型应用于土壤属性空间模拟中,以此与协同克里格插值的预测结果进行对照,从而比较它们的预测精度以提出更适合土壤属性预测的模型。结果表明：协同克里格插值和地理加权回归模型对土壤属性的空间模拟都有较高的预测精度,在辅助变量较多的情况下地理加权回归模型具有比协同克里格插值更为简单的算法,并且比较预测值相对误差的范围跨度和标准差以及均方根误差等方面,地理加权回归模型在土壤属性指标预测方面具有更高的预测精度,也具有更大的优势。     

基于不同协变量Cokriging土壤养分空间预测精度研究——以陕西省蓝田县为例

以蓝田县西北部农耕区2012年1 114份土壤有机质、碱解氮、有效磷、速效钾4个指标为基础,利用地理信息系统和地统计学相结合的方法,在对协变量个数控制的前提下,通过交叉检验系数和精度提高系数,探索协同克里格插值法对各土壤养分空间分布预测精度的影响。结果表明:各土壤养分空间分布不均匀,土壤养分存在中等变异性;利用增加协同变量方法,依据协变量之间相关性强弱控制协变量进入模型的次序对各土壤养分指标进行协同克里格插值,能提高预测精度,当协变量个数达到3时,各养分指标精度提高分别为有机质0.353%,碱解氮1.114%,有效磷1.088%,速效钾0.646%。研究结果较为准确地预测了样区4个养分指标的空间分布特征,结合土壤类型及土壤施肥管理方法,探讨了土壤养分空间分布特征的原因。     

丘陵区水稻土Cu污染空间变异的协同克里格分析

针对丘陵红壤区铜冶炼厂周围水稻土污染区（1.40km^2）,在景观尺度上,采用协同克里格方法,研究了影响表层土壤Cu含量空间分布预测的辅助因子。基于空间自相关性、间距、长轴方位角以及各种预测误差,评价了辅助变量（包括秸秆全Cu含量StrawCu、籽粒全Cu含量GrainCu、土壤全Cd含量Cd、土壤pH、土壤有机质OM、高程H）对表层土壤Cu含量分布预测精度的影响。结果表明,单辅助变量的协同克里格预测值与实测值相关系数的大小顺序为Cu/Cd〉Cu/H〉Cu/StrawCu〉Cu/GrainCu〉Cu/OM、Cu/pH,而多辅助变量协同克里格预测的相关系数大小顺序为Cu（/Cd,StrawCu）〉Cu（/Cd,StrawCu,H）〉Cu（/Cd,StrawCu,GrainCu）〉Cu/（StrawCu,GrainCu）〉Cu（/Cd,H）。与土壤全Cu含量的普通克里格插值精度相比,利用表层土壤全Cd含量、水稻秸秆全Cu含量、高程作为辅助变量与水稻土表层全Cu含量进行协同克里格插值可以显著提高预测精度;但水稻籽粒全Cu含量作为辅助变量对预测精度影响不显著;而土壤有机质含量和土壤pH作为辅助变量反而降低了预测精度。在对表层土壤全Cu含量分布的多辅助变量协同克里格预测中,表层土壤全Cd含量和水稻秸秆全Cu含量的影响最大,其次是高程,水稻籽粒全Cu含量不能提高对表层土壤全Cu含量分布的预测精度。     

Spatial prediction of soil organic matter in the presence of different external trends with REML-EBLUP

The objective of our study was to compare the performance of the empirical best linear unbiased predictor (E-BLUP) with residual maximum likelihood (REML) with that of regression kriging (RK) for predicting soil organic matter (SOM) with the presence of different external drifts. The study was conducted on a 933 km² area in Pinggu district of Beijing. Terrain attributes (elevation, slope and topographic wetness index) calculated from DEM were used as external drift variable. The root mean squared errors (RMSE) and the mean squared deviation ratio (MSDR) were used to assess the accuracy of prediction and the goodness of fit of the theoretical estimate of error respectively. RK resulted in both the most and least accurate predictions. REML-EBLUP provided more correct residual variogram models than RK for each trend model. Our results have shown that when the value of adjusted R² is greater than 0.45, there is litter difference in the ability to increase the accuracy between REML-EBLUP and RK; and when the value is less than 0.45, the performance of REML-EBLUP is significantly better than RK. It also suggested that topographical data can further improve the accuracy of the spatial predictions of SOM by using RK and REML-EBLUP.     

Small scale digital soil mapping in Southeastern Kenya

Digital soil mapping techniques appear to be an interesting alternative for traditional soil survey techniques. However, most applications deal with (semi-)detailed soil surveys where soil variability is determined by a limited number of soil forming factors. The question that remains is whether digital soil mapping techniques are equally suitable for exploratory or reconnaissance soil surveys in more extensive areas with limited data availability. We applied digital soil mapping in a 13,500 km² study area in Kenya with the main aim to create a reconnaissance soil map to assess clay and soil organic carbon contents in terraced maize fields. Soil spatial variability prediction was based on environmental correlation using the concepts of the soil forming factors equation. During field work, 95 composite soil samples were collected. Auxiliary spatially exhaustive data provided insight on the spatial variation of climate, land cover, topography and parent material. The final digital soil maps were elaborated using regression kriging. The variance explained by the regression kriging models was estimated as 13% and 37% for soil organic carbon and clay respectively. These results were confirmed by cross-validation and provide a significant improvement compared to the existing soil survey.     

20年来东北典型黑土地区土壤肥力质量变化

通过大量样品分析和数据收集,研究了最近20年来东北典型黑土地区土壤肥力质量主要指标——pH、有机质、速效磷、速效钾和黏粒的变化情况,并在地理信息系统和地统计分析的辅助下,研究了该5项肥力指标以及综合肥力指数的时空变异规律。结果表明:20年中该地区土壤pH、有机质和速效钾平均含量明显降低,速效磷平均含量有较大增加;速效磷的变异系数变化最大,pH变化最小;20世纪80年代该地区土壤肥力综合指数以一、二级为主(80%以上),但21世纪初土壤肥力质量几乎被二、三级地所占据(98%以上)。该地区土壤肥力质量明显降低可能与长期以来重种轻养有关。     

中国土壤温度的季节性变化及其区域分异研究

土壤温度是重要的土壤物理性质,其区域分异和季节性变化直接影响着土壤生物的生长发育、繁殖和分布,与农业生产和生态环境紧密相关。本研究根据中国1971～2000年地面气候资料中年均和月均土壤温度、气温和降水数据,分析了我国土壤温度的季节变化及其区域分异特征,并分析了气温和降水对土壤温度的影响。研究表明,我国土壤温度的季节性变化非常明显,土壤温度从春季到夏季变化最大,20℃等温线的纬度跳跃接近25°,而由冬季到春季土壤温度的变化最为缓和。土壤温度的季节变化在不同区域之间存在显著差异;不同区域中土壤温度与气温、降水之间的相关性也各不相同,在温带区域和青藏高原区,气温是土壤温度变化的主要影响因素;在亚热带和热带湿润区土壤温度的季节变化受到气温和降水的共同作用。     

Spatial prediction of soil nutrient in a hilly area using artificial neural network model combined with kriging

It is widely recognized that using correlated environmental factors as auxiliary variables can improve the prediction accuracy of soil properties. In this study, a radial basis function neural network (RBFNN) model combined with ordinary kriging (OK) was proposed to predict spatial distribution of four soil nutrients based on the same framework used by regression kriging (RK). In RBFNN_OK, RBFNN model was used to explain the spatial variability caused by the selected auxiliary factors, while OK was used to express the spatial autocorrelation in RBFNN prediction residuals. The results showed that both RBFNN_OK and RK presented prediction maps with more details. However, RK does not always obtain mean errors (MEs) which were closer to 0 and lower root mean square errors (RMSEs) and mean relative errors (MREs) than OK. Conversely, MREs of RBFNN_OK were much closer to 0 and its RMSEs and MREs were relatively lower than OK and RK. The results suggest that RBFNN_OK is a more unbiased method with more stable prediction performance as well as improvement of prediction accuracy, which also indicates that artificial neural network model is more appropriate than regression model to capture relationships between soil variables and environmental factors. Therefore, RBFNN_OK may provide a useful framework for predicting soil properties.     

Spatial and seasonal variations in soil respiration in a temperate deciduous forest with fluctuating water table

Our objectives were to determine both spatial and temporal variations in soil respiration of a mixed deciduous forest, with soils exhibiting contrasting levels of hydromorphy. Soil respiration (R_S) showed a clear seasonal trend that reflected those of soil temperature (T_S) and soil water content (W_S), especially during summer drought. Using a bivariate model (RMSE=1.03), both optimal soil water content for soil respiration (W_SO) and soil respiration at both 10 °C and optimal soil water content (R_S10) varied among plots, ranging, respectively, from 0.25 to 0.40 and from 2.30 to 3.60 μmol m⁻² s⁻¹. Spatial variation in W_SO was related to bulk density and to topsoil N content, while spatial variation in R_S10 was related to basal area and the difference in pH measured in water or KCl suspensions. These results offer promising perspectives for spatializing ecosystem carbon budget at the regional scale.     

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.     

Spatial distribution pattern changes of oasis soil types in Manasi River Basin,arid northwestern China

Soil distribution pattern play a significant role in the stability conservation and economic development of oasis in arid regions. Hence, ecologists and agrology scientists have a central interest in understanding the spatial distribution changes of soil types. The aim of this study was to analyze the main processes of soil distribution pattern changes from 1987 to 2006 through the landscape indexes. Soil types and soil distribution pattern changes were assessed and compared by using two soil maps made in 1987 and 2006. 14 soil types were classified and analyzed in the study area. Results indicated many differences among the changes of different soil types. During the period from 1987 to 2006, there were widespread changes in spatial distribution of soil types in Manasi River Basin at class-level. The area of Petrocambids decreased, whereas Aquicambids increased. The small patches began to coalesce into large ones and the patch numbers decreased during the past 20 years, which brought about the fragmentation decrease in Manasi River Basin. In contrast to the decrease of the patch density, the average patch area of 12 soil types increased. With the increasing man-made disturbance, more soil type patches, especially the agricultural soil patches were close to square in shape. During the recent 20 years, the decreased patch shape indexes occupied about 57% of all while the increased patch shape indexes were over 40%. The split index of most soil types has also declined during the same period. The landscape-level indexes also reflected the spatial distribution changes of oasis soil types. The landscape diversity index and landscape evenness index have increased while the landscape dominance index has decreased in the recent 20 years, which showed that more equirotal soil patches were formed and various soil types dominated the soil landscape in Manasi River Basin. Changes of different soil types are one of major indictors to show environment changes and impacts of human activities. Therefore, it is necessary to emphasize the study of soil type changes in the arid and semiarid region.