Hand-feel soil texture observations to evaluate the accuracy of digital soil maps for local prediction of soil particle size distribution: A case study in Central France

Digital maps of soil properties are now widely available. End-users now can access several digital soil mapping (DSM) products of soil properties, produced using different models, calibration/training data, and covariates at various spatial scales from global to local. Therefore, there is an urgent need to provide easy-to-understand tools to communicate map uncertainty and help end-users assess the reliability of DSM products for use at local scales. In this study, we used a large amount of hand-feel soil texture (HFST) data to assess the performance of various published DSM products on the prediction of soil particle size distribution in Central France. We tested four DSM products for soil texture prediction developed at various scales (global, continental, national, and regional) by comparing their predictions with approximately 3 200 HFST observations realized on a 1:50 000 soil survey conducted after release of these DSM products. We used both visual comparisons and quantitative indicators to match the DSM predictions and HFST observations. The comparison between the low-cost HFST observations and DSM predictions clearly showed the applicability of various DSM products, with the prediction accuracy increasing from global to regional predictions. This simple evaluation can determine which products can be used at the local scale and if more accurate DSM products are required.     

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.     

Digital mapping of cation exchange capacity using genetic programming and soil depth functions in Baneh region,Iran

This study evaluates the performances of a combination of genetic programming and soil depth functions to map the three-dimensional distribution of cation exchange capacity (CEC) in a semiarid region located in Baneh region, Iran. Using the conditioned Latin hypercube sampling method, the locations of 188 soil profiles were selected, which were then sampled and analyzed. In general, results showed that equal-area quadratic splines had the highest R², 89%, in fitting the vertical CEC distribution compared to power and logarithmic functions with R² of 81% and 84%, respectively. Our findings indicated some auxiliary variables had more influence on the prediction of CEC. Normalized difference vegetation index (NDVI) had the highest correlation with CEC in the upper two layers. However, the most important auxiliary data for prediction of CEC in 30–60 cm and 60–100 cm were topographic wetness index and profile curvature, respectively. Validation of the predictive models at each depth interval resulted in R² values ranging from 66% (0–15 cm) to 19% (60–100 cm). Overall, results indicated the topsoil can be reasonably well predicted; however, the subsoil prediction needs to be improved. We can recommend the use of the developed methodology in mapping CEC in other parts in Iran.     

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。     

Digital soil mapping of available water content using proximal and remotely sensed data

Two‐thirds of all irrigated agriculture in Australia is undertaken within the Murray–Darling Basin. However, climate change predictions for this region suggest rainfall will decrease. To maintain profitability, more will need to be done by irrigators with less water. In this regard, irrigators need to be aware of the spatial distribution of the available water content (AWC) in the root‐zone (i.e. 0.0–0.90 m). To reduce the cost, digital soil mapping (DSM) techniques are being used to map soil properties related to AWC (e.g. soil texture). The purpose of this study was to create a DSM of the AWC at the district scale. This is achieved by determining AWC by the difference between laboratory measured permanent wilting point (PWP) and field capacity (FC) and using pressure plate apparatus. The PWP and FC data are coupled to remote (i.e. gamma‐ray spectrometry) and proximal (i.e. EM38 and EM34) sensed data and two trend surface parameters. Using a hierarchical spatial regression (HSR), we predict PWP and FC across the areas of Warren and Trangie in the lower Macquarie valley, Australia. The reliability of the DSM of PWP and FC were compared using prediction precision (RMSE – root mean square error) and bias (ME – mean error). The best results were achieved using EM38‐v, EM34‐20, eU and eTh. The DSM map of AWC is consistent with known Pedoderms and provides a basis for agricultural water management.     

Digital mapping of soil ecosystem services in Scotland using neural networks and relationship modelling. Part 2: Mapping of soil ecosystem services

A framework for estimating the distribution of soil ecosystem service (ES) supply is described that is based on the concept of matrix multiplication. This approach enables relationships between fundamental soil variables and associated environmental characteristics to be linked to soil processes, and hence to ecosystem functions and ecosystem services. The parameterization of these relationships was achieved using a combination of data from the Scottish Soils Database and expert knowledge. Baseline data to allow mapping of processes, functions and services across Scotland is given by digital maps of soil classes. The matrix multiplication approach constrains the relationship linkages to linear relationships and ignores potential synergies between factors at each stage, but does provide a mechanism for relating fundamental soil characteristics to ecosystem services. The approach has been tested by developing maps of selected ecosystem services in Scotland and comparing these with existing maps of the same or similar ESs. While the values and their ranges differ in each case, the spatial distribution of services is similar. The proposed mechanism is extensible at every level and can also be used to explore the impacts of land management options on environmental characteristics. This is demonstrated by using the model to estimate impacts of liming on three ecosystem services: Agricultural Capability, Carbon Sequestration and Drinking Water Provision. The model is shown to produce reasonable estimation of the impacts of this management option. Further discussion of improvements to the system and its potential applications is given.     

基于进化遗传优化的自适应神经模糊推理新系统与地理信息系统的非洲山地土壤有机碳储量估算和绘图

Soil organic carbon (SOC) pool has the potential to mitigate or enhance climate change by either acting as a sink,or a source of atmospheric carbon dioxide (CO2) and also plays a fundamental role in the health and proper functioning of soils to sustain life on Earth.As such,the objective of this study was to investigate the applicability of a novel evolutionary genetic optimization-based adaptive neuro-fuzzy inference system (ANFIS-EG) in predicting and mapping the spatial patterns of SOC stocks in the Eastern Mau Forest Reserve,Kenya.Field measurements and auxiliary data reflecting the soil-forming factors were used to design an ANFIS-EG model,which was then implemented to predict and map the areal differentiation of SOC stocks in the Eastern Mau Forest Reserve.This was achieved with a reasonable level of uncertainty (i.e.,root mean square error of 15.07 Mg C ha-1),hence demonstrating the applicability of the ANFIS-EG in SOC mapping studies.There is potential for improving the model performance,as indicated by the current ratio of performance to deviation (1.6).The mnapping also revealed marginally higher SOC stocks in the forested ecosystems (i.e.,an average of 109.78 Mg C ha-1) than in the agro-ecosystems (i.e.,an average of 95.9 Mg C ha-1).     

水土保持林生长过程及碳密度的动态变化

刺槐是黄土高原主要的水土保持树种之一,对刺槐生长过程与碳密度动态变化的研究,是正确评价黄土高原水土保持林生态服务功能中亟待解决的关键问题.以山西吉县红旗林场马莲滩刺槐水土保持林为研究对象,采用样方(面积400 m2)调查和树干解析法,研究刺槐的生长过程,提出刺槐的树高生长模型、胸径生长模型、生物量与胸径的关系,并在此基础上模拟计算刺槐水土保持林生物量及碳密度的动态变化.结果表明:1)树龄12 a以前为刺槐树高和胸径的快速生长期,树高的平均生长速率可达0.63 m/a,胸径平均生长率为0.53 cm/a,树龄12a时树高可达6.9m、胸径5.19 cm、生物量10.18 t/hm2;2)利用建立的生物量预测模型得出,当树龄达到20 a时,刺槐水土保持林生物量的连年生长量最大,达2.99 t/hm2,树龄35 a时生物量的连年生长量曲线与平均生长量曲线相交,成为成熟林,总生物量达72.79 t/hm2,碳密度为35.52 t/hm2,对树龄35 a以上的刺槐水土保持林应该及时更新与改造.     

Spatial prediction of soil classes using digital terrain analysis and multinomial logistic regression modeling integrated in GIS: Examples from Vestfold County,Norway

The main objectives of this study were to model the relationship between WRB-1998 soil groups and terrain attributes and predict the spatial distribution of the soil groups using digital terrain analysis and multinomial logistic regression integrated in GIS in the Vestfold County of south-eastern Norway. A digital elevation model of 25 meter grid resolution was used to derive fifteen terrain attributes. A digitized soil map of thirteen WRB soil groups at the scale of 1:25,000 was used to obtain the reference soil data for model building and validation. First, the relationships between the soil groups and the terrain attributes were modeled using multinomial logistic regression. Then, the probability that a given soil type is present at a given pixel was determined from the logit models in ARCGIS to continuously map each soil group's spatial distribution. Elevation, flow length, duration of daily direct solar radiation, slope, aspect and topographic wetness index were found to be the most significant terrain attributes correlating with the spatial distribution of the soil groups. The prediction showed higher mean probability values for each soil group in the areas actually covered by that soil group compared to other areas, indicating the reliability of the prediction. However, the prediction performed poorly for soil groups that are not greatly influenced by topography but by other factors such as human activities.     

区域水土流失动态监测评价的大数据分析基础与对策

区域水土流失动态监测评价的目的是掌握水土流失状况及其防治成效,建成并运用大数据进行分析,可充分利用数据资源,提高监测成果的科学性、完整性和时效性.通过大数据采集与知识挖掘,构建区域水土流失动态监测评价数据集(或集合),再利用云网络计算平台,实现基于大数据的动态监测与分析评价.文章针对区域水土流失动态监测与评价的方法和数据基础,从完善顶层设计入手,实现大数据融合与共享,推进数据标准化建设,创新大数据和云网络服务方式,完善监测站网信息采集和大数据网络基础设施建设,探索基于大数据的区域水土流失动态监测评价方法和实现途径,并提出其大数据构成与分析流程,以及应用大数据分析的对策与建议.     

土壤有机质高光谱遥感和地统计定量预测

通过两种不同的尺度进行了土壤有机质含量的预测,在全县范围（大尺度）内运用地统计方法进行最优无偏内插估计,得到全县土壤有机质含量的空间分布格局。在小尺度高光谱Hyperion影像范围内,确定623.6 nm处反射率倒数之对数的一阶微分与564.4 nm处反射率倒数之对数的一阶微分的比值为土壤有机质的敏感变量,运用多元统计分析方法,确立各土壤有机质高光谱定量最佳反演模型,并把该模型应用于高光谱影像进行有机质含量定量填图,取得了很好的预测效果（R2=0.8684）。同时为了进行客观比较,基于同一尺度,利用30个样点进行地统计空间插值定量预测,比较两种预测结果,通过分析得出由于地统计学受到样点的数目、分布和间距情况以及内蕴假设的影响,其预测效果不如高光谱遥感反演模型。     

垄沟集雨系统Laio土壤水分动态随机模型参数敏感性分析及优化

水文模型参数的敏感性分析、优化和验证对提高模型计算精度和效率具有重要意义。为探讨Laio土壤水分动态随机模型(Laio模型)各参数在垄沟集雨系统的敏感性,同时,确定参数优化和模型验证的最佳方案,本文结合多因素敏感性分析法以及改进单纯形法(ISM)、粒子群优化算法(PSO)和混合粒子群优化算法(HPSO),利用中国气象局定西干旱气象与生态环境试验基地2012—2013年垄沟集雨燕麦生长季降雨、径流和土壤水分等实测数据,对垄沟集雨系统Laio模型的13个参数进行敏感性分析、优化和验证。结果表明,平均降水量α和凋萎系数s_w对土壤水分概率密度函数p(s)最敏感,p(s)对参数α的敏感性在低土壤含水率下更明显,对参数s_w的敏感性在高土壤含水率下更明显;3种算法(ISM、PSO和HPSO)的优化参数值均能对垄沟集雨系统土壤水分概率密度函数进行较好模拟,峰值(CPV)、峰值位置(PP)和95%置信区间(CI95%)实测值与模拟值的相对误差均小于10%,CM指数均大于0.5;同时,HPSO算法优化参数的模拟效果和收敛速度均显著优于PSO算法和ISM算法,能较显著克服ISM算法和PSO算法存在的缺陷。HPSO算法可作为垄沟集雨系统土壤水分动态随机模型参数优化的待选方案。     

利用CT数字图像和网络模型预测近饱和土壤水力学性质

在近饱和状态下,土壤的有效水力学性质主要取决于较大孔隙的结构特征,而这又决定了土壤中的优势流通道以及溶质的运移。基于孔隙形态学特征的网络模型可以很好的表现出较大孔隙的几何形态与拓扑特征对有效水力学性质的影响。本文通过对连续土壤切片CT图像的分析,定量获取了土壤孔隙的大小分布以及连通性参数。在此基础上建立了相关网络模型,在孔隙尺度上模拟了土壤中的水分运动过程并预测了近饱和土壤水力学性质。实验结果表明,虽然随机网络模型对室内填装土样本水力学性质的预测结果要优于相关网络模型,但是结合了实测孔隙形态特征的相关网络模型能够表现出田间原状土样本的双重孔隙度结构,其预测结果更符合实际的土壤结构特征。     

Evaluating a low‐cost portable NIR spectrometer for the prediction of soil organic and total carbon using different calibration models

This study aims to assess the performance of a low‐cost, micro‐electromechanical system‐based, near infrared spectrometer for soil organic carbon (OC) and total carbon (TC) estimation. TC was measured on 151 soil profiles up to the depth of 1 m in NSW, Australia, and from which a subset of 24 soil profiles were measured for OC. Two commercial spectrometers including the AgriSpec^TM (ASD) and NeoSpectra^TM (Neospectra) with spectral wavelength ranges of 350–2,500 and 1,300–2,500 nm, respectively, were used to scan the soil samples, according to the standard contact probe protocol. Savitzky–Golay smoothing filter and standard normal variate (SNV) transformation were performed on the spectral data for noise reduction and baseline correction. Three calibration models, including Cubist tree model, partial least squares regression (PLSR) and support vector machine (SVM), were assessed for the prediction of soil OC and TC using spectral data. A 10‐fold cross‐validation analysis was performed for evaluation of the models and devices accuracies. Results showed that Cubist model predicts OC and TC more accurately than PLSR and SVM. For OC prediction, Cubist showed R² = 0.89 (RMSE = 0.12%) and R² = 0.78 (RMSE = 0.16%) using ASD and NeoSpectra, respectively. For TC prediction, Cubist produced R² = 0.75 (RMSE = 0.45%) and R² = 0.70 (RMSE = 0.50%) using ASD and NeoSpectra, respectively. ASD performed better than NeoSpectra. However, the low‐cost NeoSpectra predictions were comparable to the ASD. These finding can be helpful for more efficient future spectroscopic prediction of soil OC and TC with less costly devices.     

无人机遥测技术在水土保持生态果园改造监测中的应用

以江西水土保持生态科技园为研究区,通过无人机遥测技术以及地理信息系统,对生态果园水土保持措施进行图斑化、信息化管理研究.结果表明:无人机遥测技术获取的不同阶段的航飞影像可用于生态果园改造的动态监测;利用Agisoft photoscan软件处理航飞影像可生成高分辨的数字正射影像(DOM)以及数字地表模型(DSM)数据;利用生成的DOM,结合地理信息系统,可获取生态果园水土保持措施的分布、数量、长度等信息,建立生态果园水土保持措施数据库,并输出报表和专题图.经与实际量测长度验证,基于DOM提取的长度平均精度为97.99％;与实测坡度验证,基于DSM数据提取的坡度平均精度为88.91％.无人机遥测技术能够快速、准确、自动获取地物的面积、长度、坡度等指标数据,可满足当前水土保信息化的要求,提升综合治理项目实施监测的信息化水平,提高施工监测的质量和效率.     

Assessing the effect of crop residue removal on soil organic carbon storage and microbial activity in a no‐till cropping system

Changes in agricultural management strategies have received much attention in recent years with a view to increasing or maintaining the amount of carbon (C) sequestered as soil organic C (SOC). In many parts of the world, minimum or no‐till management has been promoted as a means of improving soil quality, reducing losses of erosion and potentially increasing SOC stocks. However, no‐till systems can become problematic and potentially disease‐prone, especially due to high crop residue loadings. Consequently, residue removal either by harvesting or burning off may be employed to reduce these pressures. Here, we examined the effect of crop residue removal on C storage in soil that had been under no‐till management for 20 yr. We predicted improved physical properties (i.e. lower bulk density) and greater microbial activity under the residue retention soils due to greater readily available C and nutrients derived from crop residues. In contrast, we predicted relative reductions in SOC in the no residue soils due to a lack of available residue‐derived C for microbial use. Residue removal caused a relative C loss from the soil, which was related to C input, amount of nutrient availability and microbial activity. We demonstrate the importance of maintaining crop residue cover in no‐till cropping systems for soil function and highlight the potentially deleterious effects of changing management strategy to increased residue harvesting or removal by burning.