There are a series of special mountain soils on the Tibetan Plateau of China in an alpine environment for the high altitude. However, very few studies have focused on major soil elements in relation to soil formation ...There are a series of special mountain soils on the Tibetan Plateau of China in an alpine environment for the high altitude. However, very few studies have focused on major soil elements in relation to soil formation in this area. Aluminum （Al）, iron （Fe）, calcium （Ca）, sodium （Na）, potassium （K） and magnesium （Mg） contents of 237 topsoil samples covering a 2.8-km altitudinal gradient in uncultivated areas along the Qinghai-Tibet Railway of China were measured using inductively coupled plasma atomic emission spectroscopy. The spatial distribution of the elements and its relationship to the parent rocks and climatic parameters were analyzed. Soils along the gradient are derived from a range of parent materials, but most are less than 30 cm deep with little development （Cambisols）. Soil Al, Fe and Mg contents showed a decreasing trend from the start station （Xining Station） to end station （Lhasa Station） of the Qinghai-Tibet Railway, whereas soil K and Na contents were relative stable from Xining Station to the Kunlun Mountains and then increased gradually. Soil Ca content was lower in the southern part of the Tanggula Mountains. The major soil element contents clearly reflected the parent rock and climatic influences. Soils with higher Ca content appeared in areas with Ca-Mg carbonate rocks, soils with higher Al were found in areas with silicate-rich and high-Al silicate clastic rocks and silicate-rich aluminosilicate loose sediments. Soils with higher K and Na contents appeared in areas with high-K, high-Na and silicate-rich aluminosilicate rocks. Soil Na and K contents were affected by temperature, whereas the contents of Mg, Fe, Ca and Al were more affected by precipitation. Soil Na and K contents increased with increasing temperatures, whereas the contents of Mg, Fe, Ca and Al decreased with increasing precipitation. This analysis provides a relationship between soil properties and rapidly changing environmental conditions. The data can be used to investigate the effect of the climate or land use change on soil properties.展开更多
Visible near-infrared (vis-NIR) and portable X-ray fluorescence (pXRF) spectrometers have been increasingly utilized for predicting soil properties worldwide. However, only a few studies have focused on splitting the ...Visible near-infrared (vis-NIR) and portable X-ray fluorescence (pXRF) spectrometers have been increasingly utilized for predicting soil properties worldwide. However, only a few studies have focused on splitting the predictive models by horizons to evaluate prediction performance and systematically compare prediction performance for A, B, and combined A+B horizons. Therefore, we investigated the performance of pXRF and vis-NIR spectra, as individual or combined, for predicting the clay, silt, sand, total carbon (TC), and pH of soils developed in loess, and compared their prediction performance for A, B, and A+B horizons. Soil samples (176 in A horizon and 172 in B horizon) were taken from Mollisols and Alfisols in 136 pedons in Wisconsin, USA and analyzed for clay, silt, sand, pH, and TC. The pXRF and vis-NIR spectrometers were used to measure the pXRF and vis-NIR soil spectra. Data were separated into calibration (n = 244, 70%) and validation (n = 104, 30%) datasets. The Savitzky-Golay filter was applied to preprocess the pXRF and vis-NIR spectra, and the first 10 principal components (PCs) were selected through principal component analysis (PCA). Five types of predictor, i.e., PCs from vis-NIR spectra, pXRF of beams at 0–40 and 0–10 keV (XRF40 and XRF10, respectively) spectra, combined XRF40 and XRF10 (XRF40+XRF10) spectra, and combined XRF40, XRF10, and vis-NIR (XRF40+XRF10+vis-NIR) spectra, were compared for predicting soil properties using a machine learning algorithm (Cubist model). A multiple linear regression (MLR) model was applied to predict clay, silt, sand, pH, and TC using pXRF elements. The results suggested that pXRF spectra had better prediction performance for clay, silt, and sand, whereas vis-NIR spectra produced better TC and pH predictions. The best prediction performance for sand (R2= 0.97), silt (R2= 0.95), and clay (R2= 0.84) was achieved using vis-NIR+XRF40+XRF10 spectra in B horizon, whereas the best prediction performance for TC (R2= 0.93) and pH (R2= 0.79) was achieved using vis-NIR+XRF40+XRF10 spectra in A+B horizon. For all soil properties, the best MLR model had a lower prediction accuracy than the Cubist model. It was concluded that pXRF and vis-NIR spectra can be successfully applied for predicting clay, silt, sand, pH, and TC with high accuracy for soils developed in loess, and that spectral models should be developed for different horizons to achieve high prediction accuracy.展开更多
基金supported by the Strategic Priority Research Program of Chinese Academy of Sciences (No.XDB03030500)the National Key Technology Research and Development Program of China (No.2013BAC04B02)the National Natural Science Foundation of China (Nos.40801042 and 90202012)
文摘There are a series of special mountain soils on the Tibetan Plateau of China in an alpine environment for the high altitude. However, very few studies have focused on major soil elements in relation to soil formation in this area. Aluminum （Al）, iron （Fe）, calcium （Ca）, sodium （Na）, potassium （K） and magnesium （Mg） contents of 237 topsoil samples covering a 2.8-km altitudinal gradient in uncultivated areas along the Qinghai-Tibet Railway of China were measured using inductively coupled plasma atomic emission spectroscopy. The spatial distribution of the elements and its relationship to the parent rocks and climatic parameters were analyzed. Soils along the gradient are derived from a range of parent materials, but most are less than 30 cm deep with little development （Cambisols）. Soil Al, Fe and Mg contents showed a decreasing trend from the start station （Xining Station） to end station （Lhasa Station） of the Qinghai-Tibet Railway, whereas soil K and Na contents were relative stable from Xining Station to the Kunlun Mountains and then increased gradually. Soil Ca content was lower in the southern part of the Tanggula Mountains. The major soil element contents clearly reflected the parent rock and climatic influences. Soils with higher Ca content appeared in areas with Ca-Mg carbonate rocks, soils with higher Al were found in areas with silicate-rich and high-Al silicate clastic rocks and silicate-rich aluminosilicate loose sediments. Soils with higher K and Na contents appeared in areas with high-K, high-Na and silicate-rich aluminosilicate rocks. Soil Na and K contents were affected by temperature, whereas the contents of Mg, Fe, Ca and Al were more affected by precipitation. Soil Na and K contents increased with increasing temperatures, whereas the contents of Mg, Fe, Ca and Al decreased with increasing precipitation. This analysis provides a relationship between soil properties and rapidly changing environmental conditions. The data can be used to investigate the effect of the climate or land use change on soil properties.
基金supported by the Scientific Research Projects(BAP)(No.2019-2757)of Eskisehir Osmangazi University for postdoc research at the Department of Soil Science,University of Wise on sin-Madison.
文摘Visible near-infrared (vis-NIR) and portable X-ray fluorescence (pXRF) spectrometers have been increasingly utilized for predicting soil properties worldwide. However, only a few studies have focused on splitting the predictive models by horizons to evaluate prediction performance and systematically compare prediction performance for A, B, and combined A+B horizons. Therefore, we investigated the performance of pXRF and vis-NIR spectra, as individual or combined, for predicting the clay, silt, sand, total carbon (TC), and pH of soils developed in loess, and compared their prediction performance for A, B, and A+B horizons. Soil samples (176 in A horizon and 172 in B horizon) were taken from Mollisols and Alfisols in 136 pedons in Wisconsin, USA and analyzed for clay, silt, sand, pH, and TC. The pXRF and vis-NIR spectrometers were used to measure the pXRF and vis-NIR soil spectra. Data were separated into calibration (n = 244, 70%) and validation (n = 104, 30%) datasets. The Savitzky-Golay filter was applied to preprocess the pXRF and vis-NIR spectra, and the first 10 principal components (PCs) were selected through principal component analysis (PCA). Five types of predictor, i.e., PCs from vis-NIR spectra, pXRF of beams at 0–40 and 0–10 keV (XRF40 and XRF10, respectively) spectra, combined XRF40 and XRF10 (XRF40+XRF10) spectra, and combined XRF40, XRF10, and vis-NIR (XRF40+XRF10+vis-NIR) spectra, were compared for predicting soil properties using a machine learning algorithm (Cubist model). A multiple linear regression (MLR) model was applied to predict clay, silt, sand, pH, and TC using pXRF elements. The results suggested that pXRF spectra had better prediction performance for clay, silt, and sand, whereas vis-NIR spectra produced better TC and pH predictions. The best prediction performance for sand (R2= 0.97), silt (R2= 0.95), and clay (R2= 0.84) was achieved using vis-NIR+XRF40+XRF10 spectra in B horizon, whereas the best prediction performance for TC (R2= 0.93) and pH (R2= 0.79) was achieved using vis-NIR+XRF40+XRF10 spectra in A+B horizon. For all soil properties, the best MLR model had a lower prediction accuracy than the Cubist model. It was concluded that pXRF and vis-NIR spectra can be successfully applied for predicting clay, silt, sand, pH, and TC with high accuracy for soils developed in loess, and that spectral models should be developed for different horizons to achieve high prediction accuracy.
