A multivariate statistical analysis was performed on multi-element soil geochemical data from the Koda Hill-Bulenga gold prospects in the Wa-Lawra gold belt, northwest Ghana. The objectives of the study were to define...A multivariate statistical analysis was performed on multi-element soil geochemical data from the Koda Hill-Bulenga gold prospects in the Wa-Lawra gold belt, northwest Ghana. The objectives of the study were to define gold relationships with other trace elements to determine possible pathfinder elements for gold from the soil geochemical data. The study focused on seven elements, namely, Au, Fe, Pb, Mn, Ag, As and Cu. Factor analysis and hierarchical cluster analysis were performed on the analyzed samples. Factor analysis explained 79.093% of the total variance of the data through three factors. This had the gold factor being factor 3, having associations of copper, iron, lead and manganese and accounting for 20.903% of the total variance. From hierarchical clustering, gold was also observed to be clustering with lead, copper, arsenic and silver. There was further indication that, gold concentrations were lower than that of its associations. It can be inferred from the results that, the occurrence of gold and its associated elements can be linked to both primary dispersion from underlying rocks and secondary processes such as lateritization. This data shows that Fe and Mn strongly associated with gold, and alongside Pb, Ag, As and Cu, these elements can be used as pathfinders for gold in the area, with ferruginous zones as targets.展开更多
It is necessary to estimate heavy metal concentrations within soils for understanding heavy metal contaminations and for keeping the sustainable developments of ecosystems.This study,with the floodplain along Le'a...It is necessary to estimate heavy metal concentrations within soils for understanding heavy metal contaminations and for keeping the sustainable developments of ecosystems.This study,with the floodplain along Le'an River and its two branches in Jiangxi Province of China as a case study,aimed to explore the feasibility of estimating concentrations of heavy metal lead(Pb),copper(Cu) and zinc(Zn) within soils using laboratory-based hyperspectral data.Thirty soil samples were collected,and their hyperspectral data,soil organic matters and Pb,Cu and Zn concentrations were measured in the laboratory.The potential relations among hyperspectral data,soil organic matter and Pb,Cu and Zn concentrations were explored and further used to estimate Pb,Cu and Zn concentrations from hyperspectral data with soil organic matter as a bridge.The results showed that the ratio of the first-order derivatives of spectral absorbance at wavelengths 624 and 564 nm could explain 52% of the variation of soil organic matter;the soil organic matter could ex-plain 59%,51% and 50% of the variation of Pb,Cu and Zn concentrations with estimated standard errors of 1.41,48.27 and 45.15 mg·kg-1;and the absolute estimation errors were 8%-56%,12%-118% and 2%-22%,and 50%,67% and 100% of them were less than 25% for Pb,Cu and Zn concentration estimations.We concluded that the laboratory-based hyperspectral data hold potentials in esti-mating concentrations of heavy metal Pb,Cu and Zn in soils.More sampling points or other potential linear and non-linear regression methods should be used for improving the stabilities and accuracies of the estimation models.展开更多
An observation operator is a bridge linking the system state vector and observations in a data assimilation system. Despite its importance, the degree to which an observation operator influences the performance of dat...An observation operator is a bridge linking the system state vector and observations in a data assimilation system. Despite its importance, the degree to which an observation operator influences the performance of data assimilation methods is still poorly understood. This study aimed to analyze the influences of linear and nonlinear observation operators on the sequential data assimilation through soil temperature simulation using the unscented particle filter(UPF) and the common land model. The linear observation operator between unprocessed simulations and observations was first established. To improve the correlation between simulations and observations, both were processed based on a series of equations. This processing essentially resulted in a nonlinear observation operator. The linear and nonlinear observation operators were then used along with the UPF in three assimilation experiments: an hourly in situ soil surface temperature assimilation, a daily in situ soil surface temperature assimilation, and a moderate resolution imaging spectroradiometer(MODIS) land surface temperature(LST) assimilation. The results show that the filter improved the soil temperature simulation significantly with the linear and nonlinear observation operators. The nonlinear observation operator improved the UPF's performance more significantly for the hourly and daily in situ observation assimilations than the linear observation operator did, while the situation was opposite for the MODIS LST assimilation. Because of the high assimilation frequency and data quality, the simulation accuracy was significantly improved in all soil layers for hourly in situ soil surface temperature assimilation, while the significant improvements of the simulation accuracy were limited to the lower soil layers for the assimilation experiments with low assimilation frequency or low data quality.展开更多
The purpose of this paper is to study about the interrelationship between the backscattering intensity of PALSAR data and the laboratory measurement of dielectric constant and soil moisture. The characterization of th...The purpose of this paper is to study about the interrelationship between the backscattering intensity of PALSAR data and the laboratory measurement of dielectric constant and soil moisture. The characterization of the dielectric constant of arid soils in the 0.3 - 3 GHz frequency range, particularly focused in L-band was analyzed in varied soil moisture content and soil textures. The interrelationship between the relative dielectric constant with soil textures and backscattering of PALSAR data was also analyzed and statistical model was computed. In this study, after collecting the soil samples in the field from top soil (0 - 10 cm) in a homogeneous area then, the dielectric constant was measured using a dielectric probe tool kit. For investigated of the characteristics and behaviors of the dielectric constant and relationship with backscattering a variety of moisture content from 0% to 40% and soil fraction conditions was tested in laboratory condition. All data were analyzed by integrating it with other geophysical data in GIS, such as land cover and soil texture. Thus, the regression model computed between measured soil moisture and backscattering coefficient of PALSR data which were extracted as same point of each soil sample pixel. Finally, after completing the preprocessing, such as removing the speckle noise by averaging, the model was applied to the PALSAR data for retrieving the soil moisture map in arid region of Iran. The analysis of dielectric constant properties result has shown the soil texture after the moisture content has the largest effected on dielectric constant. In addition, the PALSAR data in dual polarization are also able to derive the soil moisture using statistical method. The dielectric constant and backscattering shown have the exponential relationship and the HV polarization mode is more sensitive than the HH mode to soil moisture and overestimated the soil moisture as well. The validation of result has shown the 4.2 Vol-% RMSE of soil moisture. It means that the backscattering analysis should consider about other factors such a surface roughness and mix pixel of vegetation effective.展开更多
Hyperspectral data are an important source for monitoring soil salt content on a large scale. However, in previous studies, barriers such as interference due to the presence of vegetation restricted the precision of m...Hyperspectral data are an important source for monitoring soil salt content on a large scale. However, in previous studies, barriers such as interference due to the presence of vegetation restricted the precision of mapping soil salt content. This study tested a new method for predicting soil salt content with improved precision by using Chinese hyperspectral data, Huan Jing-Hyper Spectral Imager(HJ-HSI), in the coastal area of Rudong County, Eastern China. The vegetation-covered area and coastal bare flat area were distinguished by using the normalized differential vegetation index at the band length of 705 nm(NDVI705). The soil salt content of each area was predicted by various algorithms. A Normal Soil Salt Content Response Index(NSSRI) was constructed from continuum-removed reflectance(CR-reflectance) at wavelengths of 908.95 nm and 687.41 nm to predict the soil salt content in the coastal bare flat area(NDVI705 < 0.2). The soil adjusted salinity index(SAVI) was applied to predict the soil salt content in the vegetation-covered area(NDVI705 ≥ 0.2). The results demonstrate that 1) the new method significantly improves the accuracy of soil salt content mapping(R2 = 0.6396, RMSE = 0.3591), and 2) HJ-HSI data can be used to map soil salt content precisely and are suitable for monitoring soil salt content on a large scale.展开更多
土壤是具有高度异质性的复合体。早期的数字土壤制图研究主要关注水平方向的土壤空间变异和制图,对垂直方向空间变异和土壤三维制图考虑较少。近年来,三维地理信息技术和对地观测与探测技术的快速发展,极大地促进了土壤三维空间数据获...土壤是具有高度异质性的复合体。早期的数字土壤制图研究主要关注水平方向的土壤空间变异和制图,对垂直方向空间变异和土壤三维制图考虑较少。近年来,三维地理信息技术和对地观测与探测技术的快速发展,极大地促进了土壤三维空间数据获取、三维空间推测、三维数据模型、三维模型构建和可视化方法等方面的研究。本文对三维空间土壤推测与土壤模型构建的已有方法进行梳理和评述,以期为三维数字土壤制图的应用和发展提供建议。以三维土壤制图、三维GIS、三维数据模型、三维地质建模、三维可视化、土壤空间变异、空间推测、克里格插值、土壤-景观分析、深度函数、机器学习、地统计学、随机模拟等为关键词检索Web of Science数据库,基于相关度、引用率和文献来源等因素进一步筛选出重点文献进行分析。归纳整理了土壤空间变异性、三维空间土壤推测、三维空间数据模型和三维模型构建等关键技术的现有研究体系,对各种三维推测和建模方法的优缺点和适用场景作出评价。针对目前研究中存在的垂直方向土壤数据稀少、土壤三维推测精度低、三维模型质量待提高等问题,提出一些可行的研究思路。展开更多
The Soil Conservation Monitorins Information System (SCMIS) presented in this paper is oriented to soil erosion control, resources exploitation, utilization, planning and management for a small watershed (about 10 sq....The Soil Conservation Monitorins Information System (SCMIS) presented in this paper is oriented to soil erosion control, resources exploitation, utilization, planning and management for a small watershed (about 10 sq. km.) on the Loess Plateau. It sums up Remote sensing (RS), Geographical Information System (GIS) and Expert System (ES) and consists of a integrated system. As a basic level information system of Loess Plateau, its perfection and psreading will bring about a great advance in resources exploitation and management of Loess Plateau.展开更多
Hybrid data assimilation （DA） is a method seeing more use in recent hydrology and water resources research. In this study, a DA method coupled with the support vector machines （SVMs） and the ensemble Kalman filter...Hybrid data assimilation （DA） is a method seeing more use in recent hydrology and water resources research. In this study, a DA method coupled with the support vector machines （SVMs） and the ensemble Kalman filter （EnKF） technology was used for the prediction of soil moisture in different soil layers： 0-5 cm, 30 cm, 50 cm, 100 cm, 200 cm, and 300 cm. The SVM methodology was first used to train the ground measurements of soil moisture and meteorological parameters from the Meilin study area, in East China, to construct soil moisture statistical prediction models. Subsequent observations and their statistics were used for predictions, with two approaches： the SVM predictor and the SVM-EnKF model made by coupling the SVM model with the EnKF technique using the DA method. Validation results showed that the proposed SVM-EnKF model can improve the prediction results of soil moisture in different layers, from the surface to the root zone.展开更多
For many environmental and agricultural applications, an accurate estimation of surface soil moisture is essential. This study sought to determine whether combining Sentinel-1A, Sentinel-2A, and meteorological data wi...For many environmental and agricultural applications, an accurate estimation of surface soil moisture is essential. This study sought to determine whether combining Sentinel-1A, Sentinel-2A, and meteorological data with artificial neural networks (ANN) could improve soil moisture estimation in various land cover types. To train and evaluate the model’s performance, we used field data (provided by La Tuscia University) on the study area collected during time periods between October 2022, and December 2022. Surface soil moisture was measured at 29 locations. The performance of the model was trained, validated, and tested using input features in a 60:10:30 ratio, using the feed-forward ANN model. It was found that the ANN model exhibited high precision in predicting soil moisture. The model achieved a coefficient of determination (R<sup>2</sup>) of 0.71 and correlation coefficient (R) of 0.84. Furthermore, the incorporation of Random Forest (RF) algorithms for soil moisture prediction resulted in an improved R<sup>2</sup> of 0.89. The unique combination of active microwave, meteorological data and multispectral data provides an opportunity to exploit the complementary nature of the datasets. Through preprocessing, fusion, and ANN modeling, this research contributes to advancing soil moisture estimation techniques and providing valuable insights for water resource management and agricultural planning in the study area.展开更多
本研究采用文献计量学方法,总结当前土壤质量研究中最小数据集(MDS)选取的方法和指标,定量分析并指出土壤质量评价中最小数据集的热点和前沿,为中国土壤质量评价和农业绿色发展提供科学参考。通过检索1991-2022年CNKI和Web of Science...本研究采用文献计量学方法,总结当前土壤质量研究中最小数据集(MDS)选取的方法和指标,定量分析并指出土壤质量评价中最小数据集的热点和前沿,为中国土壤质量评价和农业绿色发展提供科学参考。通过检索1991-2022年CNKI和Web of Science相关文献,收集了文献中310个最小数据集进行筛选,借助CiteSpace和VOSviewer对年度发文量、国家/地区、机构、期刊进行共现分析,对关键词进行突现词和聚类分析。31年来该领域文献量逐步增加并仍处于快速发展阶段,中国是发文量最多的国家,期刊载文量最多的为《土壤通报》《生态学报》和Ecological Indicators;主要研究热点表现在“农业管理对土壤质量影响、土壤退化与修复、土壤质量对气候变化的响应与应对及最小数据集筛选方法与模型构建”等方面;前期MDS在土壤质量评价中选用较多的主要为物理、化学指标,但随着土壤健康的发展,生物学指标逐步增长。在未来一段时间内MDS发文量仍为快速增长阶段,发展中国家在全球起着重要节点作用;MDS核心指标为土壤有机质/碳(SOM/SOC)、pH、全氮、速效磷和容重;未来研究应注重在基于大数据平台构建不同尺度下静态评价与动态监测相结合的综合反映土壤功能的土壤健康质量评价框架体系,探讨气候变化背景下与土壤质量变化相对应的MDS及其指标体系,构建精准反映土壤质量变化规律的评价模型与最优最小数据集。展开更多
文摘A multivariate statistical analysis was performed on multi-element soil geochemical data from the Koda Hill-Bulenga gold prospects in the Wa-Lawra gold belt, northwest Ghana. The objectives of the study were to define gold relationships with other trace elements to determine possible pathfinder elements for gold from the soil geochemical data. The study focused on seven elements, namely, Au, Fe, Pb, Mn, Ag, As and Cu. Factor analysis and hierarchical cluster analysis were performed on the analyzed samples. Factor analysis explained 79.093% of the total variance of the data through three factors. This had the gold factor being factor 3, having associations of copper, iron, lead and manganese and accounting for 20.903% of the total variance. From hierarchical clustering, gold was also observed to be clustering with lead, copper, arsenic and silver. There was further indication that, gold concentrations were lower than that of its associations. It can be inferred from the results that, the occurrence of gold and its associated elements can be linked to both primary dispersion from underlying rocks and secondary processes such as lateritization. This data shows that Fe and Mn strongly associated with gold, and alongside Pb, Ag, As and Cu, these elements can be used as pathfinders for gold in the area, with ferruginous zones as targets.
基金Supported by the National Natural Science Foundation of China (No. 40971191) the Scientific Research Starting Foundation of Ministry of Education of China for Returned Overseas Chinese Scholars the Special Foundation of Ministry of Finance of China for Nonprofit Research of Forestry Industry (No.200904001)
文摘It is necessary to estimate heavy metal concentrations within soils for understanding heavy metal contaminations and for keeping the sustainable developments of ecosystems.This study,with the floodplain along Le'an River and its two branches in Jiangxi Province of China as a case study,aimed to explore the feasibility of estimating concentrations of heavy metal lead(Pb),copper(Cu) and zinc(Zn) within soils using laboratory-based hyperspectral data.Thirty soil samples were collected,and their hyperspectral data,soil organic matters and Pb,Cu and Zn concentrations were measured in the laboratory.The potential relations among hyperspectral data,soil organic matter and Pb,Cu and Zn concentrations were explored and further used to estimate Pb,Cu and Zn concentrations from hyperspectral data with soil organic matter as a bridge.The results showed that the ratio of the first-order derivatives of spectral absorbance at wavelengths 624 and 564 nm could explain 52% of the variation of soil organic matter;the soil organic matter could ex-plain 59%,51% and 50% of the variation of Pb,Cu and Zn concentrations with estimated standard errors of 1.41,48.27 and 45.15 mg·kg-1;and the absolute estimation errors were 8%-56%,12%-118% and 2%-22%,and 50%,67% and 100% of them were less than 25% for Pb,Cu and Zn concentration estimations.We concluded that the laboratory-based hyperspectral data hold potentials in esti-mating concentrations of heavy metal Pb,Cu and Zn in soils.More sampling points or other potential linear and non-linear regression methods should be used for improving the stabilities and accuracies of the estimation models.
基金supported by the National Key Research and Development Program of China(Grants No.2016YFC0402706 and 2016YFC0402710)the National Natural Science Foundation of China(Grants No.51709046 and41323001)the Open Foundation of the State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering,Hohai University(Grant No.2015490311)
文摘An observation operator is a bridge linking the system state vector and observations in a data assimilation system. Despite its importance, the degree to which an observation operator influences the performance of data assimilation methods is still poorly understood. This study aimed to analyze the influences of linear and nonlinear observation operators on the sequential data assimilation through soil temperature simulation using the unscented particle filter(UPF) and the common land model. The linear observation operator between unprocessed simulations and observations was first established. To improve the correlation between simulations and observations, both were processed based on a series of equations. This processing essentially resulted in a nonlinear observation operator. The linear and nonlinear observation operators were then used along with the UPF in three assimilation experiments: an hourly in situ soil surface temperature assimilation, a daily in situ soil surface temperature assimilation, and a moderate resolution imaging spectroradiometer(MODIS) land surface temperature(LST) assimilation. The results show that the filter improved the soil temperature simulation significantly with the linear and nonlinear observation operators. The nonlinear observation operator improved the UPF's performance more significantly for the hourly and daily in situ observation assimilations than the linear observation operator did, while the situation was opposite for the MODIS LST assimilation. Because of the high assimilation frequency and data quality, the simulation accuracy was significantly improved in all soil layers for hourly in situ soil surface temperature assimilation, while the significant improvements of the simulation accuracy were limited to the lower soil layers for the assimilation experiments with low assimilation frequency or low data quality.
文摘The purpose of this paper is to study about the interrelationship between the backscattering intensity of PALSAR data and the laboratory measurement of dielectric constant and soil moisture. The characterization of the dielectric constant of arid soils in the 0.3 - 3 GHz frequency range, particularly focused in L-band was analyzed in varied soil moisture content and soil textures. The interrelationship between the relative dielectric constant with soil textures and backscattering of PALSAR data was also analyzed and statistical model was computed. In this study, after collecting the soil samples in the field from top soil (0 - 10 cm) in a homogeneous area then, the dielectric constant was measured using a dielectric probe tool kit. For investigated of the characteristics and behaviors of the dielectric constant and relationship with backscattering a variety of moisture content from 0% to 40% and soil fraction conditions was tested in laboratory condition. All data were analyzed by integrating it with other geophysical data in GIS, such as land cover and soil texture. Thus, the regression model computed between measured soil moisture and backscattering coefficient of PALSR data which were extracted as same point of each soil sample pixel. Finally, after completing the preprocessing, such as removing the speckle noise by averaging, the model was applied to the PALSAR data for retrieving the soil moisture map in arid region of Iran. The analysis of dielectric constant properties result has shown the soil texture after the moisture content has the largest effected on dielectric constant. In addition, the PALSAR data in dual polarization are also able to derive the soil moisture using statistical method. The dielectric constant and backscattering shown have the exponential relationship and the HV polarization mode is more sensitive than the HH mode to soil moisture and overestimated the soil moisture as well. The validation of result has shown the 4.2 Vol-% RMSE of soil moisture. It means that the backscattering analysis should consider about other factors such a surface roughness and mix pixel of vegetation effective.
基金Under the auspices of National Natural Science Foundation of China(No.41230751,41101547)Scientific Research Foundation of Graduate School of Nanjing University(No.2012CL14)
文摘Hyperspectral data are an important source for monitoring soil salt content on a large scale. However, in previous studies, barriers such as interference due to the presence of vegetation restricted the precision of mapping soil salt content. This study tested a new method for predicting soil salt content with improved precision by using Chinese hyperspectral data, Huan Jing-Hyper Spectral Imager(HJ-HSI), in the coastal area of Rudong County, Eastern China. The vegetation-covered area and coastal bare flat area were distinguished by using the normalized differential vegetation index at the band length of 705 nm(NDVI705). The soil salt content of each area was predicted by various algorithms. A Normal Soil Salt Content Response Index(NSSRI) was constructed from continuum-removed reflectance(CR-reflectance) at wavelengths of 908.95 nm and 687.41 nm to predict the soil salt content in the coastal bare flat area(NDVI705 < 0.2). The soil adjusted salinity index(SAVI) was applied to predict the soil salt content in the vegetation-covered area(NDVI705 ≥ 0.2). The results demonstrate that 1) the new method significantly improves the accuracy of soil salt content mapping(R2 = 0.6396, RMSE = 0.3591), and 2) HJ-HSI data can be used to map soil salt content precisely and are suitable for monitoring soil salt content on a large scale.
文摘土壤是具有高度异质性的复合体。早期的数字土壤制图研究主要关注水平方向的土壤空间变异和制图,对垂直方向空间变异和土壤三维制图考虑较少。近年来,三维地理信息技术和对地观测与探测技术的快速发展,极大地促进了土壤三维空间数据获取、三维空间推测、三维数据模型、三维模型构建和可视化方法等方面的研究。本文对三维空间土壤推测与土壤模型构建的已有方法进行梳理和评述,以期为三维数字土壤制图的应用和发展提供建议。以三维土壤制图、三维GIS、三维数据模型、三维地质建模、三维可视化、土壤空间变异、空间推测、克里格插值、土壤-景观分析、深度函数、机器学习、地统计学、随机模拟等为关键词检索Web of Science数据库,基于相关度、引用率和文献来源等因素进一步筛选出重点文献进行分析。归纳整理了土壤空间变异性、三维空间土壤推测、三维空间数据模型和三维模型构建等关键技术的现有研究体系,对各种三维推测和建模方法的优缺点和适用场景作出评价。针对目前研究中存在的垂直方向土壤数据稀少、土壤三维推测精度低、三维模型质量待提高等问题,提出一些可行的研究思路。
文摘The Soil Conservation Monitorins Information System (SCMIS) presented in this paper is oriented to soil erosion control, resources exploitation, utilization, planning and management for a small watershed (about 10 sq. km.) on the Loess Plateau. It sums up Remote sensing (RS), Geographical Information System (GIS) and Expert System (ES) and consists of a integrated system. As a basic level information system of Loess Plateau, its perfection and psreading will bring about a great advance in resources exploitation and management of Loess Plateau.
基金supported by the National Basic Research Program of China (the 973 Program,Grant No.2010CB951101)the Program for Changjiang Scholars and Innovative Research Teams in Universities,the Ministry of Education,China (Grant No. IRT0717)
文摘Hybrid data assimilation （DA） is a method seeing more use in recent hydrology and water resources research. In this study, a DA method coupled with the support vector machines （SVMs） and the ensemble Kalman filter （EnKF） technology was used for the prediction of soil moisture in different soil layers： 0-5 cm, 30 cm, 50 cm, 100 cm, 200 cm, and 300 cm. The SVM methodology was first used to train the ground measurements of soil moisture and meteorological parameters from the Meilin study area, in East China, to construct soil moisture statistical prediction models. Subsequent observations and their statistics were used for predictions, with two approaches： the SVM predictor and the SVM-EnKF model made by coupling the SVM model with the EnKF technique using the DA method. Validation results showed that the proposed SVM-EnKF model can improve the prediction results of soil moisture in different layers, from the surface to the root zone.
文摘For many environmental and agricultural applications, an accurate estimation of surface soil moisture is essential. This study sought to determine whether combining Sentinel-1A, Sentinel-2A, and meteorological data with artificial neural networks (ANN) could improve soil moisture estimation in various land cover types. To train and evaluate the model’s performance, we used field data (provided by La Tuscia University) on the study area collected during time periods between October 2022, and December 2022. Surface soil moisture was measured at 29 locations. The performance of the model was trained, validated, and tested using input features in a 60:10:30 ratio, using the feed-forward ANN model. It was found that the ANN model exhibited high precision in predicting soil moisture. The model achieved a coefficient of determination (R<sup>2</sup>) of 0.71 and correlation coefficient (R) of 0.84. Furthermore, the incorporation of Random Forest (RF) algorithms for soil moisture prediction resulted in an improved R<sup>2</sup> of 0.89. The unique combination of active microwave, meteorological data and multispectral data provides an opportunity to exploit the complementary nature of the datasets. Through preprocessing, fusion, and ANN modeling, this research contributes to advancing soil moisture estimation techniques and providing valuable insights for water resource management and agricultural planning in the study area.
文摘本研究采用文献计量学方法,总结当前土壤质量研究中最小数据集(MDS)选取的方法和指标,定量分析并指出土壤质量评价中最小数据集的热点和前沿,为中国土壤质量评价和农业绿色发展提供科学参考。通过检索1991-2022年CNKI和Web of Science相关文献,收集了文献中310个最小数据集进行筛选,借助CiteSpace和VOSviewer对年度发文量、国家/地区、机构、期刊进行共现分析,对关键词进行突现词和聚类分析。31年来该领域文献量逐步增加并仍处于快速发展阶段,中国是发文量最多的国家,期刊载文量最多的为《土壤通报》《生态学报》和Ecological Indicators;主要研究热点表现在“农业管理对土壤质量影响、土壤退化与修复、土壤质量对气候变化的响应与应对及最小数据集筛选方法与模型构建”等方面;前期MDS在土壤质量评价中选用较多的主要为物理、化学指标,但随着土壤健康的发展,生物学指标逐步增长。在未来一段时间内MDS发文量仍为快速增长阶段,发展中国家在全球起着重要节点作用;MDS核心指标为土壤有机质/碳(SOM/SOC)、pH、全氮、速效磷和容重;未来研究应注重在基于大数据平台构建不同尺度下静态评价与动态监测相结合的综合反映土壤功能的土壤健康质量评价框架体系,探讨气候变化背景下与土壤质量变化相对应的MDS及其指标体系,构建精准反映土壤质量变化规律的评价模型与最优最小数据集。
