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Precise irrigation has been a highly urgent need to realize a quick, accurate, and non-destructive acquisition for the distribution of moisture content in reclaimed soil, particularly in coal mining subsidence areas. Consequently, traditional measurement can be replaced with Ground-Penetrating Radar (GPR), a new type of near-earth microwave remote sensing with large-scale continuous detection, fast, non-destructive, and low-cost. GPR has also been widely used in the detection of water content in soil. However, GPR is rarely used in the detection of water content in reclaimed soil, due to the complex composition of reclaimed soil and the fast attenuation of electromagnetic waves in cohesive soil. Fortunately, spectrum analysis can convert the radar data (signal change) from the time domain to the frequency domain. Energy distribution of signal frequency can be used to alleviate a large wave speed error under different media. In this study, the soil of a subsided reclamation area in Huaibei City, Anhui Province of China was taken as the research object. A control group was set up in the surrounding normal areas. The Chirp-Z-transform (CZT) was used to find the characteristic frequency. Rayleigh scattering was selected to analyze the characteristic spectrum response of radar signals under volumetric water content in soil. Various regressions were used to establish inversion models of water cut. The results showed that the frequency of peak (FP) using the Rayleigh scattering had an excellent correlation with the volumetric water content of reclaimed soil, where the correlation coefficient reached 0.90, indicating that the radar signal FP was feasible to invert the soil volumetric water content of the reclaimed area. Moreover, the CZT effectively identified the characteristic frequency in a higher resolution without changing the original frequency spectrum. The correlation coefficient between FP and soil moisture content was improved from 0.77 to 0.93, compared with the fast Fourier transform (FFT). In addition, the spectrum of radar signal in the reclaimed area demonstrated a greater drift than that in the non-reclaimed area under the same change of water content. The bulk density was also one of the factors that affect the radar signal. The highest accuracy was achieved in the exponential regression, where the verification precisions were R2 =0.84, and RMSE=1.97%, showing that the frequency spectrum analysis was an effective way to invert the moisture content of cultivated soil. When large construction equipment was used to compact the reconstructed soil, there will be a larger influence on the distribution of soil moisture content and the fertility of reclaimed soil. The findings can provide an insightful theoretical basis and technical support for rapidly monitoring the soil quality and precision irrigation in reclaimed areas. [ABSTRACT FROM AUTHOR]

A hyperspectral reflectance can be expected as an alternative method to quickly predict the concentration of heavy metals in soil at present. However, excessive band counting of hyperspectral data can lead to information redundancy during the quantitative inversion of surface parameters, and thereby to make it difficult to extract valid data from the spectrum. In this study, two aspects were addressed to solve spectral redundancy during the inversion of Pb content in soil: One is to extract the characteristics of iron oxides from the soil spectrum, according to the adsorption mechanism of Pb element in soil, in order to reduce data redundancy, while enhance the mechanism of inversion. Another is to propose an Improved Genetic Algorithm (IGA), in order to deal with the premature convergence of traditional Genetic Algorithm (GA), further to improve the extracting ability of algorithm for the required spectrum. Taking the Xiong’an New Area near Beijing, a national new-district in China, as the research region, the field experiment was carried out to quickly collect the Pb content of soil in the farmland, resulting from the rapid construction and developing facilities in recent years. 70 soil samples from the Xiong’an farmland were selected to establish the inversion model of Pb content. SVC HR1024I type ground object spectrometer was used to measure the raw hyperspectral reflectance of soil samples according to the national standard procedure in field farmland. After that, the collected soil samples were transferred to the laboratory for chemical analysis, particularly for the Pb content. Before modeling, the methods, including Savitzky-Golay filter, envelope removal and first-order difference, were used to pretreat with segmented the curves of soil reflectance. Partial Least Squares Regression (PLSR) was selected to serve as inversion model, in order to well deal with the strong correlation between hyperspectral bands. The models were also constructed in terms of full spectrum and characteristic bands of iron oxides, to verify the effectiveness of extracting method from characteristic bands of iron oxides during the inversion process. In full spectrum, the average values of R2, RPD, RMSE in the IGA-PLSR models were 0.440, 1.366, and 4.012 mg/kg, respectively. In the characteristic bands of iron oxides, the average values of R2, RPD, RMSE in the IGA-PLSR models were 0.822, 2.377, and 2.221 mg/kg, respectively. It infers that the models using the characteristic bands of iron oxides were much better than those using the full spectrum, indicating that the extraction for the characteristic bands of iron oxides can be contributed to reduce the redundancy of spectral information, and thereby to significantly improve the accuracy of the inversion model. Consequently, a final inversion model was selected from the optimal model for the improvement of inversion algorithm, based on the utilization of the characteristic bands of iron oxides to construct the GA-PLSR and IGA-PLSR models with different iterations. In the GA-PLSR models, the average values of R2, RPD, RMSE in the optimal model were 0.788, 2.140, and 2.447 mg/kg, respectively, whereas, the IGA-PLSR model achieved the highest accuracy, where the average values of R², RPD, RMSE were 0.837, 2.403, and 2.063 mg/kg, respectively. Therefore, the optimal model of IGA-PLSR was chosen to estimate the Pb content in soil for the Xiong’an farmland area. The IGA-PLSR method has demonstrated better performance in the inversion of Pb content in soil, indicating that the proposed method was valid to improve genetic algorithm. At last, a visualized profile can be conducted for the changes of RMSE during the 1000 iterations of two models. The visualization revealed that the RMSE in the GA-PLSR model decreased monotonously during the operation process, whereas, the RMSE in the IGA-PLSR model fluctuated greatly at the beginning of the iteration, showing a certain probability to jump out from the local solution area to a better solution area at the later stage, while meaning that the problem of “premature convergence” was solved properly. This study revealed that two aspects, including the extraction of iron oxides spectral features associated with Pb absorption, and the improved algorithm of bands selection, both can be used to enhance the accuracy of Pb content estimation in soil. The proposed algorithm can provide a sound reference to real-time monitor the variation of heavy metal content in soil for Xiong’an area, China. [ABSTRACT FROM AUTHOR]

Soil salinization is a worldwide environmental problem with severe economic and social consequences. It is necessary to develop a soil-salinity-estimation model to project the spatial distribution of soil salinity. In this study, the spectra characteristics and salinization parameters of the soils in the different layers in northern Ningxia Yinchuan were measured. Based on the soil science and geostatistics methods, the sensitive wavelengths and the best transformation forms of spectral reflectance to salinity parameters (soil pH value, electric conductivity(EC) and salt ions) in 0-5 cm and 0-20 cm were selected respectively, and then the soil salinity parameters monitoring model was established. The results showed that: 1) The variation trend of soil spectral characteristic curves of five different types and different salinization degrees was similar. Saline soil had the highest spectral reflectance, and slightly SO42- type soil had the lowest reflectance. Salinized soils had good spectral response characteristics in visible and near infrared spectra region. The reflectance had the most closely related to the content of SO42- in all salinity parameters (coefficient of correlation was 0.910 4) of 0-5cm layer. There were non-significant relationships between reflectance and the contents of CO32-, HCO3- and Cl-. The average coefficient of correlation of reflectance and SO42- in 0-20 cm layer was decreased 0.232 2 than in 0-5 cm. However, the average coefficient of correlations of reflectance and Cl-, K+, HCO3-, EC were increased 0.433 1, -0.343 3, 0.303 2, 0.296 2, and got significant level. 2) After the spectral reflectance were transformed in different methods, the correlation between the most sensitive wavelengths and each salinity parameters were enhanced to some extent, especially after the (R)′ (first order differential conversion) and (CR)′ (the first order differential after continuous removal). In 0-5 cm layer, (R)′ was the optimal transformation forms of reflectance for pH value, SO42-, K+, Mg2+, and the (CR)′ was best for EC, CO32-, HCO3-, Cl-, Na+ and Ca2+. In 0-20 cm layer, (CR)′ was the optimal index for soil pH value, [lg(1/R)]′ was the optimal index for EC, HCO3-, SO42-, Na+, Ca2+ were, and (R)′ were the best one for CO32-, K+, Mg2+. In addition, there are different sensitive wavelengths in different soil layers about the same salinity parameters. 3) In the models of PLSR (Partial least squares regression), the average determination coefficient (R2) between sensitive wavelengths of 10 salinity parameters were 0.820 8 and 0.890 7 in 0-5cm and 0-20 cm soil layer, respectively. The determination coefficient between sensitive reflectance and SO42- was 0.967 6 in 0-5 cm layer, and it was higher 0.077 6 than in 0-20 cm layer. The numbers of sensitive wavelengths reduced and R2 decreased that used PLSR method to established prediction model than used the SR (step-wised regression) method, but the R2 of the SR method also got the significant level. The results conformed that the prediction accuracy of models for SO42-, CO32- in 0-20 cm were lower than in 0-5 cm. However, the prediction ability of models for other salinity parameters in 0-20 cm was stronger than in 0-5 cm. The study provide some beneficial references for regional soil salinity prediction and configuration of plant structure. [ABSTRACT FROM AUTHOR]

Soil salinization is a worldwide environmental problem with severe economic and social consequences. It is necessary to develop a soil-salinity-estimation model to project the spatial distribution of soil salinity. In this study, the spectra characteristics and salinization parameters of the soils in the different layers in northern Ningxia Yinchuan were measured. Based on the soil science and geostatistics methods, the sensitive wavelengths and the best transformation forms of spectral reflectance to salinity parameters (soil pH value, electric conductivity(EC) and salt ions) in 0-5 cm and 0-20 cm were selected respectively, and then the soil salinity parameters monitoring model was established. The results showed that: 1) The variation trend of soil spectral characteristic curves of five different types and different salinization degrees was similar. Saline soil had the highest spectral reflectance, and slightly SO42- type soil had the lowest reflectance. Salinized soils had good spectral response characteristics in visible and near infrared spectra region. The reflectance had the most closely related to the content of SO42- in all salinity parameters (coefficient of correlation was 0.910 4) of 0-5cm layer. There were non-significant relationships between reflectance and the contents of CO3 2-, HCO3 - and Cl-. The average coefficient of correlation of reflectance and SO42- in 0-20 cm layer was decreased 0.232 2 than in 0-5 cm. However, the average coefficient of correlations of reflectance and Cl-, K+, HCO3 -, EC were increased 0.433 1, -0.343 3, 0.303 2, 0.296 2, and got significant level. 2) After the spectral reflectance were transformed in different methods, the correlation between the most sensitive wavelengths and each salinity parameters were enhanced to some extent, especially after the (R)′ (first order differential conversion) and (CR)′ (the first order differential after continuous removal). In 0-5 cm layer, (R)′ was the optimal transformation forms of reflectance for pH value, SO42-, K+, Mg2+, and the (CR)′ was best for EC, CO3 2-, HCO3 -, Cl-, Na+ and Ca2+. In 0-20 cm layer, (CR)′ was the optimal index for soil pH value, [log(1/R)]′ was the optimal index for EC, HCO3 -, SO42-, Na+, Ca2+ were, and (R)′ were the best one for CO3 2-, K+, Mg2+. In addition, there are different sensitive wavelengths in different soil layers about the same salinity parameters. 3) In the models of PLSR (Partial least squares regression), the average determination coefficient (R2) between sensitive wavelengths of 10 salinity parameters were 0.820 8 and 0.890 7 in 0-5cm and 0-20 cm soil layer, respectively. The determination coefficient between sensitive reflectance and SO42- was 0.967 6 in 0-5 cm layer, and it was higher 0.077 6 than in 0-20 cm layer. The numbers of sensitive wavelengths reduced and R2 decreased that used PLSR method to established prediction model than used the SR (step-wised regression) method, but the R2 of the SR method also got the significant level. The results conformed that the prediction accuracy of models for SO42-, CO3 2-in 0-20 cm were lower than in 0-5 cm. However, the prediction ability of models for other salinity parameters in 0-20 cm was stronger than in 0-5 cm. The study provide some beneficial references for regional soil salinity prediction and configuration of plant structure. [ABSTRACT FROM AUTHOR]

Hyperspectral reflectance provides an alternative method to soil’s physical and chemical analysis in laboratory for the estimation of soil properties in large range. In order to achieve rapid measurement of the soil heavy metal content in well-facilitied capital farmland construction areas, 154 soil samples at 0-30 cm depth were collected as research objects, which were from well-facilitied capital farmland construction areas in Xinzheng City, Henan Province. The raw hyperspectral reflectance of soil samples was measured by the standard procedure with a spectrometer of ASD Field Spec3 equipped with a high intensity contact probe under the laboratory conditions. Meanwhile, the contents of Cr, Cd, Zn, Cu, and Pb in these soil samples were analyzed. The 116 samples were used for building hyperspectral estimation models and the other 38 samples were used for model validation. In the next, the raw spectral reflectance of 400-2400 nm after multiplicative scatter correction and Savitzky-Golay was transformed to 2 spectral indices, i.e. first order differential reflectance(FDR) and second order differential reflectance(SDR). The correlation coefficient between the 3 kinds of spectral indices and Cr, Cd, Zn, Cu, Pb content was analyzed by Pearson correlation analysis. Then, the correlation coefficients (P<0.01) of the 3 spectral indices were got in significant test, which could be used to extract significant bands. At last, we used partial least squares regression (PLSR) method to build quantitative inversion models of soil heavy metal content based on significant bands for this study area, respectively. The prediction accuracies of these models were assessed by comparing determination coefficients ( R2 ), root mean squared error (RMSE) and relative percent deviation (RPD) between the prediction and validation values. Based on these, the optimal models were selected. The spatial distribution map of Cr, Cd, Zn, Cu and Pb content was made by geographical interpolation. The results showed that, conducting the first order differential reflectance and second order differential reflectance transformation on raw soil spectral data, could highlight the hidden spectral reflectivity characteristics effectively. Among all of the 3 spectral indices based on PLSR model, the model of second order differential reflectance about Cr could obtain more robust prediction accuracies, its values of R2 was 0.88，its values of RPD was 1.68; the model of the raw spectral reflectance (R) of 400-2 400 nm after multiplicative scatter correction（MSC）and Savitzky-Golay（SG）about Cd、 Zn and Cu could obtain more robust prediction accuracies, their values of R2 were 0.70, 0.88 and 0.99, their values of RPD were 1.50, 2.05 and 3.36 respectively; Pb could obtain more robust prediction accuracies, their values of R2 was 0.93，its values of RPD was 3.16. The optimum model of soil heavy metal was used to interpolate the soil heavy metal content; the content of Zn was in accordance with the standard of soil environmental quality, and the contents of Cr, Cd, Cu and Pb met the soil environmental quality standard Ⅱ, but the contents in some well-facilitied capital farmland construction areas were more than the soil background value. This study provides a reference for the real-time monitoring of soil basic information in well-facilitied capital farmland construction areas by hyperspectral inversion model. [ABSTRACT FROM AUTHOR]

The availability of soil organic carbon density (SOCD) information is of great importance for the development of ecological agriculture and the study of global climate change. Compared with traditional laboratory analysis, Visible and near-infrared (VNIR) reflectance spectroscopy has proven to be a rapid, non-destructive and cost effective method for estimating a variety of soil properties. It has got rapid development and has been applied in the field of soil research. In the prediction of soil properties by using spectral information, however, traditional linear regression models often ignore the spatial heterogeneity and dependency of soil, and fail to consider the spatial structure of the error term. With the aims to fill the current gap, a total of 232 topsoil samples were collected in Jianghan Plain with their spectral reflectance and SOCD measured. Partial least squares regression (PLSR), ordinary kriging (OK), co-kriging (COK), and regression kriging (RK) were used to estimate SOCD by using differently pre-treated spectral reflectance. Due to the facts that spectral pretreatments are crucial to reduce the physical variability and particle size effect, and are helpful to remove both additive and multiplicative effects in the spectra, five combinations of spectral pretreatments were utilized while predicting SOCD with PLSR. They were Savitzky-Golay smoothing (S-G), S-G + Multiplicative Scatter Correction (MSC), S-G+Standard Normal Variate (SNV), S-G + first derivative (1st), S-G + second derivative (2nd), S-G+1st+MSC, S-G+2nd+MSC, S-G+1st+SNV, S-G+2nd +SNV. The prediction capabilities of the models were evaluated by R2, root mean squared error (RMSE), and ratio of performance to deviation (RPD). Results showed that the RK approach which utilized soil spectra information outperformed the others, with the highest R2-Pred 0.617 and RPD 1.614, and the lowest RMSEP 0.865 kg/m2. PLSR took the second place with R2-Pred 0.605, RPD 1.523 and RMSEP 0.917 kg/m2, which was also acceptable for SOCD prediction. COK and OK generally failed in the predictions of SOCD, with R2-Pred equaled to 0.007 and 0.004, RPD equaled to 0.903 and 0.874 and RMSEP equaled to 1.547 and 1.597 kg/m2, respectively. Results indicated that the RK model, which considered both the spectral reflectance and the spatial structure of the error term of multivariate linear regression model can improve the prediction accuracy of SOCD. The fundamental reasons could be that soil spectra are comprehensive reflections of soil properties and those environmental factors that influence the formation of soil. Therefore, soil spectra are related with the variation of SOCD, and could be helpful in the prediction of SOCD. Besides, the optimal spectral pretreatment for PLSR modelling of SOCD is the combination of smoothing, first-order derivation and SNV. In summary, soil reflectance spectra in the visible and near-infrared region (350-2 500 nm) could serve as an effective proxy variable for SOCD estimation. Given that soil VNIR reflectance spectra are easy and quick to measure, and the measurement is also environmentally friendly, we would like to argue that soil spectral reflectance could serve as an ideal auxiliary variable for the spatial interpolation of SOCD. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) is an essential soil property for assessing the fertility of paddy soils. It can be measured with visible and near infrared spectroscopy effectively in the field. Meanwhile, there are a lot of factors, such as soil water, surface conditions and so on, which might affect the spectra, increasing the difficulty in extracting the effective information, and reducing the prediction accuracy of SOC content. Noise reduction must be considered in developing hyperspectral estimation models, but how to reduce noise while retaining as much useful information as possible needs for investigation. As advanced spectral mining methods, local correlation maximization (LCM) arithmetic was used to solve this problem in this study. In the present study, a total of 70 soil samples of paddy soil were collected from rice fields in Zhulin town, Jintian city, Jiangsu Province. The sample holders were clear aluminum boxes in 7 cm diameter and 3 cm deep, which were filled and leveled at the rim with a spatula. Reflectance of soil samples measured using ASD Fieldspec 3 Spectrometer in a dark room when brought these samples indoor immediately to keep them in the field conditions. We used the following steps to process soil reflectance: First, discrete wavelet packet transformation (DWPT) was used to decompose the original spectral (result from 0.6-order differential) in 7 levels using Bior1.3 wavelet basis by MATLAB programming language. In order to select the maximum level of DWPT, correlation coefficients between SOC and the spectra of each level was computed. Secondly, LCM method was used to develop the local optimal correlation coefficient (LOCC) and optimal band which was determined from the optimal correlative curve and the optimal spectra (OS), respectively. Thirdly, a PLSR model was built to predict SOC contents. And then, determination coefficient of validation (R2v), root mean square error of validation (RMSEV), and residual prediction deviation (RPD) were used for accuracy assessment. We also used variable in the projection (VIP) analysis to identify the reason why LCM could improve the accuracy of predict model at the same time. The results showed: 1) significant correlated bands followed increasing-decreasing trend with the increase of wavelet decomposed level and the maximum level identified as level 6. This implied that the wavelet packet transformation amplified some useful SOC information that was previously obscured by noise. 2) optimal spectra that established from LCM could effectively remove noise while preserving the detail information of SOC simultaneously. 3) compared with raw spectral (R2v=0.693, RMSEV=1.952 g/kg, RPD=1.85), the wavelet packet transformation provided good results (R2v=0.727, RMSEV=1.840 g/kg, RPD=1.97) of SOC prediction, combined with LCM arithmetic, the model had the best performance (R2v=0.781, RMSEV=1.679 g/kg, RPD=2.17) to predict SOC content. According to VIP score, important bands for SOC prediction hadthree pink values, two of them located in the characteristic bands of soil water, this illustrated LCM can't remove the effects of soil water thorough. Results indicated that the discrete wavelet packet transformation and local correlation maximization (DWPT-LCM) method had great potential to monitor SOC contents in paddy soils when reduced white noise while retaining as much soil organic carbon information as possible. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) is a crucial soil property which has attracted wide attention in the field of global change. This is especially true in the arid and semi-arid regions. In recent years, it is a hot topic to estimate SOC content by hyperspectral remote sensing technology, however, it is hard to estimate SOC content in desert area precisely when it is less than 2%. Existing work, including related research history and current status, has mostly focused on integer differential, which yet might influence the effective information detection and cause the loss of spectral information to some extent. Therefore, this study aimed to bring fractional order differential algorithm into the preprocessing of hyperspectral data. With 103 surface soil samples collected from the Ebinur Lake basin in Xinjiang Uighur Autonomous Region, China, the SOC contents and reflectance spectra were measured in the laboratory. After removing the marginal bands (350-400 and 2401-2500 nm) and smoothed by Savitzky-Golay filter, the raw hyperspectral reflectance (R) data were transformed by 4 mathematical methods, i.e., the reciprocal, logarithm, logarithm-reciprocal and root mean square method, respectively. Secondly, their 0-2 order differentials (taking 0.2-order as step) were calculated by Grünwald-Letnikov fractional differential equation. And then, we computed the correlation coefficients between each fractional order differential value of R, its 4 mathematical transformation forms and SOC content. After choosing the feature bands whose correlation coefficient passed the significance test at 0.01 level, 103 samples were divided into 2 parts: 69 for model calibration and 34 for validation. Subsequently, partial least squares regression (PLSR) was employed to build the hyperspectral estimation models of SOC content. And then, root mean square error of calibration (RMSEC), determination coefficient of calibration (R2c ), root mean square error of prediction (RMSEP), determination coefficient of predicting (R2 p) and relative prediction deviation (RPD) were used for accuracy assessment. The results showed that: 1) Fractional order differential algorithm could refine the correlation coefficient curves between the SOC content and the raw hyperspectral reflectance in the wavelength ranges of 450-600, 640-700 and 1400-1500 nm, and also reduce the information loss to some extent; 2) With the order increasing, the number of bands whose correlation coefficient passed the significance test at 0.01 level firstly increased and then decreased, but the number of bands did not reach the maximum at the same order, and some differences occurred; 3) Comparing the predictive effects of 55 SOC estimating models calibrated by PLSR, the model based on 1.6-order differential of logarithm transform was much better than others, and had better performance in predicting SOC content in the study area (RMSEC=2.433 g/kg, R2c =0.786, RMSEP=2.263 g/kg, R2 p=0.825, RPD=2.392). And indeed, the models based on fractional order differentials were more robust and accurate than the conventional integer differential ones. Over all, it is a fairly satisfactory preprocessing method of hyperspectral data in the quantitative study on soil nutrients by means of remote sensing. In order to achieve more universal and stable inversion model, the next step is to enlarge the sampling area and the number of soil samples as much as possible to improve and perfect the soil hyperspectral database. [ABSTRACT FROM AUTHOR]

Soil hydrothermal coupling process widely exists in engineering and agriculture field. As 2 important physical features of soil, soil water content and thermal conductivity are meaningful for soil organic matter accumulation, chemical reaction rate and coupled transfer of water and heat. Many relevant tests were completed in laboratories, and test conditions were mostly artificial conditions instead of open air conditions, under which research area was different from one another, which could make the stability influenced easily by other factors. This experiment was conducted under a stable greenhouse condition and research area was in small scale, which helped to make sure the similarity of different sampling areas. To explore the distribution characteristics and relevance between soil water content and thermal conductivity, an 8 m × 8 m tomato field in the experimental greenhouse was selected. The division was conducted with 1 m × 1 m grid and 64 sampling points were set in total. The sampling method was to use soil drill to obtain samples from 0-40 cm depth soil layer. Drying method was used for measuring soil water content. By means of the portable soil thermal conductivity instrument (MTN01), which was based on the non-steady-state probe (NSSP) technology, thermal conductivity of the 0-20 cm depth soil layer was measured. On the basis of classical statistics, geostatistics, regression analysis and spectrum analysis theories, the distribution characteristics and relevance between soil water content and thermal conductivity were studied. Contour map were used to analyze the spatial distribution characteristics of soil water content and thermal conductivity. Also spectrum analysis of soil thermal conductivity on the sequence of soil water content was carried out. According to the principle of spatial spectrum analysis, soil water content was used as distance sequence with thermal conductivity as variable sequence, 32 intervals were set up with step length of 0.35%, and then the Fourier transform was conducted. The result showed that soil water content at the 0-40 cm depth had a trend of increasing firstly and then decreasing, and the peak of the average value was observed in depth of 20-30 cm. For the thermal conductivity, the 10-20 cm depth layer was 15.60% higher than the 0-10 cm layer. There was a strong spatial relationship between soil water content and thermal conductivity in all depths of soil layer. In addition, in the experiment, the spatial correlation range caused by random factors was relatively low. The smallest range (1.31 m) was larger than the sampling distance (1 m), which meant the grid arranged can meet the requirement of spatial analysis. On the condition of uniform water supply, the evaporation intensity of soil layers of different depths in the experimental field and the soil water content of adjacent field would influence the spatial distribution of soil water content. When the soil water content varied between 17% and 28%, for 0-20 cm soil layer, the spatial distribution of soil water content and thermal conductivity were highly correlated, and the result of regression and fit showed that it was a linear positive correlation (R2=0.837). The result of spectrum analysis showed thermal conductivity had a long-term negative correlation in the sequence of soil water content. [ABSTRACT FROM AUTHOR]

At present, a large tract of farmland or soil may be contaminated by heavy metal, which could make agricultural products polluted, bringing a harmful effect on human health. Hence, it is necessary to determine the heavy metal concentration in farmland soil. Rapid detection of heavy metals in soil is a key point of information acquirement tools in precision agriculture, and it also provides a base on prevention soil heavy metal pollution and sustainable development of agriculture. The traditional techniques for determining heavy metals is high in cost, time-consuming, use of large amount of chemicals, complex in sample preparation, and expensive in experimental device, etc. Laser-induced breakdown spectroscopy (LIBS), a form of atomic emission spectroscopy (AES), is regarded as a useful tool for rapid, real-time and in situ measurements because of its unique characteristics, such as no or less sample preparation, multielement analysis, direct measurement without contact with the materials, stand-off or remote analysis and high detecting speed. Relying on those unique capabilities, LIBS technique has a tremendous growth and been widely applied in variety of areas, such as environmental monitoring, geological applications, biomedical detection, industrial analysis, agriculture and food. In order to evaluate the feasibility of determining the heavy metals in soil, in this research, we employed LIBS technology combined with calibration curve and chemometrics methods to quantitatively analyze the content of Pb and Cd in soil samples. Soil samples with different Pb and Cd concentration were prepared. A laboratorial LIBS device working in air was employed to obtain the spectra of soil samples. Based on analysis of LIBS curves and the data from atomic spectra database in national institute of standards and technology (NIST), emission lines at Pb I 405.78 and Cd I 361.05 nm (I was atomic spectral line) were identified as characteristic lines. Then, models and calibration curves based on LIBS intensity, Lorentz fit intensity after normalized, peak areas and corresponding content were established and fitted. For Pb element, the linear relationships of coefficient of determination (R2) of three methods were: 0.98385, 0.97097 and 0.99321, respectively. And Cd element failed to provide an effective linear relationship. Meanwhile, PLSR (Partial Linear Square Regression) model for predicting Pb and Cd were built and results demonstrated that the calibration curve and PLSR model provided similar performance, resulting in correlation coefficient of 0.9485 and root mean square error of 2.044 mg/g in the prediction set. And PLSR model for Cd element prediction revealed the promising results, resulting in correlation coefficient of 0.9949 and root mean square error of 97.05 μg/g in the prediction set. The achievements of the research not only provides a guide for detecting heavy metals in soil, but also lays a theoretical foundation for development of the portable LIBS detector used in the field. [ABSTRACT FROM AUTHOR]

Soil is related closely to human living and vegetation grow.The quality of soil organic matter (SOM) influences plant development.Scientists take a variety of researcheson soil.Many findings focus on the estimation of SOM using remote sensing data, which are usually hyperspectral and multispectral.The former has a detailed result of band information, while the latter provides a macroscopical and convenient way to get in whole area.In addition, processing hyperspectral data needs a strong mathematical background and software technology, while processing multispectraldata needs less. To apply the multispectral method to make decisions on buildings and planning is of great significance.In order to save resources, increase efficiency and accuracy, in May 2014, we collected soil samples in the various layers of 0~10, 10~20 and 20~30 cm, and there were totally 45 points marked by GPS(global positioning system) on Google Earth.The weighed aluminum box was used to hold some soil in each layer.The collections were taken back and dried for 24 h.Then the dried soil was weighed and the soil moisture was calculated.Meanwhile, the image needed pretreatment.The atmospheric correction should be taken to remove bands′ noises to get clear data.Then the pixels of the image for each sample point were used to establish models.And at the same time, other soil was crushed and sieved in 2 mm, and the SOM was measured by the potassium dichromate volumetric method.The final work was to combine the reflectance data of multispectral image and the measured SOM data.We used the reflectance(R), the reflectance reciprocal(1/R), the reflectance reciprocal′s logarithm (ln(1/R)), the reflectance′s first derivative(FDR) and the measured SOM to build multiple linear regression models, and then, it was found that the FDR model had a better precision with the R2 of 0.963 between the predicted and the measured.This meant that the more effective approach could be applied to express the regional SOM if needed.By the FDR model, we predicted the SOM content in study area.It showed that the area with SOM content of 0~5 g/kg was 84.065% of the whole area and that with SOM content of greater than 10 g/kg was 0.001 5%.The greatest SOM value was 13.065 g/kg,and the inferior was closed to 0.355 g/kg, which was very low.The SOM content in the Wu caiwan area was lower than that in Qitai County, for the former′s SOM was less than 1% in the most area.It also indicated that the highest average SOM content in the mining area was 7.35 g/kg, which was influenced by the organic matter in coal. The bare land's area was 2 674.44 km2, accounting for 63% of all area, and the mean SOM content was 6.12 g/kg.The saline land and desert woodland had lower SOM content because of the development of water soil loss, salinization and desertification.The low SOM content and less precipitation made the area a desert increasingly.Further more, we found that in the arid area, the soil moisture content was extremely low, so it was not only influenced weakly by moisture to using remote sensing means to estimate SOM, but also formed an advantageous method which provided a higher simulation precision.All in all, it is imperative to restore the ecologic environment in the study area.Measures should be taken immediately.Choosing appropriate vegetation to plant in desert will be the key to the restoring works, while enhancing supervision of the Kalamaili Nature Reserve and controlling grazing will contribute to slow down those negative phenomena above. [ABSTRACT FROM AUTHOR]

Clay content is an important soil property that affects the structure, nutrient supply and other characteristics of soils. Variations in clay content can indicate the degree of soil development or soil age. In traditional chemical analyses of soil properties, the extractant interacts in the solution and at the solution-particle interface, thus altering the equilibrium between the soil solid and solution phases. Soil reflectance spectroscopy has been developed as an effective alternative method of measuring soil properties primarily because it requires minimal sample preparation and it is fast, cost-effective, non-destructive and non-hazardous to the soil. In recent decades, research on the use of reflectance spectroscopy in soil science has achieved rapid advances. Reflectance spectroscopy can be successfully applied to estimate the soil clay content. However, the mechanisms of soil clay content estimation using reflectance spectroscopy are not very clear. The goals of this study were to identify the bands within the range of 360-2490 nm that can be used to estimate the clay content and explore the mechanisms of the clay content estimation using reflectance spectroscopy. A total of 150 coastal soil samples were collected. The soil reflectance spectra were measured in a dark room using a FieldSpec 3 portable spectrometer. Raw spectral data were pre-processed by smoothing (R) and then by first derivative (FD), continuum removal (CR) or reciprocal transformation (DS). Calibration (75 soil samples) and validation datasets (75 soil samples) were obtained from 1,000 random selections of the data. Stepwise multiple linear regression (SMLR) and partial least squares regression (PLSR) were performed to estimate the soil clay content and to further identify the bands useful for modeling this parameter. The results indicated that the SMLR analysis of CR and R spectra and the PLSR analysis of R and FD spectra were characterized by good calibration and validation accuracies regarding the soil clay content. The frequency of a SMLR-selected band and the regression coefficient in the PLSR regression equation indicated the impact of the clay content on the reflectance spectroscopy. The bands of 360-900 nm and 1800-2490 nm were important for the clay content estimation. The absorption bands near 1900 nm were caused by crystal water in phyllosilicates, whereas the absorption at 2325 nm was attributed to the combined effects of absorption by chlorite and vermiculite. The 500-800 nm absorption bands were caused by the soil organic matter (SOM). And the relationship between the clay content and reflectance spectra was a secondary relationship attributed to the high correlation between SOM and clay content. The absorption near 410 nm was produced by the iron ions. The mutual adsorption of iron ions and clay was consistent with the relatively high model contribution. The regression coefficients in the PLSR yielded dual peaks of absorption near 1400 nm, which was attributed to the dual absorption peaks of kaolinite. In summary, the clay mineral composition, adsorption of clay and iron ions and correlations between clay and organic matter were the causes of the clay content estimation using reflectance spectroscopy. [ABSTRACT FROM AUTHOR]

In arid and semi -arid inland areas, due to high evapotranspiration , the minerals in the soil water accumulation in the soil surface, soil salinization becomes a serious threat to the local agricultural production, ecological stability and economic development. At present, most research on remote sensing technology to monitor the saline soil is focused on quantifying the relationship between the saline soil salt content and its associated, environmental factors or human factors and visible-near infrared, thermal infrared, or microwave remote sensing data. In this paper, we took the Ebinur Lake in the northeast of Junggar Basin Xinjiang as the study area. Thermal infrared emissivity spectra unique characteristics were used to determine the degree of soil salinization. First, we used the platform of FTIR (Fourier Transform Infrared Spectrometer) temperature and emissivity separation processing software to separate temperature and emissivity in order to obtain the original soil emissivity spectral data. Then, the spectrum smoothing iterative method was used to separate the soil emissivity and thermometers for elimination of environmental and human-caused errors when collecting spectral emissivity data in order to get the real soil emissivity spectral information. After that, we used Gaussian filter smoothing method to filter noise spectral data. In addition, we mixed the pure soil with salt to achieve five different soil salt contents: 0.1, 2.3 ,9.7 , 26.22, 49.8 g/kg soil and a pure soil with no salt addition, and analyzed the thermal infrared emissivity spectral characteristics of them. The raw spectral data from them were de-noised by square root transformation, logarithmic transformation, the first derivative, and the second derivative. The four transformations were compared in their normalized ratio, and we determined the relationship between spectral data and soil salinity. We also used stepwise multiple regression equation to establish six different forms of forecasting models. By comparing the models of analysis, the establishment of the square root transformation had the highest prediction accuracy and R2 was greater than 0.82. By use of stepwise multiple regression model for each data set, the modeling results very stable, and test sample coefficient of determination R2=0.82, the root mean square error(RMS) is 0.92. Prediction model had performed very well, between the Ebinur basin of thermal emissivity spectra of soil salinizaiton in square root transformation and salt content has exist a function form. In this study, we discussed the hyperspectral remote sensing monitoring technology that can be used to predict the soil salt in Ebinur Lake basin, it provided the technical methods for the large-scale, low-cost and real-time monitoring the soil salinity. Such variation of emissivity method would promote the development and application of hyperspectral remote sensing technology monitoring on the space-time dynamic of arid land saline soil for future regional ecological restoration. [ABSTRACT FROM AUTHOR]