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
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
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
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
为了准确评价川产道地药材羌活栽培区耕作层土壤质量状况,分别采用聚类分析法(CA)和主成分分析法(PCA)构建栽培区耕作层土壤质量最小数据集(minimum data set,MDS),利用最小数据集土壤质量指数(soil quality index-CA,SQI-CA和SQI-PCA)...为了准确评价川产道地药材羌活栽培区耕作层土壤质量状况,分别采用聚类分析法(CA)和主成分分析法(PCA)构建栽培区耕作层土壤质量最小数据集(minimum data set,MDS),利用最小数据集土壤质量指数(soil quality index-CA,SQI-CA和SQI-PCA)和全量数据集土壤质量指数(SQI-T)评价川西北羌活栽培区耕作层土壤质量。结果表明:(1)羌活栽培区土壤有机质含量为(19.14±6.75)g·kg^(−1),处于中度贫瘠化水平;土壤速效氮、速效磷和速效钾含量较高,分别为(129.78±47.78)mg·kg^(−1)、(22.89±14.78)g·kg^(−1)和(159.87±97.87)mg·kg^(−1);土壤为中性土壤,pH均值为7.20±1.68。(2)基于不同数据集的土壤质量指数均值排序为SQI-T>SQI-PCA>SQI-CA,而SQI-PCA与SQI-T之间的Nash有效系数高于SQI-CA,相对偏差系数低于SQI-CA,说明基于主成分分析的最小数据集(MDS-PCA)评价效果更优,该数据集包括土壤容重、抗剪强度、有机质含量、饱和导水率、黏粒含量、pH、速效氮和砂粒含量共8个指标。(3)川西北羌活栽培区土壤质量指数SQI-PCA<0.33,表明该研究区耕作层土壤质量总体水平较差,主要体现在土壤紧实、有机质含量低,需要通过合理耕作、施肥和土壤改良等方式对耕作层土壤质量进行有效调控。研究结果可为川西北高原羌活栽培区土壤质量改良和生产适宜性调控提供参考,有利于川西北高原区中药材产区土壤可持续利用。展开更多
为了解不同土壤湿度产品在云南省的适用性和可靠性,基于云南省的94个站点数据以及TC(triplecollocation)方法评估5种不同土壤湿度数据在云南省的适用性及不同干湿条件下的表现,包括ERA5-Land(the fifth global atmospheric analysis dat...为了解不同土壤湿度产品在云南省的适用性和可靠性,基于云南省的94个站点数据以及TC(triplecollocation)方法评估5种不同土壤湿度数据在云南省的适用性及不同干湿条件下的表现,包括ERA5-Land(the fifth global atmospheric analysis dataset for the land component of the European Centre for Medium-Range Weather Forecasts(ECMWF))、GLDAS(Global Land Data Assimilation System)、SMAP(Soil Moisture Active and Passive)、MERRA-2(Modern-Era Retrospective Analysis for Research and Applications,Version 2)和ESA CCI(European Space Agency Climate Change Initiative)。结果表明:与站点数据相比,5个产品的偏差均为正偏差(0.090~0.122),明显高估了云南省的土壤湿度,但变化趋势与幅度一致,都能捕获到土壤湿度的时间变化。基于站点数据的评估结果显示:在年尺度上,ERA5-Land和SMAP与站点数据吻合程度最高,相关系数(R)分别为0.456和0.454,其次是ESA CCI(0.439);干湿季的评估结果显示,所有产品相关性均低于年尺度,且湿季高于干季,但湿季表现出更大的正偏差,其中SMAP在干季(0.323)和湿季(0.418)均表现最优。基于TC方法的评估结果显示:ERA5-Land(0.925)和ESA CCI(0.931)相关性最高;其次是GLDAS(0.890)和MERRA-2(0.864);干湿季的评估结果与站点数据的评估一致,相较于年尺度大部分产品的相关性也呈下降趋势,且干季降幅更大;SMAP干湿季R分别为0.828和0.770,表现最差;MERRA-2湿季的R(0.912)和ESD(error standard deviation)(0.020)优于其年尺度评估结果。综合来看,ESA CCI相关性较高且精度最好,更适用于云南省表层土壤湿度的监测。展开更多
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 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.
文摘为了准确评价川产道地药材羌活栽培区耕作层土壤质量状况,分别采用聚类分析法(CA)和主成分分析法(PCA)构建栽培区耕作层土壤质量最小数据集(minimum data set,MDS),利用最小数据集土壤质量指数(soil quality index-CA,SQI-CA和SQI-PCA)和全量数据集土壤质量指数(SQI-T)评价川西北羌活栽培区耕作层土壤质量。结果表明:(1)羌活栽培区土壤有机质含量为(19.14±6.75)g·kg^(−1),处于中度贫瘠化水平;土壤速效氮、速效磷和速效钾含量较高,分别为(129.78±47.78)mg·kg^(−1)、(22.89±14.78)g·kg^(−1)和(159.87±97.87)mg·kg^(−1);土壤为中性土壤,pH均值为7.20±1.68。(2)基于不同数据集的土壤质量指数均值排序为SQI-T>SQI-PCA>SQI-CA,而SQI-PCA与SQI-T之间的Nash有效系数高于SQI-CA,相对偏差系数低于SQI-CA,说明基于主成分分析的最小数据集(MDS-PCA)评价效果更优,该数据集包括土壤容重、抗剪强度、有机质含量、饱和导水率、黏粒含量、pH、速效氮和砂粒含量共8个指标。(3)川西北羌活栽培区土壤质量指数SQI-PCA<0.33,表明该研究区耕作层土壤质量总体水平较差,主要体现在土壤紧实、有机质含量低,需要通过合理耕作、施肥和土壤改良等方式对耕作层土壤质量进行有效调控。研究结果可为川西北高原羌活栽培区土壤质量改良和生产适宜性调控提供参考,有利于川西北高原区中药材产区土壤可持续利用。
文摘为了解不同土壤湿度产品在云南省的适用性和可靠性,基于云南省的94个站点数据以及TC(triplecollocation)方法评估5种不同土壤湿度数据在云南省的适用性及不同干湿条件下的表现,包括ERA5-Land(the fifth global atmospheric analysis dataset for the land component of the European Centre for Medium-Range Weather Forecasts(ECMWF))、GLDAS(Global Land Data Assimilation System)、SMAP(Soil Moisture Active and Passive)、MERRA-2(Modern-Era Retrospective Analysis for Research and Applications,Version 2)和ESA CCI(European Space Agency Climate Change Initiative)。结果表明:与站点数据相比,5个产品的偏差均为正偏差(0.090~0.122),明显高估了云南省的土壤湿度,但变化趋势与幅度一致,都能捕获到土壤湿度的时间变化。基于站点数据的评估结果显示:在年尺度上,ERA5-Land和SMAP与站点数据吻合程度最高,相关系数(R)分别为0.456和0.454,其次是ESA CCI(0.439);干湿季的评估结果显示,所有产品相关性均低于年尺度,且湿季高于干季,但湿季表现出更大的正偏差,其中SMAP在干季(0.323)和湿季(0.418)均表现最优。基于TC方法的评估结果显示:ERA5-Land(0.925)和ESA CCI(0.931)相关性最高;其次是GLDAS(0.890)和MERRA-2(0.864);干湿季的评估结果与站点数据的评估一致,相较于年尺度大部分产品的相关性也呈下降趋势,且干季降幅更大;SMAP干湿季R分别为0.828和0.770,表现最差;MERRA-2湿季的R(0.912)和ESD(error standard deviation)(0.020)优于其年尺度评估结果。综合来看,ESA CCI相关性较高且精度最好,更适用于云南省表层土壤湿度的监测。
文摘为了评估高寒地区不同土地利用方式下的土壤质量状况,明确不同土地利用方式下的土壤质量的关键影响因子,在祁连山南坡采集了林地、灌丛、草地及耕地4种主要土地利用类型的土壤样品174份,通过主成分分析(Principal component analysis,PCA)建立最小数据集(Minimum data set,MDS),综合评估研究区不同土地利用方式下的土壤质量。结果表明:林地、灌丛、草地和耕地土壤质量指数值分别为0.535,0.519,0.466和0.544,表现为耕地>林地>灌丛>草地,对土壤质量分级为Ⅰ~Ⅵ级,对应指数分别为≤0.3,(0.3~0.4],(0.4~0.5],(0.5~0.6],(0.6,0.7]和>0.7,草地等级为Ⅲ级,处于“中等”水平;耕地、林地和灌丛土壤质量等级为Ⅳ级,处于“中等偏上”水平。土壤质量关键指标间存在互相影响,因此,建议研究区域土地要实施分类科学管理。此外,合理开发和应用绿色高效的新型生物技术是应对影响研究区土壤质量的微生物指标的有效措施。
