基于辅助环境变量的土壤有机碳空间插值——以黄土丘陵区小流域为例

利用普通克里格法(OK)、反距离加权法(IDW)、径向基函数法(RBF)、基于土地利用类型修正的普通克里格法(OK_LU)4种插值方法,对黄土丘陵羊圈沟小流域的土壤有机碳含量进行空间插值。预测结果的准确性通过Pearson相关系数(R),平均绝对误差(MAE),均方根误差(RMSE),准确度(AC)来评价。研究结果表明:(1)在前3种常规空间插值方法中,OK对刻画区域土壤有机碳的空间分布趋势效果最佳,其预测MAE值和RMSE值均为最小,Pearson相关系数(R)和准确度(AC)最大,说明其预测结果的准确性最好、预测的极端误差也最小;其次为RBF;IDW预测的效果最差。(2)OK_LU在空间特征表达方面能够更好地反映复杂地形区的局部变异,其插值结果的精度相比OK有一定程度的提高,其平均绝对误差(MAE)从0.900%降到了0.567%,均方根误差(RMSE)从1.101%降到了0.777%,Pearson相关系数(R)从0.4026提高到0.5589,准确度(AC)从0.9081提高到0.9505。综合比较,在黄土丘陵地区,OK_LU能使插值结果的精度有较大提高,是土壤有机碳空间制图的有效途径。     

基于神经网络模型和地统计学方法的土壤养分空间分布预测

采用径向基函数神经网络模型与普通克里格法相结合的方法,预测川中丘陵区县域尺度土壤养分(有机质和全氮)的空间分布,并与普通克里格法和回归克里格法进行比较.结果表明：各方法对研究区土壤养分的预测结果相似.与多元回归模型相比,神经网络模型对验证样点土壤有机质和全氮的预测值与样点实测值的相关系数分别提高了12.3%和16.5%,表明神经网络模型能更准确地捕捉土壤养分与定量环境因子间的复杂关系.对469个验证样点预测结果的误差分析表明,神经网络模型与普通克里格法相结合的方法对土壤有机质和全氮预测结果的平均绝对误差、平均相对误差、均方根误差较普通克里格法分别降低了6.9%、7.4%、5.1%和4.9%、6.1%、4.6%,降低幅度达到极显著水平(P<0.01);与回归克里格法相比则分别降低了2.4%、2.6%、1.8%和2.1%、2.8%、2.2%,降低幅度达显著水平(P<0.05).     

Spatial Analysis of Soil Organic Carbon in Zhifanggou Catchment of the Loess Plateau

Soil organic carbon (SOC) reflects soil quality and plays a critical role in soil protection, food safety, and global climate changes. This study involved grid sampling at different depths (6 layers) between 0 and 100 cm in a catchment. A total of 1282 soil samples were collected from 215 plots over 8.27 km². A combination of conventional analytical methods and geostatistical methods were used to analyze the data for spatial variability and soil carbon content patterns. The mean SOC content in the 1282 samples from the study field was 3.08 g·kg⁻¹. The SOC content of each layer decreased with increasing soil depth by a power function relationship. The SOC content of each layer was moderately variable and followed a lognormal distribution. The semi-variograms of the SOC contents of the six different layers were fit with the following models: exponential, spherical, exponential, Gaussian, exponential, and exponential, respectively. A moderate spatial dependence was observed in the 0–10 and 10–20 cm layers, which resulted from stochastic and structural factors. The spatial distribution of SOC content in the four layers between 20 and 100 cm exhibit were mainly restricted by structural factors. Correlations within each layer were observed between 234 and 562 m. A classical Kriging interpolation was used to directly visualize the spatial distribution of SOC in the catchment. The variability in spatial distribution was related to topography, land use type, and human activity. Finally, the vertical distribution of SOC decreased. Our results suggest that the ordinary Kriging interpolation can directly reveal the spatial distribution of SOC and the sample distance about this study is sufficient for interpolation or plotting. More research is needed, however, to clarify the spatial variability on the bigger scale and better understand the factors controlling spatial variability of soil carbon in the Loess Plateau region.     

Combining Soil Databases for Topsoil Organic Carbon Mapping in Europe

Accuracy in assessing the distribution of soil organic carbon (SOC) is an important issue because of playing key roles in the functions of both natural ecosystems and agricultural systems. There are several studies in the literature with the aim of finding the best method to assess and map the distribution of SOC content for Europe. Therefore this study aims searching for another aspect of this issue by looking to the performances of using aggregated soil samples coming from different studies and land-uses. The total number of the soil samples in this study was 23,835 and they’re collected from the “Land Use/Cover Area frame Statistical Survey” (LUCAS) Project (samples from agricultural soil), BioSoil Project (samples from forest soil), and “Soil Transformations in European Catchments” (SoilTrEC) Project (samples from local soil data coming from six different critical zone observatories (CZOs) in Europe). Moreover, 15 spatial indicators (slope, aspect, elevation, compound topographic index (CTI), CORINE land-cover classification, parent material, texture, world reference base (WRB) soil classification, geological formations, annual average temperature, min-max temperature, total precipitation and average precipitation (for years 1960–1990 and 2000–2010)) were used as auxiliary variables in this prediction. One of the most popular geostatistical techniques, Regression-Kriging (RK), was applied to build the model and assess the distribution of SOC. This study showed that, even though RK method was appropriate for successful SOC mapping, using combined databases was not helpful to increase the statistical significance of the method results for assessing the SOC distribution. According to our results; SOC variation was mainly affected by elevation, slope, CTI, average temperature, average and total precipitation, texture, WRB and CORINE variables for Europe scale in our model. Moreover, the highest average SOC contents were found in the wetland areas; agricultural areas have much lower soil organic carbon content than forest and semi natural areas; Ireland, Sweden and Finland has the highest SOC, on the contrary, Portugal, Poland, Hungary, Spain, Italy have the lowest values with the average 3%.     

Spatial Variability of the Topsoil Organic Carbon in the Moso Bamboo Forests of Southern China in Association with Soil Properties

Understanding the spatial variability of soil organic carbon (SOC) must be enhanced to improve sampling design and to develop soil management strategies in terrestrial ecosystems. Moso bamboo (Phyllostachys pubescens Mazel ex Houz.) forests have a high SOC storage potential; however, they also vary significantly spatially. This study investigated the spatial variability of SOC (0-20 cm) in association with other soil properties and with spatial variables in the Moso bamboo forests of Jian’ou City, which is a typical bamboo hometown in China. 209 soil samples were collected from Moso bamboo stands and then analyzed for SOC, bulk density (BD), pH, cation exchange capacity (CEC), and gravel content (GC) based on spatial distribution. The spatial variability of SOC was then examined using geostatistics. A Kriging map was produced through ordinary interpolation and required sample numbers were calculated by classical and Kriging methods. An aggregated boosted tree (ABT) analysis was also conducted. A semivariogram analysis indicated that ln(SOC) was best fitted with an exponential model and that it exhibited moderate spatial dependence, with a nugget/sill ratio of 0.462. SOC was significantly and linearly correlated with BD (r = −0.373**), pH (r = −0.429**), GC (r = −0.163*), CEC (r = 0.263**), and elevation (r = 0.192**). Moreover, the Kriging method requires fewer samples than the classical method given an expected standard error level as per a variance analysis. ABT analysis indicated that the physicochemical variables of soil affected SOC variation more significantly than spatial variables did, thus suggesting that the SOC in Moso bamboo forests can be strongly influenced by management practices. Thus, this study provides valuable information in relation to sampling strategy and insight into the potential of adjustments in agronomic measure, such as in fertilization for Moso bamboo production.     

综合土地利用及空间异质性的土壤有机碳空间插值模型

土壤有机碳库是陆地碳库的重要组成部分,土壤有机碳库及其动态变化对陆地生态系统碳循环有着重要的影响.土壤有机碳密度(SOCD)是土壤碳储量的重要参数,也是评价农田土壤质量的重要指标,准确预测区域SOCD空间分布对发展精确农业有重要意义.本文使用江汉平原地区242个农田土壤样本数据,探究平原地区土地利用类型对SOCD空间分布的影响,以及当SOCD空间分布规律呈现空间异质性且存在空间异常值的情况下,虚拟变量回归克里格法(DV_RK)、均值中心化克里格法(MC_OK1)和中位数中心化克里格法(MC_OK2)这3种结合土地利用的克里格法在SOCD空间预测中的应用.结果表明: 土地利用方式的差异是研究区水田和水浇地SOCD存在空间异质性的原因之一,导致SOCD存在空间非平稳特征,降低了普通克里格法(OK)的预测精度;而DV_RK、MC_OK1和MC_OK2在消除了由土地利用引起的SOCD空间异质性对建模的影响后,模型的稳定性提升,其预测精度均高于OK,其中,MC_OK2的模型可靠程度、预测精度和对SOCD总方差的解释能力最优.因此,土地利用类型作为易获取的辅助变量,可以有效减弱空间异质性和空间异常值对SOCD空间插值的影响,提升模型精度,降低不确定性,并与MC_OK2结合生成更高精度的SOCD空间分布图,帮助揭示SOCD空间分异规律,指导农业生产.     

红壤区土壤有机质和全氮含量的空间预测方法

如何利用有限的土壤采样点准确预测土壤属性一直是研究的热点。近些年来,结合辅助信息的克里格空间插值应用广泛,但将土地利用类型信息作为辅助变量提高预测精度的研究鲜有报道。以中国南方红壤丘陵区的余江县为案例区,用网格法采集254个土壤样品,通过普通克里格(OK)和以土地利用方式为辅助变量的克里格(KLU)方法对土壤有机质(SOM)和土壤全氮(STN)进行空间预测,并通过102个验证点比较了两种方法的预测精度。分析表明KLU对SOM和STN的预测值与实测值的相关系数(rSOM=0.786,rSTN=0.803)明显高于OK(rSOM′=0.224,rSTN′=0.307);OK对SOM和STN的预测RMSE分别为12.48g.kg-1和0.64g.kg-1,而KLU的预测RMSE分别为6.86g.kg-1和0.37g.kg-1,仅为前者的55%和58%。可见,KLU对研究区SOM和STN的预测精度均大幅提高。同时分析也表明,KLU对不同土地利用方式SOM和STN预测精度的提高幅度存在差异,其中对旱地预测精度的提高幅度最大,对林地预测精度的提高幅度最小,水田则介于两者之间。研究表明,KLU是南方红壤丘陵区进行SOM和STN空间预测的现实和高效方法。     

南四湖区农田土壤有机质和微量元素空间分布特征及影响因素

基于地统计学和GIS技术相结合的方法,研究了南四湖区农田土壤有机质和微量元素的空间分布特征及其影响因素。结果表明,土壤有机质和微量元素均属中等变异程度,除硼符合正态分布外,其余土壤属性均符合对数正态分布。结构分析表明,除硼为纯块金效应外,土壤有机质和其它微量元素空间自相关性较强,其中结构性因素起主导作用。克里格插值结果表明,土壤有机质分布总体趋势为由北向南逐渐降低,锰、铜、锌分布总体趋势为中部高,南北两端低。影响因素分析表明,土壤类型、耕层质地、坡度、土地利用类型和地貌类型对土壤有机质均有显著影响。土壤类型主要是由于成土母质的差异影响土壤有机质的高低与分布,随质地由砂变粘、坡度由低变高,土壤有机质含量逐步升高,田间管理水平的差异是造成不同土地利用类型下土壤有机质含量差异的主要原因。微量元素中,除硼不受影响外,铁、锰、铜和锌与土壤类型、耕层质地、坡度、土地利用类型和地貌类型密切相关。     

不同干扰程度下土壤有机质空间最优插值法研究

选择最优空间插值方法对明确不同干扰程度下干旱区土壤有机质空间分布规律具有重要意义。通过对新疆阜康市土壤采样分析,分别运用普通克里格法(Ordinary Kriging, OK)、反距离权重法(inverse distance weight, IDW)、径向基函数法(radial basis function, RBF)、局部多项式法(local polynomial, LPI)4种插值方法,描述不同人为干扰程度下干旱区土壤有机质空间分布特征,以探讨其最优空间插值方法。结果表明:①随干扰程度增加,土壤有机质含量降低,空间变异性由弱到中等,变异系数由9.27%增加至28.7%;同时,土壤有机质空间自相关性逐渐降低,从无人为干扰区、人为干扰区到重度人为干扰区分别呈强烈、中等、弱相关关系,即干扰程度越强受随机因素作用越大。②虽然4种方法的插值精度均随干扰程度的增强而降低。但OK法在空间结构性较强的无人为干扰区插值效果最好,R~2为0.625;在人为干扰区及重度人为干扰区RBF法的插值精度均最高,R~2分别为0.562和0.434。该结果为寻找适合于不同干扰程度下干旱区土壤有机质的空间插值方法提供一定的科学参考。     

Three-Dimensional Mapping of Soil Organic Carbon by Combining Kriging Method with Profile Depth Function

Understanding spatial variation of soil organic carbon (SOC) in three-dimensional direction is helpful for land use management. Due to the effect of profile depths and soil texture on vertical distribution of SOC, the stationary assumption for SOC cannot be met in the vertical direction. Therefore the three-dimensional (3D) ordinary kriging technique cannot be directly used to map the distribution of SOC at a regional scale. The objectives of this study were to map the 3D distribution of SOC at a regional scale by combining kriging method with the profile depth function of SOC (KPDF), and to explore the effects of soil texture and land use type on vertical distribution of SOC in a fluvial plain. A total of 605 samples were collected from 121 soil profiles (0.0 to 1.0 m, 0.20 m increment) in Quzhou County, China and SOC contents were determined for each soil sample. The KPDF method was used to obtain the 3D map of SOC at the county scale. The results showed that the exponential equation well described the vertical distribution of mean values of the SOC contents. The coefficients of determination, root mean squared error and mean prediction error between the measured and the predicted SOC contents were 0.52, 1.82 and -0.24 g kg^-1 respectively, suggesting that the KPDF method could be used to produce a 3D map of SOC content. The surface SOC contents were high in the mid-west and south regions, and low values lay in the southeast corner. The SOC contents showed significant positive correlations between the five different depths and the correlations of SOC contents were larger in adjacent layers than in non-adjacent layers. Soil texture and land use type had significant effects on the spatial distribution of SOC. The influence of land use type was more important than that of soil texture in the surface soil, and soil texture played a more important role in influencing the SOC levels for 0.2-0.4 m layer.     

塔里木盆地北缘盐土有机碳含量的贝叶斯地统计预测

准确预测土壤有机碳的空间分布,对于土壤资源开发和保护、应对气候变化和生态系统健康都具有重要意义.本文以塔里木盆地北缘盐土1300 m×1700 m样地为试验区,采集5～10 cm深度土壤样品144个,构建土壤有机碳含量的贝叶斯地统计空间预测模型,并以普通克里格、序惯高斯模拟和逆距离加权方法为对照,评价贝叶斯地统计对土壤有机碳含量的预测性能.结果表明: 研究区土壤有机碳含量处于1.59～9.30 g·kg^-1,平均值为4.36 g·kg^-1,标准偏差为1.62 g·kg^-1;半方差函数符合指数模型,空间结构比参数值为0.57;利用贝叶斯地统计方法,获得了土壤有机碳含量的空间分布图以及评价预测不确定性的预测方差、上95%分位数、下95%分位数分布图;与普通克里格、序惯高斯模拟和逆距离加权方法相比,贝叶斯地统计方法具有更高的土壤有机碳含量空间预测精度,显示出该方法对土壤有机碳含量预测的优越性.     

长白山低山区森林土壤有机碳及养分空间异质性

以吉林延边汪清林业局金仓林场境内森林土壤为对象,采用多元线性回归方法和地统计学回归克里格方法,研究了土壤有机碳及养分的垂直分布规律,预测了其空间分布,并对预测结果进行插值.结果表明: 0～60 cm深度土壤有机碳密度为(16.14±4.58) kg·m^-2.随土壤深度增加,土壤有机碳含量、有机碳密度以及土壤全N、全P、全K、有效P及速效K含量都呈减小趋势,其中不同土层间土壤有机碳含量、有机碳密度差异显著(P<0.01).0～60 cm土层土壤有机碳含量和碳密度的拟合方程中,地形因子中高程和坡向余弦值是最优的拟合因子,方程的决定系数分别为0.34和0.39(P<0.01).0～20和0～60 cm土层的半方差函数模型分别为高斯模型和指数模型,利用回归克里格插值方法得到土壤有机碳的空间分布图.与普通克里格法相比,回归克里格法的空间预测精度改进了18%～58%.利用回归克里格插值方法预测了土壤全N的空间分布特征.     

江西兴国水土流失治理区土壤有机质动态变化

在ArcGIS8.1软件系统平台上,用普通克立格和概率克立格等多种方法研究潋水河流域土壤有机质含量的动态变化特征。结果表明,潋水河流域106个样点的土壤有机质平均含量增加,由1981年的18.16±7.23g/kg增加到2002年的19.14±11.25g/kg;2002年土壤有机质含量为(30.0～40.0g/kg)、(20.0～30.0g/kg)级地的面积扩大,所占流域面积比分别较1981年增加11.38%和28.54%。近20年来该流域内水田、旱地和林地的土壤有机质平均增加量分别为2.3g/kg、3.5g/kg和3.1g/kg,坡度≤2°、2～6°、6～15°、15～25°和>25°的土壤有机质平均增加量分别为1.8g/kg、2.6g/kg、3.3g/kg、3.6g/kg和4.0g/kg。结合概率克立格法和加权平均法探讨了<10.0g/kg含量水平下区域土壤有机质的概率分布特征,其几何平均概率由1981年的0.2008下降到2002年的0.1503。     

基于局域模型的凉水国家自然保护区土壤全氮空间分布

以凉水国家自然保护区激光雷达数据为基础,建立数字高程模型,提取基本地形属性如坡度、坡向和复合地形属性湿度指数和相对径流强度指数等,在成土因素学说基础上,对全氮含量(TN)进行地理加权回归建模(GWR),同时运用反距离加权(IDW)、普通克里格(OK)和泛克里格(UK)对TN进行空间插值.结果表明： 对于研究区TN的预测,GWR模型预测精度(77.4%)高于其他3种空间插值方法,IDW预测精度(69.4%)高于OK(63.5%)和UK(60.6%)的预测精度.利用GWR模型预测研究区域土壤TN平均达到4.82 g·kg^-1;在高海拔、地形湿度大以及相对径流强的区域,土壤TN相对较高.对预测结果进行探讨发现,不同坡位、坡向的土壤TN也存在一定差异.因此,基于地形属性的局域模型是土壤属性空间分布预测的更为有效的方法.     

广东山区土壤有机碳空间变异的尺度效应

研究土壤有机碳的尺度效应能够为区域生态环境保护和确定合理的土壤取样间距提供科学依据。采用土壤类型法估算了广东山区表层(0-20 cm)和全剖面(0-100 cm)土壤有机碳密度,选择4条采样带,获取采样间距为250 m的土壤有机碳密度序列,并利用离散小波变换工具对其进行多尺度分解,得到2×250 m、2²×250 m、2³×250 m、2⁴×250 m、2⁵×250 m和2⁶×250 m 6个分解尺度上的小波信息,计算小波信息方差。结果表明:土壤有机碳密度具有较强的空间异质性,其空间异质性的大小受控于不同尺度下土壤有机碳密度分布格局的主导因子影响程度;整体上在大于等于1 km的尺度,其空间异质性较强;各个样带特征尺度的差异与各样带的土壤和植被类型、地貌特征以及土地利用方式、耕作管理方式等人类活动干扰强度有关。     

长白山原始阔叶红松林土壤有机质组分小尺度空间异质性

土壤有机质(SOM)对于维持生态系统生产力具有非常重要的意义,有机质的组成、空间分布和空间关联性是影响和控制诸多生态系统过程的重要因素。应用地统计学方法,对长白山原始阔叶红松林局部尺度内0—20 cm土壤有机质与活性有机质的空间异质性进行了研究,并通过交叉半方差分析探讨了二者之间的相关性。研究结果表明:(1)总体上来说,土壤有机碳(SOC)、全氮(TN)、颗粒态有机碳(POC)和颗粒态有机氮(PON)空间异质性较小;而土壤微生物量碳(MBC)、微生物量氮(MBN)和表层(0—10 cm)溶解性有机碳(DOC)的空间异质性较大;(2)SOC、TN、MBC、DOC、POC和PON随着深度的增加空间自相关性增加;而溶解性有机氮(DON)的空间自相关性随深度的增加变化不大;(3)SOC与TN在表层和下层(10—20 cm)均存在空间上的正相关关系;(4)SOC、TN在表层和下层分别与MBC、MBN、DOC、DON和POC呈空间上的正相关性,但是与PON之间的空间相关关系较差;(5)不同土层深度的土壤活性有机质之间的相关关系存在差异。在表层,除POC,PON外,其余土壤活性有机质组分在空间上两两相关;但是随着土壤深度的增加,活性有机质变量之间在空间上两两相关。研究结果表明土壤有机质组分在长白山原始阔叶红松林小尺度内存在不同空间异质性和空间关联性,这为人们更好的理解森林生态系统功能(如土壤养分循环)提供了重要的理论依据。     

Variation of soil carbon pools in Pinus sylvestris plantations of different ages in north China

Plantations play an important role in absorbing atmospheric CO₂ and plantation soil can serve as an important carbon (C) sink. However, the stocks and dynamics of soil C in differently aged plantation forests in north China remain uncertain. In this study, we measured soil inorganic carbon (SIC), soil organic carbon (SOC) and total nitrogen content (STN), the light (LF) and heavy fractions (HF) of soil organic matter (SOM) to a depth of 1 m in 3 different ages (10-, 30-, 40-year-old) of Pinus sylvestris var. mongolica (Mongolia pine) plantations in 2011 and 2012. Soil pH, texture and moisture were also measured to explore the causes of SOC dynamics for different stand ages. Our results showed that no significant difference in SIC content was observed at different soil depths. As forest age increases, SIC content as well as the C and N content in SOM, LF and HF initially rose and then decreased, while the LF in SOC initially decreased and then increased. Although the C:N ratio of SOC and HF did not significantly change, the C:N ratio of LF increased with depth. SOC dynamics at different stand ages were significantly correlated with soil moisture and clay content. Soil pH and moisture explained 58.63% of the overall variation of SOC at different depths. Moreover, the SOC increased during the early stage of afforestation, mostly because of the increase in recalcitrant C; however, the decrease of SOC with increasing stand age was also mainly affected by C loss in the recalcitrant C pool.     

利用辅助变量对污染土壤锌分布的克里格估值

采用协同克里格及与回归相结合的克里格方法,以36个下层(10～20 cm)土壤锌数据为目标变量、另36个下层土壤锌数据为校验数据、72个上层(0～10 cm)土壤锌为辅助变量,对沈阳市南郊某有色金属加工厂附近农田0.1 mol·L^-1 HCl浸提态土壤锌进行插值分析,并对这两种利用辅助变量的克里格方法在土壤锌空间分布研究中的适用性进行评价.结果表明,与回归相结合的克里格的估值效果明显优于协同克里格及普通克里格法.结合回归模型的变异函数理论模型决定系数较大、残差较小,估值精度比普通克里格法提高4%,且基于回归克里格的土壤锌分布图与利用72个样点普通克里格插值图具有高度的相似性.而协同克里格与普通克里格相比未表现出明显优势.借助辅助变量,采用基于回归模型的克里格方法是进行土壤重金属空间分布估值的一种有效方法.     

Assessing the Impact of Afforestation on Soil Organic C Sequestration by Means of Sequential Density Fractionation

Afforestation is a prevalent practice carried out for soil recovery and carbon sequestration. Improved understanding of the effects of afforestation on soil organic carbon (SOC) content and dynamics is necessary to identify the particular processes of soil organic matter (SOM) formation and/or decomposition that result from afforestation. To elucidate these mechanisms, we have used a sequential density fractionation technique to identify the transfer mechanisms of forest derived C to soil fractions and investigate the impact of afforestation on SOC sequestration. Surface soil samples from continuous maize crop land (C4) and forest land (C3), which had been established 5, 12 and 25 yr, respectively, on the Northeast China Plain were separated into five density fractions. SOC, nitrogen (N) concentration and δ13C data from the three forests and adjacent cropland were compared. Afforestation decreased SOC concentration in the < 2.5 g cm-3 fractions from 5 yr forest sites, but increased SOC content in the < 2.0 g cm-3 fractions from 25 yr forest sites. Afforestation did not affect soil mass distribution, SOC and N proportional weight distributions across the density fractions. The < 1.8 g cm-3 fractions from 12 and 25 yr forests showed higher C/N and lower δ13C as compared to other fractions. Incorporation of forest litter-derived C occurred from low density (< 1.8 g cm-3) fractions to aggregates of higher density (1.8-2.5 g cm-3) through aggregate recombination and C transport in the pore system of the aggregates. Some forest litter-derived C could transfer from the light fractions or directly diffuse and adsorb onto mineral particles. Results from this study indicate that microaggregate protection and association between organic material and minerals provide major contribution to the SOC sequestration in the afforested soil system.     

Mapping soil organic matter using the topographic wetness index: A comparative study based on different flow-direction algorithms and kriging methods

Terrain attributes derived from digital elevation models have been used widely for mapping soil organic matter (SOM). Among these attributes, the topographic wetness index (TWI), an index for quantitatively indicating the balance between water accumulation and drainage conditions at the local scale, has been shown to correlate with SOM. However, TWIs used in most studies are calculated using a single-flow-direction (SFD) algorithm, which assumes that all water from a grid cell flows into only one neighboring cell. This assumption is not always valid, especially in areas with low relief where movement of water may be divergent. To overcome this SFD limitation, a multiple-flow-direction (MFD) algorithm has been developed, which distributes flow from a grid cell to several downslope neighbors. In this study we compared the effect of TWI calculations based on SFD and MFD in predictive mapping of SOM by incorporating them into different kriging methods over a 51.76 km² area in Nenjiang County of northeastern China. We found that the MFD-based TWI was better correlated with SOM than was the SFD-based index. We then compared the accuracies of SOM maps which were derived from MFD-based TWI and SFD-based TWI incorporated by ordinary kriging (OK), simple kriging with varying local means (SKlm), kriging with external drift (KED) and collocated cokriging (CC). The MFD-based TWI, used as a secondary variable in SKlm and CC, outperforms the SFD-based TWI. For the different kriging methods, CC (incorporating either MFD-based TWI or SFD-based TWI) showed the best performance, and OK generated a better result than SKlm and KED. Both the MFD-based TWI and SFD-based TWI proved to be incompatible with KED and SKlm due to their numerical instability caused by the rough TWI surfaces. Among all predictive methods, CC incorporating the MFD-based TWI produced the best results. This is because: (1) the MFD-based TWI is best able to indicate quantitatively soil moisture and therefore has the strongest correlation with SOM; (2) CC is capable of utilizing effectively the spatial auto-correlation of SOM and the cross-correlation between SOM and the MFD-based TWI.