The authors apologize for the erroneous transcription of the average chemical composition data of Apollo lunar soil samples in Table 4.The difference in chemical composition between lunar regolith simulants and actual...The authors apologize for the erroneous transcription of the average chemical composition data of Apollo lunar soil samples in Table 4.The difference in chemical composition between lunar regolith simulants and actual lunar samples is an important indicator for evaluating their similarity.For comparison,Table 4 lists the chemical compositions of Apollo 12,Apollo 14,Apollo 15,Apollo 16,and other classic lunar regolith simulants.However,the Apollo lunar soil data in the original Table 4 contained errors,which have been corrected in this corrigendum.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
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
【目的】开展土壤健康评价,不仅可以了解甜瓜田土壤健康状况,还可以精准识别土壤障碍因子,为甜瓜田健康土壤培育提供理论支撑。本研究通过建立土壤健康评价数据库并构建最小数据集(minimum data set,MDS),系统评价吐鲁番市甜瓜田的土壤...【目的】开展土壤健康评价,不仅可以了解甜瓜田土壤健康状况,还可以精准识别土壤障碍因子,为甜瓜田健康土壤培育提供理论支撑。本研究通过建立土壤健康评价数据库并构建最小数据集(minimum data set,MDS),系统评价吐鲁番市甜瓜田的土壤健康状况。【方法】在新疆吐鲁番市吐峪沟乡、亚尔镇、三堡乡的甜瓜田中,选择种植年限平均为5年的72个甜瓜地块,采集0—20 cm土层土壤样品,测定了28项土壤物理、化学和生物学指标,建立土壤健康评价数据库。利用主成分分析构建最小数据集,运用线性和非线性评分函数进行土壤健康评价,并通过全数据集(total data set,TDS)对最小数据集的评价结果进行验证。【结果】用主成分分析法筛选出容重、电导率、有效磷、有机碳、交换性钙、土壤呼吸和β-木糖苷酶7个指标,建立了土壤健康评价最小数据集。采用线性评分函数法,基于全数据集和最小数据集计算的土壤健康指数分别为0.44和0.49,变异系数分别为22.5%和18.7%;采用非线性评分函数法,得到的土壤健康指数平均值分别为0.46和0.47,变异系数分别为25.8%和24.3%。基于全数据集和最小数据集计算的土壤健康指数间均呈显著正相关(P<0.01),斜率接近0.9。整体上,吐峪沟乡和亚尔镇甜瓜田土壤健康指数均高于三堡乡。【结论】最小数据集可以代替全数据集用于甜瓜田土壤健康评价,非线性评分函数计算的土壤健康指数变异系数大,说明非线性评分函数更加敏感,建议选择非线性评分函数用于甜瓜田土壤健康评价。新疆吐鲁番市甜瓜田土壤pH为碱性,土壤养分含量处于丰富水平,但有机碳和活性碳含量较低。经最小数据集评价,吐鲁番甜瓜田土壤健康指数在0.5以下,处于中等水平。展开更多
The aim of the study was to assess the current state and development of the Soil Health Index (SHI) at 13 localities with various soil-ecological conditions in the Slovak Republic. The SHI was developed using a minimu...The aim of the study was to assess the current state and development of the Soil Health Index (SHI) at 13 localities with various soil-ecological conditions in the Slovak Republic. The SHI was developed using a minimum soil data set, physical and chemical soil parameters in combination with environmental parameters (land use, gradients). The SHI is one numerical value accumulates information about the state of soil health and its ability to provide soil functions and thus ecosystems in the optimal range. The highest SHI values were determined at model localities used as arable land (Haplic Chernozem, Fluvisol) located in a warm climate at altitudes up to 200 meters above sea level. Ecosystems with very low and low value are mostly grasslands with mildly cold climate (Cambisol) and considerable slope, agroecosystem on low organic matter (Arenosol). Arable ecosystem SHI is also reduced in areas of geochemical anomalies and areas with anthropogenic load, where there is a higher content of risk elements. The SHI changes are mainly the result of changes in dynamic indicators such as soil response and soil bulk density.展开更多
文摘The authors apologize for the erroneous transcription of the average chemical composition data of Apollo lunar soil samples in Table 4.The difference in chemical composition between lunar regolith simulants and actual lunar samples is an important indicator for evaluating their similarity.For comparison,Table 4 lists the chemical compositions of Apollo 12,Apollo 14,Apollo 15,Apollo 16,and other classic lunar regolith simulants.However,the Apollo lunar soil data in the original Table 4 contained errors,which have been corrected in this corrigendum.
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【目的】开展土壤健康评价,不仅可以了解甜瓜田土壤健康状况,还可以精准识别土壤障碍因子,为甜瓜田健康土壤培育提供理论支撑。本研究通过建立土壤健康评价数据库并构建最小数据集(minimum data set,MDS),系统评价吐鲁番市甜瓜田的土壤健康状况。【方法】在新疆吐鲁番市吐峪沟乡、亚尔镇、三堡乡的甜瓜田中,选择种植年限平均为5年的72个甜瓜地块,采集0—20 cm土层土壤样品,测定了28项土壤物理、化学和生物学指标,建立土壤健康评价数据库。利用主成分分析构建最小数据集,运用线性和非线性评分函数进行土壤健康评价,并通过全数据集(total data set,TDS)对最小数据集的评价结果进行验证。【结果】用主成分分析法筛选出容重、电导率、有效磷、有机碳、交换性钙、土壤呼吸和β-木糖苷酶7个指标,建立了土壤健康评价最小数据集。采用线性评分函数法,基于全数据集和最小数据集计算的土壤健康指数分别为0.44和0.49,变异系数分别为22.5%和18.7%;采用非线性评分函数法,得到的土壤健康指数平均值分别为0.46和0.47,变异系数分别为25.8%和24.3%。基于全数据集和最小数据集计算的土壤健康指数间均呈显著正相关(P<0.01),斜率接近0.9。整体上,吐峪沟乡和亚尔镇甜瓜田土壤健康指数均高于三堡乡。【结论】最小数据集可以代替全数据集用于甜瓜田土壤健康评价,非线性评分函数计算的土壤健康指数变异系数大,说明非线性评分函数更加敏感,建议选择非线性评分函数用于甜瓜田土壤健康评价。新疆吐鲁番市甜瓜田土壤pH为碱性,土壤养分含量处于丰富水平,但有机碳和活性碳含量较低。经最小数据集评价,吐鲁番甜瓜田土壤健康指数在0.5以下,处于中等水平。
文摘The aim of the study was to assess the current state and development of the Soil Health Index (SHI) at 13 localities with various soil-ecological conditions in the Slovak Republic. The SHI was developed using a minimum soil data set, physical and chemical soil parameters in combination with environmental parameters (land use, gradients). The SHI is one numerical value accumulates information about the state of soil health and its ability to provide soil functions and thus ecosystems in the optimal range. The highest SHI values were determined at model localities used as arable land (Haplic Chernozem, Fluvisol) located in a warm climate at altitudes up to 200 meters above sea level. Ecosystems with very low and low value are mostly grasslands with mildly cold climate (Cambisol) and considerable slope, agroecosystem on low organic matter (Arenosol). Arable ecosystem SHI is also reduced in areas of geochemical anomalies and areas with anthropogenic load, where there is a higher content of risk elements. The SHI changes are mainly the result of changes in dynamic indicators such as soil response and soil bulk density.
