Estimating forest soil bulk density using boosted regression modelling

Soil bulk density (ρ) is an important physical property, but its measurement is frequently lacking in soil surveys due to the time‐consuming nature of making the measurement. As a result pedotransfer functions (PTFs) have been developed to predict ρ from other more easily available soil properties. These functions are generally derived from regression methods that aim to fit a single model. In this study, we use a technique called Generalized Boosted Regression Modelling (GBM; Ridgeway, 2006 ) which combines two algorithms: regression trees and boosting. We built two models and compared their predictive performance with published PTFs. All the functions were fitted based on the French forest soil dataset for the European demonstration Biosoil project. The two GBM models were Model G3 which involved the three most frequent quantitative predictors used to estimate soil bulk density (organic carbon, clay and silt), and Model G10, which included ten qualitative and quantitative input variables such as parent material or tree species. Based on the full dataset, Models G3 and G10 gave R² values of 0.45 and 0.86, respectively. Model G3 did not significantly outperform the best published model. Even when fitted from an external dataset, it explained only 29% of the variation of ρ with a root mean square error of 0.244 g/cm³. In contrast, the more complex Model G10 outperformed the other models during external validation, with a R² of 0.67 and a predictive deviation of ±0.168 g/cm³. The variation in forest soil bulk densities was mainly explained by five input variables: organic carbon content, tree species, the coarse fragment content, parent material and sampling depth.     

Pedotransfer functions for estimating soil water retention curve of Sicilian soils

Pedotransfer functions (PTFs) make use of routinely surveyed soil data to estimate soil properties but their application to soils different from those used for their development can yield inaccurate estimates. This investigation aimed at evaluating the water retention prediction accuracy of eight existing PTFs using a database of 217 Sicilian soils exploring 11 USDA textural classes. PTFs performance was assessed by root mean square differences (RMSD) and average differences (AD) between estimated and measured data. Extended Nonlinear Regression (ENR) technique was adopted to recalibrate or develop four new PTFs and Wind’s evaporation method was applied to validate the effectiveness of the relationships proposed. PTFs evaluation resulted in RMSD and AD values in the range 0.0630–0.0972 and 0.0021–0.0618 cm³ cm^–3, respectively. Best and worst performances were obtained respectively by PTF-MI and PTF-ZW. ENR allowed to recalibrate PTF-MI and PTF-ZW with improvements of RMSD (0.0594 and 0.0508 cm³ cm^–3) and to develop two relationships that improved RMSD by 75–78% as compared to PTF-MI. The results confirmed the potential of ENR technique in calibrating existing PTFs or developing new ones. Validation conducted with an independent dataset suggested that recalibrated/developed PTFs represent a viable alternative for water retention estimation of Sicilian soils.     

Prediction of soil properties using a hyperspectral remote sensing method

Quickly and accurately mapping soil properties is critical for agricultural, forestry and environmental management. In this study, a new hyperspectral remote sensing method of soil property prediction was developed and validated in Stipa purpurea dominated alpine grasslands located in Shenzha County of the Qiangtang Plateau, northwestern Qinghai-Tibet Plateau. Hyperspectral data were collected in a total of 67 sample points. At the same time, soil samples were obtained at the locations and soil properties including organic carbon, total nitrogen, total potassium and total phosphorus were measured. The correlations of the soil properties with original bands and enhanced spectral variables derived from both field and satellite hyperspectral data were analyzed. Regression models that explained the relationships were further developed to map the soil properties. The results showed that the stepwise regression models based on the satellite hyperspectral image derived enhanced spectral variables produced reasonable spatial distributions of the soil properties and the relative RMSE values of 68.9, 46.3, 31.4 and 45.5% for soil organic carbon, total nitrogen, total phosphorus and total potassium, respectively. Thus, this study implied that the hyperspectral data based method provided great potential to predict the soil properties.     

Prediction of soil water retention properties after stratification by combining texture,bulk density and the type of horizon

Among the numerous pedotransfer functions (PTFs) published, class‐PTfs have received little attention because their accuracy is often considered limited. However, recent studies show that performance of class‐PTFs can be similar to the more popular continuous‐PTFs. In this study, we compare the performance of PTFs that were derived from a set of 456 horizons collected in France grouped by combinations of texture, bulk density and type of horizon (topsoil and subsoil). The performance of these class‐PTFs was validated against water retained at ?33 and ?1500 kPa. Our results show that the best performance was obtained with class‐PTFs that used both texture and bulk density (texture‐structural class‐PTFs). They also showed that incorporation of horizon type into the PTF did not improve prediction performance. Comparison of performance at ?33 and ?1500 kPa showed very little difference, thus indicating no bias according to the value of water potential. Finally, the class‐PTFs developed are well suited for predicting water retention properties at the continental and national scales because only very basic soils data are available at these scales. A map of the available water capacity (AWC) was established for France using the 1:1 000 000 Soil Geographical Database of France and an averaged AWC of 104 mm was computed for France.     

Prediction of soil salinity in the Yellow River Delta using geographically weighted regression

It is essential to determine the content and spatial distribution of soil salinity in a timely manner because soil salinization can cause land degradation on a regional scale. Geographically weighted regression (GWR) is a local regression method that can achieve the spatial extension of dependent variables based on the relationships between the dependent variables and environment variables and the spatial distances between the sample points and predicted locations. This study aimed to explore the feasibility of GWR in predicting soil salinity because the existing interpolation methods for soil salinity in the Yellow River Delta are still of low precision. Additionally, multiple linear regressions, cokriging and regression kriging were added to compare the accuracy of GWRs. The results showed that GWR predicted soil salinity with high accuracy. Furthermore, the accuracy was improved when compared to other methods. The root mean square error, correlation coefficient, regression coefficient and adjustment coefficients between the observed values and predicted values of the validation points were 0.31, 0.65, 0.57 and 0.42, respectively, which were better than that of other methods, indicating that GWR is an optimal method.     

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。     

基于偏最小二乘回归的土壤有机质含量高光谱估算

为实现基于光谱分析土壤有机质含量的快速测定,该文以江汉平原公安县的土壤为研究对象,进行室内理化分析、光谱测量与处理等一系列工作,在土壤原始光谱反射率(raw spectral reflectance,R)的基础上,提取了其倒数之对数(inverse-log reflectance,LR)、一阶微分(first order differential reflectance,FDR)和连续统去除(continuum removal,CR)3种光谱指标,分析4种不同形式的光谱指标与有机质含量的相关性,对相关系数进行P=0.01水平上的显著性检验来确定显著性波段的范围,并基于全波段(400~2 400 nm)和显著性波段运用偏最小二乘回归(partial least squares regression,PLSR)建立了该区域土壤有机质高光谱的预测模型,通过模型精度的比较确定最优模型。结果表明,进行CR变换后,光谱曲线的特征吸收带更加明显,相关系数在可见光波段范围内有所提高;基于全波段的PLSR建模效果要优于显著性波段,其中以CR的预测精度最为突出,其模型的决定系数R2和相对分析误差RPD分别为0.84、2.58;显著性波段的PLSR模型与全波段对比在模型精度方面虽有一定差距,但从模型的复杂程度来比较,具有模型简单、运算量小、变量更少的特点;最后,综合比较了全波段和显著性波段4种光谱指标的反演精度,发现CR-PLSR模型的建模和预测的效果比R-PLSR、LR-PLSR、FDR-PLSR模型都要显著。该研究可为将CR-PLSR高光谱反演模型用于该区域土肥信息的遥感监测提供参考。     

滩涂土壤有机质含量的反射光谱估算

Rapid determination of soil organic matter (SOM) using regression models based on soil reflectance spectral data serves an important function in precision agriculture. “deviation of arch”(DOA)-based regression and partial least squares regression (PLSR) are two popular modeling approaches to predict SOM. However, few studies have explored the accuracy of the DOA-based regression and PLSR models. Therefore, the DOA-based regression and PLSR were applied to the visible near-infrared (VNIR) spectra to estimate SOM content in the case of various dataset divisions. A two-fold cross-validation scheme was adopted and repeated 10 000 times for rigorous evaluation of the DOA-based models in comparison with the widely used PLSR model. Soil samples were collected for SOM analysis in the coastal area of northern Jiangsu Province, China. The results indicated that both modelling methods provided reasonable estimates of SOM, with PLSR outperforming DOA-based regression in general. However, the performance of PLSR for the validation dataset decreased more noticeably. Among the four DOA-based models, the linear model of the DOA provided the best estimation of SOM and a cutoff of SOM content (19.76 g kg^-1), and the performance for calibration and validation datasets was consistent. As the SOM content exceeded 19.76 g kg^-1, SOM became more effective in masking the spectral features of other soil properties to a certain extent. This work confirmed that reflectance spectroscopy combined with PLSR could serve as a non-destructive and cost-efficient way for rapid determination of SOM when hyperspectral data were available. The DOA-based model, which requires only 3 bands in the visible spectra, also provided SOM estimation with acceptable accuracy.     

Estimating the wetting branch of the soil water retention curve from grain-size fractions

The development of a theoretical method for estimating the wetting branch of the soil water retention curve (SWRC) is required for knowledge of the soil infiltration process. The aim of this study was to derive the theoretical functions to represent the wetting branch of the SWRC based on the Jensen method, and then compare the Jensen method and Kool & Parker (KP87) model for estimating the wetting branch of the SWRC. Fifteen soil samples with varying basic properties (e.g., grain-size distribution and bulk density (BD)) were selected from the Unsaturated Soil Hydraulic Database (UNSODA) to test these two methods. Results showed that the Jensen method (root mean squared error (RMSE) = 0.057 cm³ cm⁻³) produced a substantially better performance in predicting the wetting branch of the SWRC than the KP87 (RMSE = 0.089 cm³ cm⁻³) for the 15 samples. The range of the scaled mean bias error (SMBE) between the Jensen method-predicted and measured soil water contents at all pressure heads was −0.529 to 0.402. A positive linear relationship between the SMBE and silt content was observed for the Jensen method. The findings of this study have practical significance for simulating the soil infiltration in the unsaturated zone.     

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.     

反射光谱估算滨海土壤黏粒含量

探明反射光谱估算土壤黏粒含量的成因是实现黏粒含量测定、提高估算精度的基础。该文以江苏省滨海平原的150个土壤样品为研究对象,将测得的原始光谱数据进行平滑、一阶导数、连续统去除和倒数等数据变换,采用逐步多元线性回归(stepwise multiple linear regression,SMLR)和偏最小二乘回归(partial least squares regression,PLSR)方法估算黏粒含量,并在此基础上分析建模的影响波段,探讨反射光谱估算土壤黏粒含量的成因。结果表明,连续统去除光谱数据的SMLR分析估算精度最高,建模集和验证集决定系数分别为0.941和0.750。360~900、1 800~2 490 nm是黏粒含量的重要建模影响波段,该建模影响波段主要包括铁离子(410 nm附近)、土壤有机质(500~800 nm)、层状硅酸盐中的结晶水(1 900 nm附近)、绿泥石和蛭石等黏土矿物(2 325 nm)的吸收特征波段;PLSR分析表明,1 400 nm附近回归系数出现的双峰特征源于高岭石的双峰吸收。黏粒中的黏土矿物、黏粒对铁离子的吸附特性以及黏粒与有机质的高度相关性是实现反射光谱估算滨海土壤黏粒含量的原因。     

基于成像光谱技术预测氮素在土壤剖面中的垂直分布

可见—近红外(vis-NIR)高光谱成像技术应用于土壤科学是当前数字土壤研究的新方向。本研究考察了该技术预测土壤剖面氮素垂直分布的可行性。深达1 m的土壤整段剖面(1 000 mm×170 mm×65 mm)采自湖北崇阳县,成像光谱仪配备了25μm狭缝,视场角13.1°的35 mm焦距镜头和1 004×1 002像素的面阵CCD,拍摄得到剖面vis-NIR高光谱影像(400~1 000 nm共753个波段)。对获取的影像先通过几何校正解决影像形变问题,再采用监督分类方法识别提取有效土壤像素,剔除阴影裂缝等无效像素。最后利用室内土样vis-NIR反射光谱建立的土壤全氮校正模型,对3个土壤整段剖面的高光谱影像数据进行全氮(TN)预测制图。结果表明,vis-NIR成像光谱技术对土壤整段剖面TN含量预测效果达到甚至优于经标准制样处理后所建模型精度。但存在纵向局限性,其良好地还原了浅层土壤氮素的分布规律,0~600 mm为较佳预测深度。     

土壤含水率与土壤碱度对土壤抗剪强度的影响

土壤含水率和土壤碱度是表征土壤物理化学性质的两个重要参数。通过室内三轴不固结不排水试验,研究了土壤含水率和土壤碱度对土壤抗剪强度的影响。试验处理采用5种土壤碱度(土壤可交换钠百分比ESP=0、5、10、20、40)和4种土壤质量含水率(0.05、0.10、0.20以及饱和含水率0.34)水平。试验结果显示,土壤黏聚力随着土壤含水率的增加基本上呈先增大后减小之趋势;当土壤含水率在0.10附近时黏聚力达到其最大值。土壤内摩擦角随着土壤含水率的增加而线性减小。土壤碱度对土壤黏聚力的影响机理较为复杂,其影响效果随土壤含水率的增加而减小;但土壤碱度对土壤内摩擦角的影响较小。土壤碱度对土壤抗剪强度的影响程度明显地小于土壤含水率对其的影响程度。     

Digital soil mapping in Germany—a review

Digital soil mapping as a tool to generate spatial soil information provides solutions for the growing demand for high‐resolution soil maps worldwide. Even in highly developed countries like Germany, digital soil mapping becomes essential due to the decreasing, time‐consuming, and expensive field surveys which are no longer affordable by the soil surveys of the individual federal states. This article summarizes the present state of soil survey in Germany in terms of digitally available soil data, applied digital soil mapping, and research in the broader field of pedometrics and discusses future perspectives. Based on the geomorphologic conditions in Germany, relief is a major driving force in soil genesis. This is expressed by the digital–soil mapping research which highlights the great importance of digital terrain attributes in combination with information on parent material in soil prediction. An example of digital soil mapping using classification trees in Thuringia is given as an introduction in digital soil‐class mapping based on correlations to environmental covariates within the scope of the German classification system.     

基于灰度关联-岭回归的荒漠土壤有机质含量高光谱估算

为改善高光谱技术对荒漠土壤有机质的估测效果,该文采集了以色列Seder Boker地区的荒漠土壤,经预处理、理化分析后将土样分为砂质土和黏壤土2类,再通过光谱采集、处理得到6种光谱指标:反射率(reflectivity,REF)、倒数之对数变换(inverse-log reflectance,LR)、去包络线处理(continuum removal,CR)、标准正态变量变换(standard normal variable reflectance,SNV)、一阶微分变换(first order differential reflectance,FDR)和二阶微分变换(second order differential reflectance,SDR)。通过灰度关联(gray correlation,GC)法确定SNV、FDR、SDR为敏感光谱指标,采用偏最小二乘回归(partial least squares regression,PLSR)法和岭回归(ridge regression,RR)法,构建基于敏感光谱指标的土壤有机质高光谱反演模型,并对模型精度进行比较。结果表明:砂质土有机质含量的反演效果要优于黏壤土;基于SNV指标建立的模型决定系数R~2和相对分析误差RPD均为最高、均方根误差RMSE最低,所以SNV是土壤有机质的最佳光谱反演指标;对SNV-PLSR模型和SNV-RR模型综合比较得出,SNV-RR模型仅用全谱4%左右的波段建模,实现了更为理想的反演效果:其中,对砂质土有机质的预测能力极强(R_p~2为0.866,RMSE为0.610 g/kg、RPD为2.72),对黏壤土有机质的预测能力很好(Rp2为0.863,RMSE为0.898 g/kg、RPD为2.37)。荒漠土壤有机质GC-SNV-RR反演模型的建立为高光谱模型的优化、土壤有机质的快速测定提供了一种新的途径。     

电阻率成像法监测人工梭梭林土壤水分

土壤水分是影响干旱半干旱沙区植物生长发育的主要限制因素。快速、准确地监测土壤水分时空动态可为干旱半干旱区植被建设与生态恢复提供科学依据。以乌兰布和沙漠东北部人工梭梭固沙林土壤为研究对象,在林内、外(根际、冠中、冠缘、行间、林外)设置了5条监测样线,分别于一次强降雨后的第2天、第15天、第55天用多电极电阻仪定位测定了土壤电阻率,同步采取土样用烘干法测定了土壤实际含水率,建立了土壤含水率与土壤电阻率之间的相关关系,并对二维剖面土壤水分空间分布特征进行了分析。结果表明:1)土壤含水率与土壤电阻率之间为极显著负相关关系(P0.01),可用幂函数表示。2)5条测线的土壤电阻率在3次监测时均随土层深度增加而减小,而土壤含水量随土层深度增加而增大,根际冠中冠缘行间林外。强降水后的不同时间内,由于受土壤属性、树冠对水分再分配、树干径流、根系吸收水分等影响,二维剖面上土壤水分空间分布格局有明显变化。随着雨后干旱时间的延长,0～51 cm水分含量由于受蒸发、植物吸收利用的影响而明显降低。3)电阻率成像技术在野外能快速准确,长期定位监测土壤水分含量;对地表扰动小,实现了非破坏性测量;保证测定精度的同时,还能提供尺度较大的土壤水分空间分布的详实数据,可高效快速地获取连续的土壤水分分布信息。