基于空间模拟退火算法的最优土壤采样尺度选择研究

以研究区0.5 km×0.5 km(尺度a)网格的7050个样点为基础,分别得到1 km×1 km网格的1757个样点(尺度b),2 km×2 km网格的444个样点(尺度c),4 km×4 km网格的110个样点(尺度d),以土壤有机质(SOM)为目标属性,运用模拟退火算法对4种采样尺度的土壤样点进行优化选择,确定区...     

Land Use and Soil Organic Carbon in China’s Village Landscapes

Village landscapes, which integrate small-scale agriculture with housing, forestry, and a host of other land use practices, cover more than 2 million square kilometers across China. Village lands tend to be managed at very fine spatial scales (≤ 30 m), with managers both adapting their practices to existing variation in soils and terrain (e.g., fertile plains vs. infertile slopes) and also altering soil fertility and even terrain by terracing, irrigation, fertilizing, and other land use practices. Relationships between fine-scale land management patterns and soil organic carbon (SOC) in the top 30 cm of village soils were studied by sampling soils within fine-scale landscape features using a regionally weighted landscape sampling design across five environmentally distinct sites in China. SOC stocks across China’s village regions (5 Pg C in the top 30 cm of 2 × 10 6 km 2 ) represent roughly 4% of the total SOC stocks in global croplands. Although macroclimate varied from temperate to tropical in this study, SOC density did not vary significantly with climate, though it was negatively correlated with regional mean elevation. The highest SOC densities within landscapes were found in agricultural lands, especially paddy, the lowest SOC densities were found in nonproductive lands, and forest lands tended toward moderate SOC densities. Due to the high SOC densities of agricultural lands and their predominance in village landscapes, most village SOC was found in agricultural land, except in the tropical hilly region, where forestry accounted for about 45% of the SOC stocks. A surprisingly large portion of village SOC was associated with built structures and with the disturbed lands surrounding these structures, ranging from 18% in the North China Plain to about 9% in the tropical hilly region. These results confirmed that local land use practices, combined with local and regional variation in terrain, were associated with most of the SOC variation within and across China’s village landscapes and may be an important cause of regional variation in SOC.     

Methods for estimating types of soil organic carbon and their application to surveys of UK urban areas

The occurrence of substantial quantities of black carbon (BC) in urban soil due to local dispersal following incomplete combustion of fossil fuel complicates the determination of labile soil organic carbon (SOC). Estimates of SOC content were made from loss on ignition (LOI) analyses undertaken on samples (0–15 cm depth) from comprehensive soil geochemical surveys of three UK urban areas. We randomly selected 10 samples from each decile of the LOI distribution for each of the surveys of Coventry (n = 808), Stoke‐on‐Trent (n = 737) and Glasgow (n = 1382) to investigate the proportions of labile SOC and BC. We determined their total organic carbon (TOC) and BC contents, and by difference the labile SOC content, and investigated the linear relationship of the latter with SOC estimates based on LOI analyses. There was no evidence for a difference in the slope of the regression for the three urban areas. We then used a linear regression of labile SOC based on LOI analyses (r² = 0.81) to predict labile SOC for all survey samples from the three urban areas. We attribute the significantly higher median BC concentrations in Glasgow (1.77%, compared with 0.46 and 0.59% in Coventry and Stoke‐on‐Trent) to greater dispersal of coal ash across the former. An analysis of the 30 samples showed that LOI at 450 °C accounts for a consistent proportion of BC in each sample (r² = 0.97). Differences between TOC (combustion at 1050 °C after removal of inorganic carbon) and an LOI estimate of SOC may be a cost‐effective method for estimation of BC. Previous approaches to estimation of urban SOC contents based on half the mean SOC content of the equivalent associations under pasture, underestimate the empirical mean value.     

Evaluation of the spatial pattern of surface soil water content of a karst hillslope in Southwest China using a state-space approach

This study aims to evaluate the effects of soil physicochemical properties and environmental factors on the spatial patterns of surface soil water content (SWC) based on the state-space approach and linear regression analysis. For this purpose, based on a grid sampling scheme (10 m × 10 m) applied to a 90 m × 120 m plot located on a karst hillslope of Southwest China, the SWC at 0–16 cm depth was measured 3 times across 130 sampling points, and soil texture, bulk density (BD), saturated hydraulic conductivity (K_s), organic carbon (SOC), and rock fragment content as well as site elevation (SE) were also measured at these locations. Results showed that the distribution pattern of SWC could be more successfully predicted by the first-order state-space models (R² = 67.5–99.9% and RMSE = 0.01–0.14) than the classic linear regression models (R² = 10.8–79.3% and RMSE = 0.11–0.24). The input combination containing silt content (Silt), K_s, and SOC produced the best state-space model, explaining 99.9% of the variation in SWC. And Silt was identified as the first-order controlling factor that explained 98.7% of the variation. In contrast, the best linear regression model using all of the variables only explained 79.3% of variation.     

Use of different chemometric approaches for an estimation of soil properties at field scale with near infrared spectroscopy

Visible and near infrared spectroscopy (vis‐NIRS) may be useful for an estimation of soil properties in arable fields, but the quality of results are often variable depending on the applied chemometric approach. Partial least squares regression (PLSR) may be replaced by approaches which employ supervised learning methods or variable selection procedures in order to increase the proportion of informative wavelengths used in the estimation procedure, to reduce the noise of the spectra and to find the best fitting solution. Objectives were (1) to compare the usefulness of PLSR with either PLSR combined with a genetic algorithm (GA‐PLSR) or support vector machine regression (SVMR) for an estimation of soil organic carbon (SOC), total nitrogen (N), pH, cation exchange capacity (CEC) and soil texture for surface soils (0–5 cm, n = 144) of an arable field in Bangalore (India) and (2) to test and optimize different calibration strategies for GA‐PLSR for an improved estimation of soil properties. PLSR was useful for an estimation of SOC, N, sand and clay. In the cross‐validation (n = 96), accuracies of estimated soil properties generally decreased in the order GA‐PLSR > SVMR > PLSR. However, the order of estimation accuracies for the random validation sample (n = 48) changed to SVMR > GA‐PLSR > PLSR for SOC, N, pH, and CEC, whereas for clay the order changed to SVMR > PLSR > GA‐PLSR. A sequential procedure, which used the most frequently selected wavelengths of the GA‐PLSR runs, proved to be useful for an improved estimation of SOC and N. Overall, SVMR especially improved estimations of SOC and clay, whereas GA‐PLSR was particularly useful for SOC and N and it was the only approach which successfully estimated CEC in cross‐validation and validation.     

机器学习用于耕地土壤有机碳空间预测对比研究——以亚热带复杂地貌区为例

耕地土壤有机碳(SOC)是土壤质量的重要指标,也是生态系统健康的重要表征。当前机器学习(Machine Learning, ML)用于SOC数字制图日益热门,但不同算法在高空间分辨率SOC数字制图中的对比研究尚有欠缺。本研究以福建省东北部复杂地形地貌区为例,采用10m空间分辨率Sentinel-2影像数据,选取地形、气候、遥感植被变量为驱动因子,重点分析当前常用的机器学习算法——支持向量机(SupportVector Machine,SVM)、随机森林(RandomForest,RF)在SOC预测中的差异,并与传统普通克里格模型(Ordinary Kriging, OK)进行比较。结果表明:基于地形、遥感植被因子和气候因子构建的RF模型表现最佳(RMSE=2.004,r=0.897),其精度优于OK模型(RMSE=4.571, r=0.623),而SVM模型预测精度相对最低(RMSE=5.190, r=0.431);3种模型预测SOC空间分布趋势总体相似,表现为西高东低、北高南低,其中RF模型呈现的空间分异信息更加精细;最优模型反演得到耕地土壤有机碳平均含量为15.33 g·kg-1; RF模型和SVM模型变量重要性程度表明:高程和降水是影响复杂地貌区SOC空间分布的重要变量,而遥感植被因子重要性程度低于高程。     

短距离样点对土壤呼吸空间变异预测精度的影响

不同采样设计会对土壤呼吸空间变异特征的预测精度产生重要影响。本研究选取黄淮海平原北部潮土区1 km×1 km夏玉米样地,在7×7单元规则格网(样点间距167 m)、完全随机(样点平均间距433 m)以及3×3单元规则格网+完全随机(样点平均间距405m)3种布点方式的基础上,保持样本总量(49)不变,以占总样点2%～14%的短距离样点(样点间距4m)随机替换原方案相应样点个数的方法优化布点方式,应用普通克里金法插值,以均方根误差(RMSE)和确定系数(R2)作为验证指标,检验基于3种布点方式设置的短距离样点对土壤呼吸空间变异预测精度的影响。结果表明:研究区土壤呼吸平均速率为2.65μmol·m?2·s?1,空间分布均呈西高东低,表现出中等程度变异。采样设计对土壤呼吸空间分布的预测精度影响显著,基于3种布点方式设置短距离样点可提高预测精度7%～13%。无短距离样点替换时,规则格网+完全随机的布点方式最优,比完全随机布点和规则格网布点的空间插值预测精度分别提高10%和22%;设置短距离样点替换后,在最优布点方式(规则格网+完全随机)中,对土壤呼吸空间变异的预测精度可再提高4%～7%,其中短距离样点个数占样本总量10%对土壤呼吸空间变异预测精度的提高最为明显。研究发现,基于相同的样本数量设置短距离样点可增加区域范围内样点密度,提高土壤呼吸空间变异预测精度及试验结果的可靠性。因此,在黄淮海平原北部潮土区100 hm2尺度的夏玉米样地中,规则格网+完全随机+10%短距离样点的布点方式是预测土壤呼吸空间变异最适宜的采样布点方式。     

县域尺度红壤丘陵区水稻土有机碳模拟

区域尺度土壤有机碳储量的时空变化及其管理是全球气候变化和农业可持续发展研究的重要内容。本文以中亚热带红壤丘陵区的江西省余江县为例,基于12a的长期试验和1998年、2001年的野外定位采样对比研究,利用反硝化分解模型?DNDC(Denitrification-Decomposition)在田块和县域尺度研究了县域尺度表层(0~20 cm)水稻土有机碳储量的时空变化规律。结果表明,以长期试验数据验证,DNDC模型可以较好地模拟水稻土表层有机碳的长期动态变化。2001年农田水稻土(面积为3.6×108m2)表层(0~20 cm)有机碳总储量为2.9×109kg,平均土壤有机碳密度为6.0 kg m-2。1998年至2001年余江县水稻土表层土壤有机碳库逐年增加,年际平均变化量为3.0×107kg。通过对余江县水稻田模拟不同碳投入的情景,分析预测1998年至2017年土壤有机碳储量,种植绿肥提高秸秆还田比率同时减少化肥的投入,可有效地增加红壤区域有机碳蓄积。     

Prediction of volumetric soil organic carbon from field‐moist intact soil cores

This study investigated the potential for visible–near‐infrared (vis–NIR) spectroscopy to predict locally volumetric soil organic carbon (SOC) from spectra recorded from field‐moist soil cores. One hundred cores were collected from a 71‐ha arable field. The vis–NIR spectra were collected every centimetre along the side of the cores to a depth of 0.3 m. Cores were then divided into 0.1‐m increments for laboratory analysis. Reference SOC measurements were used to calibrate three partial least‐squares regression (PLSR) models for bulk density (ρ_b), gravimetric SOC (SOC_g) and volumetric SOC (SOC_v). Accurate predictions were obtained from averages of spectra from those 0.1‐m increments for SOC_g (ratio of performance to inter‐quartile (RPIQ) = 5.15; root mean square error (RMSE) = 0.38%) and SOC_v (RPIQ = 5.25; RMSE = 4.33 kg m^?3). The PLSR model for ρ_b performed least well, but still produced accurate results (RPIQ = 3.76; RMSE = 0.11 Mg m^?3). Predictions for ρ_b and SOC_g were combined to compare indirect and direct predictions of SOC_v. No statistical difference in accuracy between these approaches was detected, suggesting that the direct prediction of SOC_v is possible. The PLSR models calibrated on the 10‐cm depth intervals were also applied to the spectra originally recorded on a 1‐cm depth increment. While a bigger bias was observed for 1‐cm than for 10‐cm predictions (1.13 and 0.19 kg m^?3, respectively), the two populations of estimates were not distinguishable statistically. The study showed the potential for using vis–NIR spectroscopy on field‐moist soil cores to predict SOC at high depth resolutions (1 cm) with locally derived calibrations.     

多尺度土壤入渗特性的变异特征和传递函数构建

土壤入渗特性的变异特征具有明显空间依赖性和尺度效应,其多尺度上的参数估值是农田灌溉设计和管理的重要基础。该研究以在关中平原进行的52组双环入渗试验为基础,通过比较不同方法计算的标定因子对Kostiakov公式的标定效果,结合小波分析和通径分析方法识别并量化分析标定因子和土壤特性参数（土壤机械组成、容重、初始含水率和有机质含量）在多尺度的相关性,在此基础上分别利用多元线性回归（Multiple Linear Regression, MLR）、BP神经网络（BP Artificial Neural Network, BP-ANN）和支持向量机（Support Vector Machine, SVM）3种方法构建估算标定因子的土壤传递函数。结果表明,采用最小二乘法计算标定因子对Kostiakov公式的标定效果最优,所有测点标定后累积入渗量与实测值的均方根误差（Root Mean Square Error, RMSE）、平均偏差（Mean Bias Error, MBE）、相对误差绝对值均值（Mean Absolute Value of Relative Error, MARE）分别为1.83 cm、0.24 cm、21.2%;多尺度条件下,土壤容重、砂粒、黏粒和有机质含量组合是引起研究区域标定因子空间变化的主要变异源,其中标定因子与砂粒和有机质含量呈显著正相关关系,总通径系数分别为0.78和0.65,与黏粒和土壤容重呈显著负相关关系,总通径系数分别为?0.74和?0.68;采用SVM法构建估算标定因子的土壤传递函数精度最高,其验证集所得入渗量估算值与实测值具有较高的一致性,两者间的RMSE、MBE和MARE分别为1.92 cm、0.05 cm和27.6%,说明SVM法可用于构建估算标定因子的土壤传递函数。研究结果有助于揭示多尺度上土壤入渗特性的变异特征和解决入渗参数难以快速获取的问题。     

典型喀斯特高基岩出露坡地表层土壤有机碳空间异质性及其储量估算方法

本研究基于详尽、系统的土壤采样调查,研究了喀斯特高基岩出露坡地典型样地(100 m×100 m)内表层土壤(0~15 cm)有机碳(SOC)含量的空间异质性特征,并以土壤斑块加和法为基准,探讨了传统空间插值方法和基于岩石出露率、土深校正的空间插值方法在喀斯特高基岩出露地区土壤表层有机碳储量估算中的适用性。结果表明,研究区SOC和容重均值分别为75.5 g·kg-1和0.8 g·cm-3,变异系数分别为30.6%与47.3%,皆呈现中等变异;SOC半变异函数的最优拟和模型为指数模型,块金值和基台值分别为260.8与521.7,变程为52.5 m,其半变异函数分别在滞后距0~15.2 m与34.7~54.2 m范围内呈现明显的各向异性,说明在该尺度范围内微地貌与地形显著影响SOC的空间分布;利用土壤斑块加和法估算的样地表层SOC储量和碳密度分别为983.8 kg和0.1 kg·m-2,利用传统空间插值方法估算的表层SOC储量和碳密度分别为86 264.0 kg和8.6 kg·m-2,利用基于岩石出露率、土深校正的空间插值方法估算的表层SOC储量和碳密度分别为2 712.8 kg和0.3 kg·m-2。其中传统空间插值方法大大高估了喀斯特地区表层SOC储量和碳密度值,用该方法估算的SOC储量为该区SOC实际储量的87.7倍,其误估率为8 668.4%。说明传统地统计学方法不适合估算喀斯特高基岩出露坡地表层SOC储量及碳密度。而基于岩石出露率、土深校正的空间插值方法大大降低了估算喀斯特高基岩出露坡地表层SOC储量和碳密度的误差,为该区SOC实际储量及碳密度的2.7倍。说明校正后的地统计方法在估算该区高基岩出露坡地表层SOC储量时具有一定的适用性。以上研究表明,地统计方法是表示该区SOC空间分布的有效手段,但由于传统地统计方法难以精确拟合高基岩出露坡地土壤斑块的空间分布、微地貌特征、岩石出露率以及土层深度等信息,在估算同类坡地SOC储量和碳密度时必须修正估算公式以接近实际值。     

基于各向异性的区域土壤有机碳三维模拟与空间特征分析

为探索更加科学的土壤属性三维空间模拟方法,以各项同性三维普通克里格法为对比方法,采用均方根误差（root mean squared errors,RMSE）和标准化克里格方差（mean squared deviation ratio,MSDR）以及空间模拟方差图等,评价比较了各项同性和顾及各项异性的三维模拟方法的模拟效果。结果显示：三种方法模拟的土壤有机碳三维空间分布格局基本一致。随着土壤深度的不断增加,土壤有机碳含量较高的斑块逐渐减少,垂直方向上总体呈现出土体上部高下部低的格局。顾及各向异性能在一定程度上克服普通克里格法常出现的牛眼和趋中效应等缺陷问题。顾及各向异性基于Markov 的同位置协同格里格法模拟效果最佳。该法的 RMSE 值最小（1.6215）,相比于各项同性三维普通克里格法 RMSE提高将近50%,特异值覆盖比率最大（76.15%）,模拟精度最高,能够更好地突出波动性,体现特异值;该方法的 MSDR最接近1（1.4409）,且模拟的土壤有机碳质量分数总体方差均值最小（2.08）。研究成果将为区域土壤属性三维空间有效模拟提供方法参考。     

结合多等级代表性采样和模糊隶属度的区域土壤制图方法

High-resolution and detailed regional soil spatial distribution information is increasingly needed for ecological modeling and land resource management. For areas with no point data, regional soil mapping includes two steps: soil sampling and soil mapping. Because sampling over a large area is costly, efficient sampling strategies are required. A multi-grade representative sampling strategy, which designs a small number of representative samples with different representative grades to depict soil spatial variations at different scales,could be a potentially efficient sampling strategy for regional soil mapping. Additionally, a suitable soil mapping approach is needed to map regional soil variations based on a small number of samples. In this study, the multi-grade representative sampling strategy was applied and a fuzzy membership-weighted soil mapping approach was developed to map soil sand percentage and soil organic carbon(SOC) at 0–20 and 20–40 cm depths in a study area of 5 900 km2 in Anhui Province of China. First, geographical sub-areas were delineated using a parent lithology data layer. Next, fuzzy c-means clustering was applied to two climate and four terrain variables in each stratum. The clustering results(environmental cluster chains) were used to locate representative samples. Evaluations based on an independent validation sample set showed that the addition of samples with lower representativeness generally led to a decrease of root mean square error(RMSE). The declining rates of RMSE with the addition of samples slowed down for 20–40 cm depth, but fluctuated for 0–20 cm depth. The predicted SOC maps based on the representative samples exhibited higher accuracy, especially for soil depth 20–40 cm, as compared to those based on legacy soil data. Multi-grade representative sampling could be an effective sampling strategy at a regional scale. This sampling strategy, combined with the fuzzy membership-based mapping approach, could be an optional effective framework for regional soil property mapping. A more detailed and accurate soil parent material map and the addition of environmental variables representing human activities would improve mapping accuracy.     

煤矿区土壤有机碳含量的高光谱预测模型

可见—近红外光谱已被证明是一种快速、及时、有效的土壤有机碳含量预测工具。利用Field Spec4对济宁鲍店矿区的104个土壤样品进行光谱测量,采用Savitzky-Golay卷积平滑(SG)、多元散射校正(MSC)及数学变换等多种方式组合对光谱预处理,并运用偏最小二乘回归分析建立土壤有机碳含量预测模型,进而探讨煤矿区土壤有机碳含量的高精度预测方法。结果表明:(1)不同的光谱预处理方法对建模结果影响差异较大,建模结果以SG加MSC预处理再结合光谱反射率的一阶微分变换最优,建模R~2=0.86,RMSE=2.0g/kg,验证R~2=0.78,RMSE=1.81g/kg,RPD=2.69。(2)倒数和倒数的对数与土壤有机碳含量的相关性曲线接近重合,与反射率曲线成反比,但是建模效果远低于反射率;光谱反射率的一阶微分能明显提高500~600nm波段相关性。(3)光谱反射率随土壤有机碳的含量减少而增大,当有机碳含量较低时,其波谱的近红外波段反射率响应能力也随之降低,反射率直接建模难度加大。     

Spatial variability of soil organic carbon in a catchment of the Loess Plateau

Abstract

The issue of soil organic carbon (SOC) is of increasing concern. Because SOC, as an important soil component in farming systems, is essential for improving soil quality, sustaining food production and quality, and maintaining water quality and as a major part of the terrestrial carbon reservoir, it plays an important role in the global carbon cycle. In this paper, a total of 665 soil samples from different depths were collected randomly in the autumn of 2007, and the spatial variability of SOC content at a small catchment of the Loess Plateau was analysed using classical statistics and geo-statistical analysis. In nonsampled areas classical kriging was utilized for interpolation of SOC estimation. The classic statistical analysis revealed moderate spatial variability with all five layers of SOC-content. In addition, the average SOC content decreased with soil depth and the relationship can be modelled by an exponential equation (y=3.1795x ^?1.2015, R ²=0.9866) and all of the SOC-content data in the different depth were normally distributed. The geo-statistical analysis indicated a moderate spatial dependence in 0–60 cm, while in the 60–80 cm depth spatial dependence was strong. The semi-variogram could be fitted by an exponential model for 0–10 cm depth; by a spherical model for 10–20 cm depth and 60–80 cm depth; and by a Gaussian model for 20–60 cm depth. The range increases with increasing depth. In addition, classical kriging could successfully interpolate SOC content in the catchment. In general, the geo-statistics method on a watershed scale could be accurately used to evaluate spatial variability of the SOC content in the Loess Plateau, China.     

Neoformation of pedogenic carbonate and conservation of lithogenic carbonate by farming practices and their contribution to carbon sequestration in soil

Land use change, tillage practices and straw incorporation are known to affect soil organic carbon (SOC) as well as soil inorganic carbon (SIC) turnover in agricultural soils. SOC and SIC, particularly pedogenic carbonates (PC), were assessed in a semi‐humid region of China to a depth of 160 cm. δ¹³C values were used to calculate the percentage of PC and lithogenic carbonates (LC) in the total SIC. Over the 39‐y period of intensive agriculture including 14 y of tillage × straw experiment, three treatments, i.e ., tillage with wheat and maize straw return (TWM), tillage with wheat straw return (TW), and wheat and maize straw return with no‐tillage (WM) showed an increase of PC compared to a native plantation plot (NP). The significantly higher SOC stock via no‐tillage was limited to top 1 m soil and there was no significant difference between tillage and no‐tillage treatments at 0–160 cm depth. The changes of SOC caused by the tillage and maize straw addition were negligible compared to the gain in PC. Tillage, crop residues incorporation and irrigation played an important role in the turnover of PC and LC. SIC accumulation resulted from combination of neoformation of PC and conservation of LC. Neoformation of silicatic PC sequestered at least 0.49, 0.47, and 0.29 Mg C ha⁻¹ y⁻¹ in TWM, TW, and WM treatments, respectively, with reference to NP plot. We concluded that to evaluate the long term impacts of land use and farming practices on soil C storage, change of pedogenic and lithogenic carbonates and soil organic carbon in deeper soil profiles should be integrated on regional and global scales.     

江西省耕地土壤有机碳空间变异的主控因素研究

准确地获取省域尺度下土壤有机碳空间变异的主控因素对土壤碳调控以及全球环境保护具有重要意义。本文基于江西省2012年测土配方施肥项目采集的16 582个耕地表层(0~20 cm)土壤样点数据,探讨江西省耕地表层土壤有机碳空间变异的主控因素。运用普通克里格法、单因素方差分析与回归分析方法对比地形因子、耕地利用方式、成土母质、土壤类型、灌溉能力和秸秆还田方式对江西省土壤有机碳空间分布的影响。结果表明:(1)江西省土壤有机碳含量在5.22~40.31 g/kg之间,平均值为17.90 g/kg,变异系数为31.01%,呈中等程度的变异性。(2)经半方差分析,土壤有机碳的变程为30.6 km,空间自相关范围较小;块金效应值为12.49%,表明土壤有机碳空间变异受结构性因素影响大于随机性因素。(3)在空间分布上,高值区主要分布在萍乡市、新余市、南昌市、抚州市与景德镇市。(4)回归分析与单因素方差分析结果表明,地形因子、灌溉能力、成土母质、耕地利用方式、土壤类型和秸秆还田方式对土壤有机碳空间变异影响均显著(P0.05),但影响程度不一。秸秆还田方式对土壤有机碳空间变异的独立解释能力最高,为38.9%,是江西耕地表层土壤有机碳空间变异的主控因素。     

Distribution of soil organic carbon and phosphorus on an eroded hillslope of the rangeland in the northern Tibet Plateau, China

Some studies on the relationship between soil erosion and subsequent redeposition of eroded soils in the same field and soil quality have been conducted in croplands, yet few studies have revealed this relationship in rangelands. We selected a toposequence with a slope of 30% and a horizontal length of 342 m from the rangeland in the northern Tibet Autonomous Region, China (31°16′N, 92°09′E) to determine the relationship between soil erosion, soil organic carbon (SOC) content and available P patterns within a hillslope landscape. Soil samples for the determination of ¹³⁷Cs as well as SOC, available P and particle‐size fractions were collected at 20 m intervals along a transect of this hillslope. Soil redistribution was caused primarily by wind erosion at toe‐slope positions, but primarily by water erosion at the hillslope positions above the toe‐slope. In upper‐ and mid‐slope portions (0 m to 244 m horizontal length), SOC content is closely correlated to ¹³⁷Cs concentration (r = 0.74, P < 0.01, n= 15), suggesting that SOC distribution along the slope was similar to ¹³⁷Cs distribution, which itself was dependent on topographic changes. However, SOC contents in toe‐slope portions are less than those above the toe‐slope (i.e. upper‐ and mid‐slope portions), and the correlation between ¹³⁷Cs and SOC in the toe‐slope portion is weaker than that above the toe‐slope. A highly significant correlation (r = 0.72, P < 0.001, n= 20) between ¹³⁷Cs concentration and available P was found within the whole hillslope landscape, implying the distribution pattern of available P was somewhat different from that of SOC. We suggest that the distribution of SOC within the hillslope landscape is also affected by factors such as assimilation rates due to difference in grassland productivity at different points and different biological oxidation rates of carbon related to patterns of moisture distribution.     

空间自相关性对冬小麦种植面积空间抽样效率的影响

空间抽样是实现区域农作物面积高效估算的重要手段,农作物分布受自然条件等因素影响普遍存在空间自相关性,但以往针对空间相关性对农作物面积抽样效率的影响研究明显不足。该研究选取安徽省凤台县为研究区,通过2017年4月4景GF-1全色多光谱影像(Panchromatic and Multispectral, PMS)与Google Earth高空间分辨率影像相结合提取研究区冬小麦。设计10种抽样单元尺度、3种抽样外推方法、2种相对允许误差和5种样本布局方式,构建多种冬小麦面积空间抽样方案。利用全局莫兰指数(global Moran’s index)评价不种尺度下抽样单元内冬小麦面积比的空间自相关强度,分析空间自相关性对冬小麦面积抽样效率(抽样误差、样本容量和空间布局)的影响。研究结果表明,抽样单元内冬小麦面积比的空间自相关强度随单元尺度的增大而减小,全局莫兰指数相应地由0.75降至0.50。无论在何种尺度下抽样单元内冬小麦面积比都呈显著的空间正相关性;抽样外推冬小麦面积总体的误差随空间自相关强度的减小呈先减小后明显增大的趋势。在10种抽样单元尺度中,当抽样单元尺度为2000m且抽样比为5%时,无论采用何种抽样方法外推总体的误差均为最小(简单随机抽样、系统和分层抽样外推总体的相对误差分别为17.94%、9.48%和1.82%);当相对允许误差设计为5%时,简单随机抽样外推总体所需样本容量随空间自相关强度的降低从660降至56。而分层抽样的样本容量不受空间自相关性的影响;5种样本布局方式中,采用分层随机抽样方式外推冬小麦面积总体的平均相对误差、平均变异系数和均方根误差最小,分别为1.82%、3.19%和0.11×108 m2。该研究可为有空间自相关存在下的农作物面积空间抽样方案合理设计提供参考依据。     

Soil organic carbon in semiarid alpine regions: the spatial distribution,stock estimation,and environmental controls

Purpose

Soil organic carbon (SOC) in alpine regions is characterized by a strong local heterogeneity, which may contribute to relatively large uncertainties in regional SOC stock estimation. However, the patterns, stock, and environmental controls of SOC in semiarid alpine regions are still less understood. Therefore, the purpose of this study is to comprehensively quantify the stock and controls of SOC in semiarid alpine regions.

Materials and methods

Soils from 138 study sites across a typical semiarid alpine basin (1755–5051 m, ~1?×?10⁴ km²) are sampled at 0–10, 10–20, 20–40, and 40–60 cm. SOC content, bulk density, soil texture, and soil pH are determined. Both a classical statistical model (i.e., a multiple linear regression, MLR) and a machine learning technique (i.e., a random forest, RF) are applied to estimate the SOC stock at a basin scale. The study further quantifies the environmental controls of SOC based on a general linear model (GLM) coupled with the structural equation modeling (SEM).

Results and discussion

SOC density varies significantly with topographic factors, with the highest values occurring at an elevation zone of ~3400 m. The results show that the SOC is more accurately estimated by the RF compared to the MLR model, with a total stock of 219.33 Tg C and an average density of 21.25 kg C m^?2 at 0–60 cm across the study basin. The GLM approach reveals that the topography is seen to explain about 58.11% of the total variation in SOC density at 0–10 cm, of which the largest two proportions are attributable to the elevation (44.32%) and the aspect factor (11.25%). The SEM approach further indicates that, of the climatic, vegetative, and edaphic factors examined, the mean annual temperature, which is mainly shaped by topography, exerts the most significant control on SOC, mainly through its direct effect, and also, through indirect effect as delivered by vegetation type.

Conclusions

The results of this study highlight the presence of high stocks of organic carbon in soils of semiarid alpine regions, indicating a fundamental role played by topography in affecting the overall SOC, which is mainly attained through its effects on the mean annual temperature.