The Experts below are selected from a list of 180 Experts worldwide ranked by ideXlab platform
Chencheng Zhang - One of the best experts on this subject based on the ideXlab platform.
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using pedotransfer functions to estimate Soil hydraulic conductivity in the loess plateau of china
Catena, 2016Co-Authors: Chunlei Zhao, Mingan Shao, Xiaoxu Jia, Mubasher Nasir, Chencheng ZhangAbstract:Abstract Soil hydraulic conductivity ( K s ) is a crucial Soil Physical Property that not only influences Soil hydrological processes, but also the planning for vegetation recovery, irrigation practice and drainage design. However, K s data are often lacking at large-scale Soil database due to difficulties in direct measurement that is often labour intensive, time consuming and cost inefficient. The objective of this study was to compare the performance of different emerging methods [Multiple linear regression (MLR) and artificial neural network (ANN)] of K s prediction. The pedotransfer function (PTF) is one such method that is based on selected factors closely correlated with K s at regional scale. We collected disturbed and undisturbed Soil samples in the 0–40 cm Soil layer at 243 sites across the entire typical Loess Plateau of China (430,000 km 2 ) and then measured K s and the potentially related factors. The results showed that K s was normally distributed with moderate a spatial variation (CV = 67%). Correlation analysis indicated that bulk density (BD), saturated Soil water content (SSWC), clay content (Clay), silt content (Silt) and latitude were closely correlated ( p K s . Although the accuracies of MLR and ANN were equal in terms of estimating K s , the stability of PTF developed via ANN was not as good as that of MLR. Thus PTF developed via MLR, which included BD, Silt and Clay, was considered as the best model for estimating K s . There is a need to closely monitor the stability and repeatability of PTF during comparison and determination of PTF.
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prediction of bulk density of Soils in the loess plateau region of china
Surveys in Geophysics, 2014Co-Authors: Yunqiang Wang, Mingan Shao, Zhipeng Liu, Chencheng ZhangAbstract:Soil bulk density (BD) is a key Soil Physical Property that may affect the transport of water and solutes and is essential to estimate Soil carbon/nutrients reserves. However, BD data are often lacking in Soil databases due to the challenge of directly measuring BD, which is considered to be labor intensive, time consuming, and expensive especially for the lower layers of deep Soils such as those of the Chinese Loess Plateau region. We determined the factors that were closely correlated with BD at the regional scale and developed a robust pedotransfer function (PTF) for BD by measuring BD and potentially related Soil and environmental factors at 748 selected sites across the Loess Plateau of China (620,000 km2) at which we collected undisturbed and disturbed Soil samples from two Soil layers (0–5 and 20–25 cm). Regional BD values were normally distributed and demonstrated weak spatial variation (CV = 12 %). Pearson’s correlation and stepwise multiple linear regression analyses identified silt content, slope gradient (SG), Soil organic carbon content (SOC), clay content, slope aspect (SA), and altitude as the factors that were closely correlated with BD and that explained 25.8, 6.3, 5.8, 1.4, 0.3, and 0.3 % of the BD variation, respectively. Based on these closely correlated variables, a reasonably robust PTF was developed for BD using multiple linear regression, which performed equally with the artificial neural network method in the current study. The inclusion of topographic factors significantly improved the predictive capability of the BD PTF and in which SG was an important input variable that could be used in place of SA and altitude without compromising its capability for predicting BD. Thus, the developed PTF with only four input variables (clay, silt, SOC, SG), including their common transformations and interactive terms, predicted BD with reasonable accuracy and is thus useful for most applications on the Loess Plateau of China. More attention should be given to the role of topography when developing PTFs for BD prediction. Testing of the developed PTF for use in other loess regions in the world is required.
Mingan Shao - One of the best experts on this subject based on the ideXlab platform.
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using pedotransfer functions to estimate Soil hydraulic conductivity in the loess plateau of china
Catena, 2016Co-Authors: Chunlei Zhao, Mingan Shao, Xiaoxu Jia, Mubasher Nasir, Chencheng ZhangAbstract:Abstract Soil hydraulic conductivity ( K s ) is a crucial Soil Physical Property that not only influences Soil hydrological processes, but also the planning for vegetation recovery, irrigation practice and drainage design. However, K s data are often lacking at large-scale Soil database due to difficulties in direct measurement that is often labour intensive, time consuming and cost inefficient. The objective of this study was to compare the performance of different emerging methods [Multiple linear regression (MLR) and artificial neural network (ANN)] of K s prediction. The pedotransfer function (PTF) is one such method that is based on selected factors closely correlated with K s at regional scale. We collected disturbed and undisturbed Soil samples in the 0–40 cm Soil layer at 243 sites across the entire typical Loess Plateau of China (430,000 km 2 ) and then measured K s and the potentially related factors. The results showed that K s was normally distributed with moderate a spatial variation (CV = 67%). Correlation analysis indicated that bulk density (BD), saturated Soil water content (SSWC), clay content (Clay), silt content (Silt) and latitude were closely correlated ( p K s . Although the accuracies of MLR and ANN were equal in terms of estimating K s , the stability of PTF developed via ANN was not as good as that of MLR. Thus PTF developed via MLR, which included BD, Silt and Clay, was considered as the best model for estimating K s . There is a need to closely monitor the stability and repeatability of PTF during comparison and determination of PTF.
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prediction of bulk density of Soils in the loess plateau region of china
Surveys in Geophysics, 2014Co-Authors: Yunqiang Wang, Mingan Shao, Zhipeng Liu, Chencheng ZhangAbstract:Soil bulk density (BD) is a key Soil Physical Property that may affect the transport of water and solutes and is essential to estimate Soil carbon/nutrients reserves. However, BD data are often lacking in Soil databases due to the challenge of directly measuring BD, which is considered to be labor intensive, time consuming, and expensive especially for the lower layers of deep Soils such as those of the Chinese Loess Plateau region. We determined the factors that were closely correlated with BD at the regional scale and developed a robust pedotransfer function (PTF) for BD by measuring BD and potentially related Soil and environmental factors at 748 selected sites across the Loess Plateau of China (620,000 km2) at which we collected undisturbed and disturbed Soil samples from two Soil layers (0–5 and 20–25 cm). Regional BD values were normally distributed and demonstrated weak spatial variation (CV = 12 %). Pearson’s correlation and stepwise multiple linear regression analyses identified silt content, slope gradient (SG), Soil organic carbon content (SOC), clay content, slope aspect (SA), and altitude as the factors that were closely correlated with BD and that explained 25.8, 6.3, 5.8, 1.4, 0.3, and 0.3 % of the BD variation, respectively. Based on these closely correlated variables, a reasonably robust PTF was developed for BD using multiple linear regression, which performed equally with the artificial neural network method in the current study. The inclusion of topographic factors significantly improved the predictive capability of the BD PTF and in which SG was an important input variable that could be used in place of SA and altitude without compromising its capability for predicting BD. Thus, the developed PTF with only four input variables (clay, silt, SOC, SG), including their common transformations and interactive terms, predicted BD with reasonable accuracy and is thus useful for most applications on the Loess Plateau of China. More attention should be given to the role of topography when developing PTFs for BD prediction. Testing of the developed PTF for use in other loess regions in the world is required.
Chunlei Zhao - One of the best experts on this subject based on the ideXlab platform.
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using pedotransfer functions to estimate Soil hydraulic conductivity in the loess plateau of china
Catena, 2016Co-Authors: Chunlei Zhao, Mingan Shao, Xiaoxu Jia, Mubasher Nasir, Chencheng ZhangAbstract:Abstract Soil hydraulic conductivity ( K s ) is a crucial Soil Physical Property that not only influences Soil hydrological processes, but also the planning for vegetation recovery, irrigation practice and drainage design. However, K s data are often lacking at large-scale Soil database due to difficulties in direct measurement that is often labour intensive, time consuming and cost inefficient. The objective of this study was to compare the performance of different emerging methods [Multiple linear regression (MLR) and artificial neural network (ANN)] of K s prediction. The pedotransfer function (PTF) is one such method that is based on selected factors closely correlated with K s at regional scale. We collected disturbed and undisturbed Soil samples in the 0–40 cm Soil layer at 243 sites across the entire typical Loess Plateau of China (430,000 km 2 ) and then measured K s and the potentially related factors. The results showed that K s was normally distributed with moderate a spatial variation (CV = 67%). Correlation analysis indicated that bulk density (BD), saturated Soil water content (SSWC), clay content (Clay), silt content (Silt) and latitude were closely correlated ( p K s . Although the accuracies of MLR and ANN were equal in terms of estimating K s , the stability of PTF developed via ANN was not as good as that of MLR. Thus PTF developed via MLR, which included BD, Silt and Clay, was considered as the best model for estimating K s . There is a need to closely monitor the stability and repeatability of PTF during comparison and determination of PTF.
S M Eltaib - One of the best experts on this subject based on the ideXlab platform.
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effective porosity of paddy Soils as an estimation of its saturated hydraulic conductivity
Geoderma, 2004Co-Authors: W Aimrun, M S M Amin, S M EltaibAbstract:Abstract Soil saturated hydraulic conductivity ( K s ) is an important Soil Physical Property. Some laboratory and field methods are expensive, time consuming and labour intensive. Indirect methods such as pedo-transfer functions (PTF) are available. Effective porosity or macroporosity (O e ) is approximately equals to porosity minus volumetric Soil water content at the field capacity. According to Kozeny–Carman equation, K s could be evaluated using O e . Franzmeier estimated the K s from O e based on Ahuja et al. He found a strong relationship between O e and K s . This paper presents results of a study to characterize the effective porosity in lowland paddy fields and to show the possibility of using the effective porosity (O e ) in estimating the saturated hydraulic conductivity ( K s ). Soil in lowland paddy fields forms its horizon as topSoil, hardpan and subSoil. Soil samples were collected randomly within a 2300-ha rice cultivation area where there are five dominant Soil series. A total of 408 Soil samples were taken from 136 sampling points and at three depths, namely, the topSoil, hardpan and subSoil. K s values were measured in the laboratory using the falling head method. Soil bulk density and moisture content at −66 kPa were determined. The O e was then calculated using the difference of the total porosity (O) minus the volumetric moisture content at −66 kPa. The K s values ranged from 5.35×10 −4 to 8.77×10 −2 m day −1 . The D b varied from 0.62 to 1.91 Mg m −3 , and the values of the D p ranged from 1.10 to 2.89 Mg m −3 . The O ranged from 0.17 to 0.68 m 3 m −3 . The results of the O e of the samples in this study were obtained by calculating the difference of the total porosity and volumetric moisture content at field capacity. For clayey Soils, field capacity is taken at the suction of −66 kPa. The O e varied from 0.05 to 0.55 m 3 m −3 , with the mean value of 0.24 m 3 m −3 . The regression equation of a power function shows a highly significant regression coefficient, r 2 of 0.50 ( n =400). This indicates that there is a strong relationship between K s and O e for the lowland paddy Soils in the study area.
J Geiger - One of the best experts on this subject based on the ideXlab platform.
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a gis framework for surface layer Soil moisture estimation combining satellite radar measurements and land surface modeling with Soil Physical Property estimation
Environmental Modelling and Software, 2007Co-Authors: Michael A Tischler, Matthew Garcia, Christa D Peterslidard, M S Moran, Scott N Miller, David P Thoma, Sujay V Kumar, J GeigerAbstract:A GIS framework, the Army Remote Moisture System (ARMS), has been developed to link the Land Information System (LIS), a high performance land surface modeling and data assimilation system, with remotely sensed measurements of Soil moisture to provide a high resolution estimation of Soil moisture in the near surface. ARMS uses available Soil (Soil texture, porosity, K"s"a"t), land cover (vegetation type, LAI, Fraction of Greenness), and atmospheric data (Albedo) in standardized vector and raster GIS data formats at multiple scales, in addition to climatological forcing data and precipitation. PEST (Parameter EStimation Tool) was integrated into the process to optimize Soil porosity and saturated hydraulic conductivity (K"s"a"t), using the remotely sensed measurements, in order to provide a more accurate estimate of the Soil moisture. The modeling process is controlled by the user through a graphical interface developed as part of the ArcMap component of ESRI ArcGIS.