Soil salinization is one of the most common land degradation processes. In this study, spectral measurements of saline soil samples collected from the Yellow River Delta region of China were conducted in laboratory an...Soil salinization is one of the most common land degradation processes. In this study, spectral measurements of saline soil samples collected from the Yellow River Delta region of China were conducted in laboratory and hyperspectral data were acquired from an EO-1 Hyperion sensor to quantitatively map soil salinity in the region. A soil salinity spectral index (SSI) was constructed from continuum-removed reflectance (CR-reflectance) at 2052 and 2203 nm, to analyze the spectral absorption features of the salt-affected soils. There existed a strong correlation (r = 0.91) between the SSI and soil salt content (SSC). Then, a model for estimation of SSC with SSI was established using univariate regression and validation of the model yielded a root mean square error (RMSE) of 0.986 and an R2 of 0.873. The model was applied to a Hyperion reflectance image on a pixel-by-pixel basis and the resulting quantitative salinity map was validated successfully with RMSE = 1.921 and R2 = 0.627. These suggested that the satellite hyperspectral data had the potential for predicting SSC in a large area.展开更多
Concentrations of Iron (Fe), As, and Cu in soil samples from the fields near the Baoshan Mine in Hunan Province, China, were analyzed and soil spectral reflectance was measured with an ASD FieldSpec FR spectroradiomet...Concentrations of Iron (Fe), As, and Cu in soil samples from the fields near the Baoshan Mine in Hunan Province, China, were analyzed and soil spectral reflectance was measured with an ASD FieldSpec FR spectroradiometer (Analytical Spectral Devices, Inc., USA) under laboratory condition. Partial least square regression (PLSR) models were constructed for predicting soil metal concentrations. The data pre-processing methods, first and second derivatives (FD and SD), baseline correction (BC), standard normal variate (SNV), multiplicative scatter correction (MSC), and continuum removal (CR), were used for the spectral reflectance data pretreatments. Then, the prediction results were evaluated by relative root mean square error (RRMSE) and coefficients of determination (R 2 ). According to the criteria of minimal RRMSE and maximal R 2 , the PLSR models with the FD pretreatment (RRMSE = 0.24, R 2 = 0.61), SNV pretreatment (RRMSE = 0.08, R 2 = 0.78), and BC-pretreatment (RRMSE = 0.20, R 2 = 0.41) were considered as the final models for predicting As, Fe, and Cu, respectively. Wavebands at around 460, 1 400, 1 900, and 2 200 nm were selected as important spectral variables to construct final models. In conclusion, concentrations of heavy metals in contaminated soils could be indirectly assessed by soil spectra according to the correlation between the spectrally featureless components and Fe; therefore, spectral reflectance would be an alternative tool for monitoring soil heavy metals contamination.展开更多
Soil visible-near infrared diffuse reflectance spectroscopy(vis-NIR DRS)has become an important area of research in the fields of remote and proximal soil sensing.The technique is considered to be particularly useful ...Soil visible-near infrared diffuse reflectance spectroscopy(vis-NIR DRS)has become an important area of research in the fields of remote and proximal soil sensing.The technique is considered to be particularly useful for acquiring data for soil digital mapping,precision agriculture and soil survey.In this study,1581 soil samples were collected from 14 provinces in China,including Tibet,Xinjiang,Heilongjiang,and Hainan.The samples represent 16 soil groups of the Genetic Soil Classification of China.After air-drying and sieving,the diffuse reflectance spectra of the samples were measured under laboratory conditions in the range between 350 and 2500 nm using a portable vis-NIR spectrometer.All the soil spectra were smoothed using the Savitzky-Golay method with first derivatives before performing multivariate data analyses.The spectra were compressed using principal components analysis and the fuzzy k-means method was used to calculate the optimal soil spectral classification.The scores of the principal component analyses were classified into five clusters that describe the mineral and organic composition of the soils.The results on the classification of the spectra are comparable to the results of other similar research.Spectroscopic predictions of soil organic matter concentrations used a combination of the soil spectral classification with multivariate calibration using partial least squares regression(PLSR).This combination significantly improved the predictions of soil organic matter(R2=0.899;RPD=3.158)compared with using PLSR alone(R2=0.697;RPD=1.817).展开更多
Soil salinization is a land degradation process that leads to reduced agricultural yields. This study investigated the method that can best predict electrical conductivity (EC) in dry soils using individual bands, a n...Soil salinization is a land degradation process that leads to reduced agricultural yields. This study investigated the method that can best predict electrical conductivity (EC) in dry soils using individual bands, a normalized difference salinity index (NDSI), partial least squares regression (PLSR), and bagging PLSR. Soil spectral reflectance of dried, ground, and sieved soil samples containing varying amounts of EC was measured using an ASD FieldSpec spectrometer in a darkroom. Predictive models were computed using a training dataset. An independent validation dataset was used to validate the models. The results showed that good predictions could be made based on bagging PLSR using first derivative reflectance (validation R2 = 0.85), PLSR using untransformed reflectance (validation R2 = 0.70), NDSI (validation R2 = 0.65), and the untransformed individual band at 2257 nm (validation R2 = 0.60) predictive models. These suggested the potential of mapping soil salinity using airborne and/or satellite hyperspectral data during dry seasons.展开更多
基金Supported by the Open Foundation of State Key Laboratory of Remote Sensing Science,the Institute of Remote Sensing Applications of the Chinese Academy of Sciences and Beijing Normal University (No.2009KFJJ002)the National Natural Science Foundation of China (No.30590370)
文摘Soil salinization is one of the most common land degradation processes. In this study, spectral measurements of saline soil samples collected from the Yellow River Delta region of China were conducted in laboratory and hyperspectral data were acquired from an EO-1 Hyperion sensor to quantitatively map soil salinity in the region. A soil salinity spectral index (SSI) was constructed from continuum-removed reflectance (CR-reflectance) at 2052 and 2203 nm, to analyze the spectral absorption features of the salt-affected soils. There existed a strong correlation (r = 0.91) between the SSI and soil salt content (SSC). Then, a model for estimation of SSC with SSI was established using univariate regression and validation of the model yielded a root mean square error (RMSE) of 0.986 and an R2 of 0.873. The model was applied to a Hyperion reflectance image on a pixel-by-pixel basis and the resulting quantitative salinity map was validated successfully with RMSE = 1.921 and R2 = 0.627. These suggested that the satellite hyperspectral data had the potential for predicting SSC in a large area.
基金Project supported by the National Natural Science Foundation of China (No. 40571130)the Natural Science Foundation of Shanghai, China (No. 07ZR14032)
文摘Concentrations of Iron (Fe), As, and Cu in soil samples from the fields near the Baoshan Mine in Hunan Province, China, were analyzed and soil spectral reflectance was measured with an ASD FieldSpec FR spectroradiometer (Analytical Spectral Devices, Inc., USA) under laboratory condition. Partial least square regression (PLSR) models were constructed for predicting soil metal concentrations. The data pre-processing methods, first and second derivatives (FD and SD), baseline correction (BC), standard normal variate (SNV), multiplicative scatter correction (MSC), and continuum removal (CR), were used for the spectral reflectance data pretreatments. Then, the prediction results were evaluated by relative root mean square error (RRMSE) and coefficients of determination (R 2 ). According to the criteria of minimal RRMSE and maximal R 2 , the PLSR models with the FD pretreatment (RRMSE = 0.24, R 2 = 0.61), SNV pretreatment (RRMSE = 0.08, R 2 = 0.78), and BC-pretreatment (RRMSE = 0.20, R 2 = 0.41) were considered as the final models for predicting As, Fe, and Cu, respectively. Wavebands at around 460, 1 400, 1 900, and 2 200 nm were selected as important spectral variables to construct final models. In conclusion, concentrations of heavy metals in contaminated soils could be indirectly assessed by soil spectra according to the correlation between the spectrally featureless components and Fe; therefore, spectral reflectance would be an alternative tool for monitoring soil heavy metals contamination.
基金This project was funded in part by the National High Technology Research and Development Program (Grant No. 2013AA102301)the program for New Century Talents in University (Grant No. NCET-10-0694), and the National Natural Science Foundation of China (Grant No. 41271234)
文摘Soil visible-near infrared diffuse reflectance spectroscopy(vis-NIR DRS)has become an important area of research in the fields of remote and proximal soil sensing.The technique is considered to be particularly useful for acquiring data for soil digital mapping,precision agriculture and soil survey.In this study,1581 soil samples were collected from 14 provinces in China,including Tibet,Xinjiang,Heilongjiang,and Hainan.The samples represent 16 soil groups of the Genetic Soil Classification of China.After air-drying and sieving,the diffuse reflectance spectra of the samples were measured under laboratory conditions in the range between 350 and 2500 nm using a portable vis-NIR spectrometer.All the soil spectra were smoothed using the Savitzky-Golay method with first derivatives before performing multivariate data analyses.The spectra were compressed using principal components analysis and the fuzzy k-means method was used to calculate the optimal soil spectral classification.The scores of the principal component analyses were classified into five clusters that describe the mineral and organic composition of the soils.The results on the classification of the spectra are comparable to the results of other similar research.Spectroscopic predictions of soil organic matter concentrations used a combination of the soil spectral classification with multivariate calibration using partial least squares regression(PLSR).This combination significantly improved the predictions of soil organic matter(R2=0.899;RPD=3.158)compared with using PLSR alone(R2=0.697;RPD=1.817).
基金Project supported by the Agricultural Research Council-Institute for Soil, Climate and Water (ARC-ISCW) of South Africa (No.GW51/072)the National Research Foundation (NRF) of South Africa (No.GW 51/083/01)the Water Research Commission (WRC)of South Africa (No.K5/1849)
文摘Soil salinization is a land degradation process that leads to reduced agricultural yields. This study investigated the method that can best predict electrical conductivity (EC) in dry soils using individual bands, a normalized difference salinity index (NDSI), partial least squares regression (PLSR), and bagging PLSR. Soil spectral reflectance of dried, ground, and sieved soil samples containing varying amounts of EC was measured using an ASD FieldSpec spectrometer in a darkroom. Predictive models were computed using a training dataset. An independent validation dataset was used to validate the models. The results showed that good predictions could be made based on bagging PLSR using first derivative reflectance (validation R2 = 0.85), PLSR using untransformed reflectance (validation R2 = 0.70), NDSI (validation R2 = 0.65), and the untransformed individual band at 2257 nm (validation R2 = 0.60) predictive models. These suggested the potential of mapping soil salinity using airborne and/or satellite hyperspectral data during dry seasons.
