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Purple soil is widely distributed in southwestern China and is one of the important natural resources. Because of the loose parent rock and low degree of differentiation, purple soil is characterized by a loose structure that is easily eroded and high soil fertility. Soil erosion is very serious in the purple soil region, which is one of the main sources of sediment in the upper reaches of the Yangtze River, and has a certain impact on the agricultural production and engineering development in the purple soil region. After erosion, purple soil shows the characteristics of reduced dry density of soil and serious loss of fine particles, which affects its water-holding characteristics. In order to explore the changes of soil water-holding properties of purple soil after rainfall erosion, this study was carried out to analyze the effect of missing grain groups on the water-holding properties of sandy clayey purple soil under rainfall conditions by selecting three test methods, namely, axial translation technique, filter paper method, and saturated salt saturated saline solution vapor equilibrium technique for the three grain groups which are the most susceptible to changes in the rainfall conditions, and carrying out the Soil-Water Characteristic Curve (SWCC) test with the absence of grain groups within the full suction range to analyze the effect of the missing grain groups on the water-holding properties of sandy clayey purple soil. The results show that: 1) the missing grain group reduces the water-holding characteristics of sandy clayey purple soil, which is most obvious in the boundary effect zone and transition zone, and has less effect on the residual zone. The mass of the missing grain group is inversely proportional to the air intake value and the residual water content. 2) Using the grain size parameters (Constrained diameter、Average diameter、Median diameter、Effective diameter) to characterize the soil grading condition, the relationship between the soil grading curves and the characteristic values of SWCC was established. It was found that except for effective diameter, the other parameters were linearly inversely related to the air intake value and residual water content; 3) The data obtained from different methods were fitted using the Fredlund-Xing model, and it was found that the results of the data obtained from a single test method fitted better, but the SWCC significantly deviated from the test data points in the boundary effect zone and the transition zone. Root mean square error (RMSE)、 percent bias (PB) and mean absolute percent relative error (MAPRE) were introduced as evaluation indexes, and it was found that the suction range was the main factor affecting the fitting effect of SWCC, and within a certain suction range, the larger the range of data points involved, the better the model fitting effect was, and after a certain suction value was reached, the suction range had very little effect on the model fitting effect. It is proposed that to obtain the accurate SWCC of purple soil quickly, only the maximum suction value needs to be determined, and the number of test points with high suction is reduced, which can effectively shorten the SWCC test period. The results lay a foundation for the subsequent study of the role of soil erosion and water-holding characteristics of purple soil, and also provide reliable ideas for the prevention of soil erosion and the application of soil-water characteristic curves in the Three Gorges Reservoir area. [ABSTRACT FROM AUTHOR]

In order to improve the efficiency and accuracy of the quantitative prediction of soil nutrient content in black soil of Heilongjiang province, in this paper, we utilized statistical theory and spectral analysis method, researched the relationship of three kinds of soil nutrient content and soil spectrum to established hyperspectral inversion model of soil total nitrogen, available phosphorus, available kalium content. We acquired the aerial hyperspectral data by using CASI-1500 and SASI-600 linear array push-broom imaging spectrometers. Preprocessing of calibration and atmospheric radiation correction of Airborne Hyperspectral raw radiation data was studied. 96 samples were evenly sampled. In order to increase the representativeness of samples, 96 groups of samples were collected from 3-5 samples collected from 15 meters around the sampling point, and 1.5 kg was retained after mixing. After air-drying, mixing and grindingetc, it is used for the contents of total nitrogen, available phosphorus and available kalium were obtained through laboratory tests. The content of total nitrogen, available phosphorus and available kalium was determined by NaOH diffusion method, NaHCO3 extraction-molybdenum blue colorimetry and NH4OAC extraction-flame photometry. Referring to Kennard-Stone method, 72 groups of representative samples were selected as model samples for nutrient content prediction, and 24 groups were model prediction samples. 96 black soil samples were sorted according to nutrient content, and the spectral transformation in the visible near red range was analyzed. The change rule of total nitrogen is that the reflectance decreases with the increase of content. The first order differential spectra at 580 nm were significantly correlated with total nitrogen and available phosphorus content, with a correlation coefficient of -0.43 and -0.36, respectively. The first-order differential spectra at 1730-2200 nm were significantly correlated with K2O, and the maximum correlation coefficient was -0.31. Compared with the original spectral waveform, the correlation coefficient between the first derivative and three nutrient contents fluctuated sharply, and the positive and negative cross-sections were relatively sharp, with more peak coefficients. After spectral contrast analysis and correlation coefficient calculation, 86 bands with higher correlation coefficient were selected for the study under the first order differential variation. On black soil airborne hyperspectral data processing, the application of partial least squares regression (PLSR) and BP neural network method respectively establish soil nutrient content of high spectral quantitative inversion model. The results showed that RPIQ values (Difference between the third and the first quartile of sample observations ratio to RMSE) of total nitrogen PLSR and BP neural network prediction model were 2.42 and 2.80, respectively. The RPIQ values of effective phosphorus PLSR and BP neural network model were 0.83 and 1.67 respectively. The RPIQ values of the available kalium PLSR and BP neural network models were 2.00 and 2.33 respectively. Experiments showed that the spectral quantitative prediction model of soil total nitrogen and available kalium has good accuracy and prediction ability. Nitrogen, phosphorus and potassium, and the spatial distribution of nutrient content in black soil were obtained. However, the prediction effect of effective Phosphorus was not particularly ideal, which could only meet the requirements of approximate quantitative prediction. At the same time, the BP neural network modeling has better accuracy and prediction ability than the partial least square modeling, and the prediction accuracy increased by 6.5%, 10.1% and 6.6% respectively. Due to the limitation of soil samples and other conditions, more samples are needed to verify the universality of the model. More data mining methods are expected to establish more robust prediction models, which will provide more reliable information for the prediction and evaluation of black soil quality information. [ABSTRACT FROM AUTHOR]

The spatial variability of soil organic carbon (SOC) is one of the reasons leading to uncertainty in the estimation of carbon stocks. Simulation and analysis research of SOC spatial distribution, especially the three-dimensional (3D) spatial distribution characteristics, is of great significance for revealing the soil nutrient and pollutant migration, the precise management of farmland and agricultural sustainable development. The 3D spatial distribution pattern of SOC was estimated using 3D ordinary Kriging with regard to the isotropy (3DOKI), 3D ordinary Kriging and 3D CoKriging coupling Markov with regard to anisotropy (3DOKA and 3DCKMA) in the region. Based on estimation results, the paper revealed 3D distribution pattern of regional SOC content. The mean root mean square error (RMSE), relative improvement (RI), the scatter diagram of SOC simulated and measured value and specific value coverage ratio were used to estimate the accuracy of different approaches, and the mean squared deviation ratio (MSDR) and the 3D variogram were used to evaluate model fitting effect and spatial local uncertainty. The results showed that 3D spatial distribution patterns of SOC content for the 3 kinds of estimation methods were basically the same, and the overall spatial distribution pattern was SOC content of the west and the north was higher, and that of the east and the south was relatively low. SOC contents of 5 layers (0-20, >20-40, >40-60, >60-80 and >80-100 cm) were 11.88±5.76, 10.08±4.89, 8.40±5.49, 7.83±5.89 and 7.17±5.22 g/kg, respectively. With the increase of soil depth, the SOC content of the patch gradually reduced. No matter which kind of method, the spatial distribution of SOC in different soil depth was similar, that was, the distribution characteristics of surface layer of the patch were also the embodiment of the deep soil layer. Compared to 3DOKI, the spatial search strategy and its parameters with regard to anisotropy were able to reduce defect (Bovine and central tendency effect) of Kriging method in a certain extent. The RMSE values for 3DOKI, 3DOKA and 3DCKMA were 3.1645, 2.0523 and 1.6215, respectively, so the RMSE value was the minimum for 3DCKAM, whose RI reached nearly 50%. Specific value coverage ratio for 3DOKI, 3DOKA and 3DCKMA were 33.12%, 57.83% and 76.15%, respectively, which was the largest for 3DCKMA method, whose MSDR value (1.4409) was the most close to 1, and variance was the least. The same position CoKriging method coupling Markov was more accurate, and its model fitting effect was the best, and uncertainty was the smallest, which could better highlight the volatility and reflect the specific value. The related research results of this paper will provide method reference for the regional soil properties, and provide technical guidance for the reasonable and scientific research of the spatial distribution pattern of regional SOC. [ABSTRACT FROM AUTHOR]

In order to solve the problems of high resistance and high power consumption of rotary tillage and land preparation, the rotary blade is studied based on theoretical analysis, simulation and field experiment in this paper. First, a wedge drag reduction rotary blade is designed based on the theoretical force model of cutting tools in mechanical soil dynamics. Second , the three-dimensional model of the national rotary blade and the wedge drag reduction rotary blade is established by using the Inventor software and the simulation experiment is carried on. The cutting soil model of 2 kinds of rotary blades is established by using HyperMesh software, and the material parameters, boundary constraints and working parameters are defined. On this basis, the soil cutting process, stress and cutting resistance are analyzed. It is proved that the wedge drag reduction rotary blade design is reasonable. In order to verify whether the wedge drag reduction rotary blade meets the strength requirement, the stress of the rotary blades is analyzed, and the cutting resistance of the national rotary blade and the wedge drag reduction rotary blade are compared. Third, field comparative experiments are carried out on national rotary blade and wedge drag reducing rotating blade with the evaluation indexes of torque, power consumption and soil breakage. The results show that the cutting resistance of the wedge drag reduction rotary blade is smaller than that of the national rotary blade; the maximum stress of the national rotary blade is 35.68 MPa and the maximum stress of the wedge drag reduction rotary blade is 29.49 MPa, which is less than that of the national rotary blade and far less than the yield strength of the material 430 MPa. On the premise of ensuring the strength of the blade body, the weight of the wedge drag reduction rotary blade is reduced by about 50 g, which is about 8.3% lower than that of the national rotary blade. The maximum cutting resistance of the national rotary blade is 530 N, the average cutting resistance is 375.5 N, and the maximum cutting resistance of the wedge drag reduction rotary blade is 510 N, the average cutting resistance is 335.5 N, the average cutting resistance of the wedge drag reduction rotary blade is 10.65% lower than that of the national rotary blade, which proves the feasibility of the wedge drag reduction rotary blade designed; The average torque of the wedge drag reduction rotary blade is 11.35% lower than that of the national rotary blade. In the working process of rotary blades, the average power consumption of the national rotary blade is 74.2 kW, the average power consumption of the wedge drag reduction rotary blade is 67.3 kW, and the average power consumption of the wedge drag reduction rotary blade is 9.29% lower than that of the national rotary blade. The average breaking rate of soil of the national rotary blade is 75%, and the average breaking rate of wedge drag reduction rotary blade is 79%, which is 4% higher than that of the national rotary blade; After continuous 33 hm2 tillage, rotary blades did not break, and the average wear of the national rotary blade is 98.5 g, the average wear of the wedge shaped drag reduction rotary blade is 97.3 g, the wear difference is 1.3%, and the wear degree is the same. The wedge drag reduction rotary blade can effectively solve the problems of high resistance and high power consumption of rotary blade, which is helpful to accelerate the development of arable land machinery, improve the mechanization level of cultivated land, and reduce the production cost of crops. [ABSTRACT FROM AUTHOR]

Sloping farmland is an important part of cultivated land resources in Yunnan district. The evaluation of cultivated-layer soil quality and the appropriate range of parameters for sloping farmland are the basis for the development of reasonable plough layer. Based on the Soil Management Assessment Framework (SMAF), taking the cultivated-layer soil quality of sloping farmland in Yunnan province as the research object, this paper constructed a soil quality index evaluation model (CLSQI) for sloping farmland, and used the minimum data set (MDS) method to screen the evaluation indicators to evaluate the soil quality of sloping farmland in Yunnan, proposed the range of soil parameters for rational tillage in sloping farmland. The results showed that the minimum data set (MDS) of soil quality evaluation of sloping farmland in Yunnan region consisted of five indicators: pH value, total nitrogen, total porosity, soil shear strength and slope. The comparative analysis of AL-CLSQI (the cultivated-layer soil quality index calculated by all indicators) and AL-CLSQI (the cultivated-layer soil quality index calculated by the minimum data set index) showed that the evaluation accuracy of cultivated-layer soil quality based on MDS index was higher. In the primary selection index system, the weights of soil bulk density, total porosity, capillary porosity, shear strength, and slope grade were larger, while in the MDS index system, the weight of the slope and total nitrogen content was larger. The cultivated-layer soil quality index (CLSQI) of sloping cultivated land was between 0.39 and 0.84, the mean value was 0.59±0.11, and the coefficient of variation was 0.19. The cultivated-layer soil quality of sloping farmland was generally “medium”, and it was generally low. The CLSQI of the sloping farmland in different sampling areas was significantly different (P<0.05), and the size relationship was: Chuxiong＞Shuangbai＞Xuanwei＞Ninger＞Malong＞ Shilin. The 0～20 cm sampling depth sloping farmland CLSQI (mean value 0.59) was greater than >20 cm (mean value 0.56), and the CLSQI (mean value 0.61) of purple soil sloping farmland was larger than the red soil (mean value 0.54) (P<0.05), while the ridge and contour farming cultivation mode sloping farmland CLSQI was larger than that of the straight-plowing. The order of the CLSQI of the sloping farmland in different agricultural divisions was: Northeastern Yunnan＞Central Yunnan ＞Southwest Yunnan. According to the corresponding relationship between the soil quality of the cultivated-layer and the crop productivity and membership function, the cultivated-layer was in a relatively reasonable state when the soil quality grade of the sloping farmland was at a “higher” and above level (CLSQI≥0.6). The appropriate range of MDS indicators for cultivated-layer of sloping land in Yunnan were indicated as follows: the pH value of 5.58～7.82, the total nitrogen content of ≥1.37 g/kg, the total porosity content of ≥52.28%, the soil shear strength value of ≥5.19 kPa, and slope value of ≤11.79°. The suitable range of the above-mentioned cultivated-layer soil parameters can be used as the standard for the rational cultivated-layer diagnosis of sloping farmland in Yunnan. The research can provide theoretical basis and parameter support for the quality evaluation of ploughed plough layer and the construction of reasonable plough layer. [ABSTRACT FROM AUTHOR]

Soil salinization is a major desertification and land degradation that threatens especially the stability of ecosystems in arid land. Either natural factors or human unreasonable use of the soil can cause soil salinization, and also has impact on the sustainable development of resources and the environment. There is an urgent need for intensive monitoring and quick assessment of salinization through hyperspectral remote sensing as a tool for combating soil salinizaiton in such ecosystems. In this paper, estimation the soil salinization of Ebniur lake basin, Xinjiang, China by a multiple regression model was carried out using Analytical Spectral Devices (ASD) data and Advanced Space borne Thermal Emission and Reflection Radiometer (ASTER) image data and soil reflectance spectra. A total of 11 spectral transformation forms of reflectance were used to relate with the measured soil salinity. Spectral reflectance experienced root mean square, logarithmic, and reciprocal transformation, then the first and second derivative of original and transformation forms were calculated. In addition, the first derivative of reciprocal of logarithmic was also calculated. A relationship between the sensitive bands of soil salinity was used to build models of soil salinity. The transformation and model establishment were conducted again after resampling ASD spectral reflectance data. Finally, the model after resampling was correlated with that before resampling to increase the model accuracy. A total of 50 sampling points were obtained and 30 of them were randomly selected for model establishment and the other 20 was used for validation. The results showed that the model with second derivative of reflectance was best with R2 of 0.59 and 0.75, and the root means square of error (RMSE) of 1.29 and 1.24 g/kg. ASD resampling improved the model accuracy with the second derivative of logarithmic transformation as the best model, which yielded the R2 of 0.80-0.82, and RMSE of 0.97-1.05 g/kg. Through the regression analysis, a linear model was established between ASD resampling spectral inversion model and ASTER spectral model with R2 of 0.88. Using the linear model and based on the ASTER spectral model and ASD resampling model, the ASTER spectral model was calibrated. Using measured soil salinity to validate the calibrated model, the resulted showed that the calibrated model improved the model accuracy to 0.91 and reduced the RMSE to 0.96 g/kg. Therefore, the multiple regression method could be used for model calibration for ASTER spectral model based on ASD resampling spectral inversion model, which has great potential for estimation of soil salinity in the arid land. This paper made contribution to the dynamic monitoring of soil salinization, realized the scale transformation from the measured field scale to spectral scale of multi-spectral remote sensing, and also can provide valuable information for future research. [ABSTRACT FROM AUTHOR]

The information of surface soil moisture is of great importance for the irrigation and production of agriculture. Researches have shown that surface reflectance spectra of soils are often jointly affected by soil moisture content and salt content, whichhas not yet been sufficiently addressed. In this study, we investigated the external parameter orthogonalization(EPO) method to eliminate the effect of soil salinity by preprocessing soil spectral reflectance and establishing EPO-PLS(partial least squares regression after EPO pre-processing) model to predict soil moisture content. Soil salt composition and texture were obtained by taking soil samples in Hetao Irrigation District, Inner Mongolia, China in July 2014. The components of soil salt were mixed to artificially create 11 levels(percentage by weight, g/(100 g)) of salt salinity in the soil samples: 0.1(natural soil salt content), 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 1.0, 2.0 and 5.0%. The moisture contents of total soil samples were designed as relative weight 38%. Filling 11 replicate dishes(12 cm in diameter) with each level of salinity soil, respectively. Each dish was filled with about 374 g wet soil with 3cm depth and a bulk density of 1.3. A controlled laboratory experiment was conducted by a way of continuously monitoring changes of soil moisture and salt content. Soil reflectance spectra were measured for each level of salt salinity samples in a darkroom using Analytical Spectral Device Field Spec 3 Hi-Res(ASD, USA) spectrometer covering wavelengths from 350 to 2 500 nm at an interval of 1 nm. Reflectance spectra and weight of each soil samples were measured every day until the weights remained unvaried(completely air-dried). Based on laboratory controlled experiments, this paper is mainly focused on the changes of slightly and moderately salt-affected soil reflectance spectra in the process of evaporation. We quantitatively analyzed the changes in soil reflectance of overall bands and the results suggested that a combination of salt and moisture in soil caused confusion of soil reflectance spectra. The reflectance spectra of soils are leveling off and almost converge to 0.2 after moisture content reaches 25% where the effect of soil moisture dominates in the reflectance spectra of soil. However, for salt-affected soil reflectance spectra, a clear increasing pattern is noted along with a decreasing of soil moisture, in which the severer the salinity is, the higher the reflectance value and the faster the rising speed of average reflectance would be. The effect of soil salt on soil reflectance spectra became predominant when more and more salt accumulated on the soil surface in a form of white crusts with high reflectance because of moisture evaporation. EPO is a method to reduce the space dimensionality in regard to external parameters which is referred to soil salt in this paper. PLS and EPO-PLS models were established to predict the moisture of salt-affected soil, respectively. The prediction results of PLS model show a significant deterioration and bias with an increase of soil salt content. It is clear that soil salt has a strong influence on the prediction of soil moisture content. Direct application of PLS models leads to an over-predicted results of moisture content of salt-affected soil, based on the spectra of non-saline soil samples. Through comparing PLS with EPO-PLS model, R2 and RPD rose from 0.722 and 1.976 to 0.898 and 3.145 for validation data, respectively. RMSE was reduced from 5.087 g/(100 g) to 3.237 g/(100 g). Results show the model quality of EPO-PLS for prediction of soil moisture increases significantly. EPO is verified to eliminate the effect of soil salt on spectra successfully. In this way more precise information of soil moisture can be predicted by establishing the partial least squares regression after EPO pre-processing and the approach should realize the soil moisture estimation well in saline area. [ABSTRACT FROM AUTHOR]

With the continuous development of the economy and society, the problem of air pollution in China is becoming more and more serious especially in Hebei Province. Currently, Hebei Province has been one of the most serious areas of air pollution in China. The wind-blown dust, emitted from the earth’s surface to the atmosphere, has become one of the main components of air pollutants. The wind-blown dust has significant impacts on atmospheric phenomena and consequently on air quality. The wind-blown dust not only causes atmospheric pollution, but also changes the radiation balance of the ground and the acidity or alkalinity of the aerosol. It not only affects human health but also causes changes in the global climate system and ecosystem. In order to evaluate the PM10 of atmospheric pollutants in Hebei Province caused by wind-blown dust, the article set up parameters system of wind-blown dust production by Community Land Model 4.5 (CLM4.5) using remote sensing data, the ground meteorological data, the Land Data Assimilation System data and the land surface characteristic parameters production data with 1 km spatial resolution. The factors affecting the wind-blown dust include soil texture, wind speed, soil moisture, surface roughness, and the proportion of bare soil determined by vegetation coverage, surface freezing ratio, snow cover ratio, lakes and wetland ratio. Wind speed, soil moisture, vegetation coverage, snow cover area and freezing ratio change greatly with the seasons, so they are the sensitive factors of the wind-blown dust. This study provides the first estimates of the fine-scale spatial and temporal distribution of dust emissions from Hebei Province. The result shows that: The wind-blown dust annual emission flux of PM10 is 1.02 t/hm2 in 2013. The highest monthly emission flux is 0.28 t/hm2 in March, accounting for 27.6% of the whole year, and the lowest is July, accounting for only 0.2%. The dust emission flux of spring is 0.55 t/hm2, the summer is 0.015 t/hm2, the autumn is 0.18 t/hm2, and the winter is 0.28 t/hm2. That shows spring is the highest season and summer is the lowest season for wind-blown dust emission flux. There are obvious regional differences about wind-blown dust emissions in Hebei Province, the coastal plain of Cangzhou and Bashang plateau are the highest areas for dust emission flux, annual emission flux of which is 5.365 and 3.542 t/hm2 respectively, and Taihang Mountain piedmont plain is the lowest area, annual emission flux of which is 0.20 t/hm2. The result above shows CLM4.5 wind-blown dust model can well simulate the characteristics of spatial and temporal change in Hebei Province. Comparing the initial data of CLM4.5 with our study data, it presents the spatial resolution of initial data is very low, and not suitable for the simulation of the small area, in contrast, the local parameters we used including vegetation coverage, wind speed, soil moisture, the proportion of bare soil, lakes and wetland ratio data with high spatial resolution (1 km) have improved the accuracy of the simulation result. This paper is not only meaningful for evaluating the PM10 from wind-blown dust in Hebei Province, but also the first application to estimate wind-blown dust in the fine scale by CLM4.5 model. [ABSTRACT FROM AUTHOR]

Soil subsoiling is currently the most widely used method for soil loosening, which can break the hard bottom of soil. At present, soil subsoiling operations mainly use shovel subsoiler, which has common problems like poor subsoiling effect, uneven soil disturbance and high energy consumption. In this context, a new subsoiling technology, gas explosion subsoiling, is studied in this paper, which injects high pressure gas into the soil to cause fissures, thereby breaking the bottom soil and achieving the purpose of subsoiling. In order to clarify the subsoiling effect of gas explosion on soil, an orthogonal test with the gas explosion parameters like pressure, depth and volume was designed. The soil block used in test was in standard size of 100 cm × 50 cm (diameter × height). The soil fissures and disturbance situation on the soil section were observed through the cross-sectional treatment method, and the influence of gas explosion parameters on soil fissure extension was analyzed. Then, the soil fissure trace equation and soil disturbance model were established based on the 9 orthogonal test results calculated by OriginPro software and Auto2Fit software. The effect of soil damage form on the impact of soil subsoiling was discussed based on Mohr-Coulomb soil strength theory and Rankine's earth pressure theory. The orthogonal experiment results showed that the greater the gas explosion pressure, the deeper the gas explosion depth; the more the gas volume, the better the soil subsoiling effect. The soil fissure trace equation was a quadratic function and the soil disturbance area was parabolic. This research provided a model and method for studying the soil fissure trace equation, and the soil hardness in the equation could be used as a variable for the soil type, so the soil fissure trace equation was applicable to most types of soils. The soil was mainly subjected to pulling damage when the gas explosion depth was less than 25 cm, the soil surface tended to rise at the gas splitting force of 0.17 MPa and the disturbance coefficient of 50%, and the center of soil fissure was the gas explosion center. The soil was mainly subjected to shear damage when the gas explosion depth was more than 25 cm, the soil surface elevation was not obvious at the gas splitting force of 0.39 MPa and the soil disturbance coefficient of more than 50%, and meanwhile the center of soil fissure moved downward, and the downward displacement increased with the increase of gas explosion pressure. What was more, the effect of shear damage on soil subsoiling was better than that of pulling damage. Therefore, soil shear damage should occur as much as possible in design of gas explosion technological parameters, which played a significant role in promoting the expansion of soil fissures and increasing the degree of soil loosening. When the gas explosion depth was 25 cm, the soil damage would be the joint action of pulling and shear, and the degrees of these 2 kinds of damages were equivalent, which was conducive to the horizontal expansion of soil fissures. In addition, the gas explosion pressure was not as high as possible, and higher pressure gas source had a higher technical requirement on gas storage and booster device, and also a longer time for gas collection, which was not conducive to long-term working operation. The data optimization results showed that 1 MPa gas explosion pressure and 30 cm gas explosion depth were more appropriate, and under the condition the soil subsoiling effect was remarkable with the soil disturbance coefficient of 79.5%. The results can provide theoretical basis for the optimization of gas explosion subsoiling parameters. [ABSTRACT FROM AUTHOR]

Soil salinization is the main problem of land degradation in the Yellow River Delta of China. Remote sensing technology can gain soil salinization information quickly and accurately, which is of great significance to the sustainable development of agriculture. In this paper, a typical salinization area in Kenli County of the Yellow River Delta was chosen as the study area. In order to retrieve soil salinity from hyperspectral imagery with high accuracy, image fusion and machine learning were used in this study. HSI (hyperspectral imaging radiometer) hyperspectral imagery of HJ-1A satellite of China and OLI (operational oand imager) multispectral imagery of Landsat 8 of USA (United States of America) were preprocessed, including radiometric calibration, atmospheric correction and image registration. After that, the 2 kinds of images were fused with the hyperspherical color space resolution merge algorithm. This algorithm was designed for 8‑band data of Worldview‑2 sensor, and it works with any multispectral data containing 3 bands or more. The fused image has 30 m spatial resolution and 4.32 nm spectral resolution, in which saline soil can be identified better than that in the original image. The feature bands were selected according to spectral analysis of different levels of saline soil and the PLSR (partial least squares regression) regression coefficients between soil salinity and imagery bands. Two types of models, i.e. statistical model and machine learning model, were built. The statistical model includes multi linear regression model and PLSR model, while the machine learning model includes BP (back propagation) neural network model, support vector machine (SVM) model and random forest (RF) model. These models were built with soil salinity data as retrieval target and feature bands of images as input variables. In this process, natural logarithm function was adopted for soil salinity data to obey the normal distribution. The research gained the following results. Firstly, the retrieval model based on fused images is overall better than HSI images, and the latter is better than OLI multispectral images, which shows that both spatial and spectral resolution have important effects on the retrieval results. The retrieval accuracy of fused image is obviously better than that of HSI and OLI images. The main reason is that the fused image not only has both high spatial and spectral resolution but also has fewer mixed pixels. Secondly, with regard to performance of the models, the machine learning models are superior to the classic statistical models. This is because classic statistics often require sufficient samples, while machine learning is designed for small sample data and has more advantages over retrieval problems. In general, BP neural network model is better than SVM model and RF model. For the retrieval of fused images, the correlation coefficients of the 3 models are all higher than 0.82, and thus all of them achieve desirable results. Thirdly, the results also indicate that the accuracy of the models can be improved to some extent by proper preprocessing of the data, such as natural logarithm function which can let the data obey normal distribution. Either the classic statistical analysis method or the new machine learning method is based on the training data to explore the relationship between the retrieval target and the input variables. We then conclude that: 1) Despite the differences both in time and in wavelength between OLI and HSI images, image fusion can significantly improve the accuracy of remote sensing retrieval of soil salinity; 2) Machine learning model is better than traditional statistical model for soil salinization retrieval; 3) The main factors that affect the retrieval accuracy in our study include the number of measured samples, the quality of remote sensing data, the data preprocessing, fusion and modeling methods, and so on. Therefore, this study provides useful results and has positive theoretical and practical significance to the soil salinity retrieval in the typical area of the Yellow River Delta with remote sensing method. [ABSTRACT FROM AUTHOR]

Reservoir leakage has a great influence on water-salt dynamic, which is easy to cause soil secondary salinization and swamping in arid area. Groundwater depth of farmland behind dam is reduced by anti-seepage body and drain measure usually. Drainage ditches are set up behind dam, which can cut off seepage water and discharge water, therefore reducing the leakage loss. But how to choose parameters of drain, how to be combined with impervious body, and so on, these questions need to be studied further. Based on unsaturated soil seepage theory, aimed to the farmland around Qiala Reservoir, this paper calculated the combination measures by numerical simulation, and analyzed the relationship between groundwater depth and depth and position of drainage ditch by vertical cutoff wall, horizontal impervious blanket or non anti-seepage measures. Besides, we analyzed the downstream slope stability and adopted the field experiment to validate it. This research showed that the buried depth of farmland groundwater is characterized by a “funnel-shaped” downward trend taking the central axis of drainage ditch as a symmetrical axis in different seepage control schemes. The groundwater depth increased gradually before the drainage ditch, and decreased a little after the drainage ditch. Therefore, the “anti-seepage body and drain measure” reduce seepage into the farmland from the source, which can make farmland maintain a low groundwater depth. Different seepage control schemes of “anti-seepage body and drain measure” were different on the curbing and the effect of reducing the leakage of reservoir to farmland. The hanging cutoff wall and the non-impervious body cannot reduce the leakage of the reservoir effectively, and we found the combination of the former 2 ways has a weaker effect on anti-seepage. By comparing the 3 schemes, it was considered that the horizontal impervious blanket with a length of 22 times water head was better than the suspended cutoff wall and non-impervious body in seepage control. After combining it with the drainage ditch and the drainage in time, we controlled the groundwater depth at about 2.72 m effectively, which was greater than the local critical groundwater depth of 2.45 m, and curbed the trend of soil salinization after dam effectively. Drainage ditch position and depth had some effect on dam; we calculated the actual construction (length of 22 times water head) and found the downstream dam slope anti slide safety coefficient was 1.358, which was larger than the minimum slope anti slide safety coefficient of 1.242, so the downstream slope of the dam was in a safe state. After the drainage ditches were set up, the length of hydrops from the dam toe to drainage ditch was the main reason to the soil swamping. By calculating and measurement in the actual condition, we found the length of hydrops was 0.27 m, and the swamping area was smaller. In addition, calculations also revealed that the minimum depth of drainage ditches corresponding to avoiding field swamping was 5.18 m. Drain depth was 6 m in practical project, and thus the present cutoff wall, the distance between drainage ditch and dam toe, and the drain depth were reasonable. The study also found that water level changing and making sure draining timely have great effects on groundwater depth; drainage ditch was an auxiliary measure, and it can be combined with drainage and impervious body and farmland irrigation system, which can have a more effective influence. [ABSTRACT FROM AUTHOR]

Soil organic matter (SOM) is not only an important indicator of soil fertility but also an important source and sink of the global carbon cycle. Therefore, it is essential to acquire the information of SOM for soil management. Visible and near-infrared (VIS-NIR) reflectance spectroscopy, known as a novel, rapid, accurate, environment-friendly and efficient approach compared with conventional laboratory analyses, has proven to be promising in the acquisition of various soil properties. Construction of a calibration set is key to the VIS-NIR quantitative analysis in building up a prediction model of high quality. The aim of this paper was to explore how the sample selection method and the number of samples may affect the accuracy of VIS-NIR models for SOM estimation. A total of 100 paddy soil samples (0-15 cm) were collected from the Honghu City, which is located in the Jianghan Plain, China. After air drying, grinding and sieving (0.25 mm), reflectance of these pretreated samples was measured with FieldSpec3 (Analytical Spectral Devices Inc., America). Three samples were neglected after outlier detections of spectra and SOM content. Out of the remaining 97 samples, 20 samples were selected by means of concentration gradient, which then formed the validation sample set. The remaining 77 samples formed the total calibration set. With SOM content or soil spectral information as inputs, 3 sample selection methods, namely Kennard-Stone (KS), sample set partitioning based on joint X-Y distance (SPXY) and Rank-KS, were used in the construction of calibration subsets with different proportions of the samples in total calibration set, such as 10% and 20%. Based on the different calibration subsets, partial least squares regression (PLSR) was used for model calibrations. Results showed that the calibration set selected by KS approach could not improve model predictive capability compared with the total calibration set. The KS approach, however, could reduce as many as 30% samples of the total calibration set while the ratio of performance to standard deviation (RPD) was retained above 2.0. The SPXY approach performed the best when 50% samples of the total calibration set were selected in the model calibration. The determination coefficient for calibration (Rc2) reached 0.922, the determination coefficient for prediction (Rp2) was 0.848, and the RPD reached 2.557. This was because the SPXY approach took into account both SOM content and soil spectra in the sample selection process. With only 30% samples of the total calibration set selected by the Rank-KS method, it had the lowest cost of calibration with satisfactory performance (Rc2=0.872, Rp2=0.802 and RPD=2.212). Overall, such results indicate that it is possible to reduce the number of calibration samples while retaining or even improving the predictive capacity of VIS-NIR models for SOM estimation. All the 3 calibration selection approaches have been proven to be useful for the improvement of model practicability. [ABSTRACT FROM AUTHOR]

Preferential flow refers to such a flow transport pattern that water and solute flow round soil matrix, which accelerates soil water movement. Soil cracks are a part of reasons that lead to preferential flow. This study aimed to analyze soil preferential flow pattern in order to establish a model of preferential model based on soil fracture network. A field color tracer experiment was carried out in Ningxia University from July to September in 2015 under precipitation intensity of 20 and 50 mm/h, and rainfall duration of 20-60 min. The results showed that when the rainfall intensity was 20 mm/h, the water infiltration depth increased with the increase of rainfall duration and the preferential flow began to develop, but it was still overall-matrix flow and the preferential flow was only locally developed. When precipitation intensity was 50mm/h and the duration of rainfall was 20min, water flows bypassed the soil matrix and preferential flow phenomena was observed obviously. The infiltration depth exceeded the fracture depth after duration of rainfall increased to 50min, soil fracture had not play a major role in the soil water movement, and the flow patterns were overall-matrix flow. The results showed that the flow patterns were mainly matrix flow under lower rainfall or irrigation intensity, and preferential flow under higher rainfall or irrigation intensity. A coupling model of soil matrix and fracture network for simulating preferential flow was established (CP model). In this model, the soil medium was divided into matrix and fracture networks formed by flow channels, each having its own saturated hydraulic conductivity and water movement equation. These equations were solved by using the control volume finite element method, and the nonlinear equations were linearized by Newton-Raphson methods to improve the convergence, and shorten the operation time. Equation of water movement in matrix and fracture network flow equation were coupled using global approach which solved the problems of the flow mutual influence caused by the cross-fracture network, and improved the computational efficiency. The validity of the proposed model was tested by the analysis of the soil moisture content between the simulated and the observed values. The results showed that the root mean squared error (RMSE) of soil moisture was 0.016-0.025, R2 of soil moisture was above 0.78, bias was 0.020-0.026, index of agreement (IA) was 0.918-0.938, indicating good simulation by the CP model. In the end, different crack structure but statistically equivalent networks was generated to validate the model simulation and produced well simulation results. These numerous fractures had stable effect on the variances of soil moisture. The results of this paper can provide valuable information for analyzing preferential flow and the coupling mechanism of the water content and solute transport between soil dry shrinkage crack and soil matrix. [ABSTRACT FROM AUTHOR]

Soil physical basic parameters are key factors for impacting the soil thermal conductivity, and they are also closely related to the model parameters used to calculate the soil thermal conductivity. In order to study the relationship between soil physical basic parameters, organic matter content and soil thermal conductivity model parameters, the precision of different soil thermal conductivity models was discussed by analyzing 10 types of soil samples in this paper. There were 9 types of soil textures which were sampled from different areas in Shaanxi Province, and the last one was sampled from Zhangye, Gansu Province, which was used to verify the feasibility of the new models. According to the sand content, these soil samples were divided into 2 types: fine-textured soil and coarse-textured soil. The soil thermal conductivity models were used to fit these 2 types of soils, and the comparison results indicated that the theoretical models such as Côté-Konrad model and Lu-Ren model were more precise than Campbell model and Johansen model. The fitted results of Johansen model were significantly smaller than the measured values, and the ranges of root mean square error (RMSE), coefficient of determination (R2) and relative error (Re) for this model were 0.0848-0.2548, 0.656-0.827 and 10.32%-20.41%, respectively. Moreover, Côté-Konrad model and Lu-Ren model had better fitting results for fine-textured soil, and the ranges of RMSE, R2 and Re were 0.0810-0.1208, 0.842-0.940 and 9.67%-10.57% for Côté-Konrad model and 0.0725-0.1238, 0.874-0.937 and 8.28%-9.91% for Lu-Ren model. However, these 2 models were not suitable for calculating the soil thermal conductivity of coarse-textured soil when the water saturation was larger than 50%. Thus, the improved models, which described the relationship between thermal conductivity and soil physical basic parameters, were developed based on Côté-Konrad model and Lu-Ren model. The results showed that the improved models could be used to fit the thermal conductivity in different soil textures, and the RMSE was less than 0.0964, the R2 was up to 0.92 and the Re was less than 9.6%. For predicting the soil thermal conductivity with higher sand content or higher silt content, the values of RMSE, R2 and Re for the improved Côté-Konrad model were 0.1183, 0.9259 and 9.47%, respectively, which was better than the Côté-Konrad model, Lu-Ren model, and improved Lu-Ren model through the analysis of simulation error. On the other hand, for predicting the soil thermal conductivity with lower sand and silt contents, the values of RMSE, R2 and Re for the improved Lu-Ren model were 0.0815, 0.9326 and 8.11%, respectively, which was better than the other 3 models. Moreover, the improved models were used to calculate the soil thermal conductivity of the other types of soil textures in Zhangye. Because the soil texture in Zhangye is sandy clay loam soil in which the sand content is higher than 60%, the improved Côté-Konrad model has the best effect when calculating the soil thermal conductivity according to the analysis results. The parameters in the improved models contain soil texture and organic matter content, which can be used to describe the relationship between thermal conductivity and soil physical basic parameters in detail. Furthermore, choosing an appropriate improved model based on soil texture can calculate the soil thermal conductivity more accurately. [ABSTRACT FROM AUTHOR]