Analysis on Reflectance Spectral Characteristics of Typical Saline Soil in Arid Area

The spectral characteristic of geography objects is not only the important content of remote sensing mechanism, but also the important basis for remote sensing application. The reflectance spectral characteristics reflect the physiochemi-cal properties of saline soil. With 3 kinds of typical saline soils in the arid area as the study objects, the reflectance spectrums of soils with different salt contents and soil moistures were measured, and the spectral characteristics of the spectrums were analyzed. The results showed that under dry condition, the reflectance of the three kinds of saline soils presented obvious high-low patterns, while under damp condition, there was no obvious pattern. With continuum removed ,the three kinds of saline soils showed significant difference in reflectance spectral characteristics. There was significant difference in the absorption depth of the two absorption val eys un-der dry and damp conditions, which could be used to identify these 3 saline soils. The result of this research can be used for the parametric inversion and classifica-tion of saline soil retrieval and classification, as wel as for the remote sensing monitoring on saline soil.

Hyper-spectral characteristics and classification of farmland soil in northeast of China

The physical and chemical heterogeneities of soils make the soil spectral different and complicated, and it is valuable to increase the accuracy of prediction models for soil organic matter（SOM） based on pre-classification. This experiment was conducted under a controllable environment, and different soil samples from northeast of China were measured using ASD2500 hyperspectral instrument. The results showed that there are different reflectances in different soil types. There are statistically significant correlation between SOM and reflectence at 0.05 and 0.01 levels in 550–850 nm, and all soil types get significant at 0.01 level in 650–750 nm. The results indicated that soil types of the northeast can be divided into three categories： The first category shows relatively flat and low reflectance in the entire band; the second shows that the spectral reflectance curve raises fastest in 460–610 nm band, the sharp increase in the slope, but uneven slope changes; the third category slowly uplifts in the visible band, and its slope in the visible band is obviously higher than the first category. Except for the classification by curve shapes of reflectance, principal component analysis is one more effective method to classify soil types. The first principal component includes 62.13–97.19% of spectral information and it mainly relates to the information in 560–600, 630–690 and 690–760 nm. The second mainly represents spectral information in 1 640–1 740, 2 050–2 120 and 2 200–2 300 nm. The samples with high OM are often in the left, and the others with low OM are in the right of the scatter plot（the first principal component is the horizontal axis and the second is the longitudinal axis）. Soil types in northeast of China can be classified effectively by those two principles; it is also a valuable reference to other soil in other areas.

Eco-geochemical Characteristics of Muskmelon Root Soil in Planting Region of Hetao Irrigation Area of Inner Mongolia

[Objective] The aim was to evaluate the eco-geochemical characteristics and geochemistry conditions of root soil in muskmelon planting area, evaluate the soil environment quality in Hetao irrigation area and provide scientific basis for the musmelon planting in this area. [Method] Root system soil sample and plow pan sample were collected from the main muskmelon planting area in Hetao irrigation area, so as to analyze the contents of heavy metal elements. By comparing with the Soft Environmental Quality Standard （GB15618-1995）, the research explored whether the heavy metal elements in root system met the national standard. [Result] Heavy metal elements in root system soil had the maximum content in recession area of Yellow River, followed by saline soils. The content of heavy metal elements in chestnut-brown soil was the minimum. Harmful elements As, Cd, Hg, F and Pb in anthropogenic-alluvial soil of Hetao irrigation area showed enrichment characteristics in earth surface, with zonality vertically. Trace elements Cu and Zn, and beneficial elements P, K20, CaO, MgO and Se showed depletion. In anthropogenic-aUuvial soil of Ulansuhai of the Yellow River, harmful elements As and Cd showed significant enrichment in root system soil, while other elements showed depletion or was close to background value. In soil of plow pan, both beneficial component and harmful component showed enrichment characteristics. [Conclusion] Hetao irrigation area has the ideal geochemical conditions and heavy metal elements in muskmelon area meet the national standards.

Dynamic Variation Characteristics of Phosphorus in Paddy Field Runoff in Saline Land and Its Potential Environmental Effect

[Objective] The aim was to study the dynamic variation characteristics of phosphorus in paddy field runoff in saline land and its potential environmental effect. [Method] Taking Qianguo irrigation district in soda-saline land in Songnen Plain as study object, the dynamic variation law of phosphorus in paddy field runoff under different irrigation conditions and its potential environmental effect were discussed. [Result] Surface water in paddy field was alkaline, and scattered soil had poor fertilizer conservation capacity. Phosphorus accumulated in soil surface, which could increase the risk of phosphorus loss. Phosphorus loss in paddy field mainly occurred in irrigation period and runoff period caused by rainstorm. The concentration of total phosphorus (TP), particulate phosphorus (PP), total dissolved phosphorus (TDP) and dissolved reactive phosphorus (DRP) in paddy field runoff decreased with time, especially PP. Phosphorus concentration exceeded critical value and resulted to eutrophication, which threatened the water quality security of Chagan Lake. Phosphorus concentration in water recession canal increased with time, and eutrophication with different degrees appeared under high temperature. TP concentration in surface water of paddy field was highly negatively correlated with that in water recession canal, and the correlation coefficients R2(α=0.05)in three paddy fields were 0.850 9, 0.896 4 and 0.915 3, respectively. The pollution load of phosphorus in paddy field with the best irrigation condition was higher, and its pollution risk was the highest. Thus, such fields should be monitored and controlled mainly as the critical source area of phosphorus loss. [Conclusion] The study could provide theoretical foundations for developing saline land rationally, establishing optimal management measure of phosphorus in saline land and controlling phosphorus loss from farmland to protect local water resources.

Derivation of salt content in salinized soil from hyperspectral reflectance data: A case study at Minqin Oasis, Northwest China

Soil salinization is a serious ecological and environmental problem because it adversely affects sustainable development worldwide, especially in arid and semi-arid regions. It is crucial and urgent that advanced technologies are used to efficiently and accurately assess the status of salinization processes. Case studies to determine the relations between particular types of salinization and their spectral reflectances are essential because of the distinctive characteristics of the reflectance spectra of particular salts. During April 2015 we collected surface soil samples(0–10 cm depth) at 64 field sites in the downstream area of Minqin Oasis in Northwest China, an area that is undergoing serious salinization. We developed a linear model for determination of salt content in soil from hyperspectral data as follows. First, we undertook chemical analysis of the soil samples to determine their soluble salt contents. We then measured the reflectance spectra of the soil samples, which we post-processed using a continuum-removed reflectance algorithm to enhance the absorption features and better discriminate subtle differences in spectral features. We applied a normalized difference salinity index to the continuum-removed hyperspectral data to obtain all possible waveband pairs. Correlation of the indices obtained for all of the waveband pairs with the wavebands corresponding to measured soil salinities showed that two wavebands centred at wavelengths of 1358 and 2382 nm had the highest sensitivity to salinity. We then applied the linear regression modelling to the data from half of the soil samples to develop a soil salinity index for the relationships between wavebands and laboratory measured soluble salt content. We used the hyperspectral data from the remaining samples to validate the model. The salt content in soil from Minqin Oasis were well produced by the model. Our results indicate that wavelengths at 1358 and 2382 nm are the optimal wavebands for monitoring the concentrations of chlorine and sulphate compounds, the predominant salts at Minqin Oasis. Our modelling provides a reference for future case studies on the use of hyperspectral data for predictive quantitative estimation of salt content in soils in arid regions. Further research is warranted on the application of this method to remotely sensed hyperspectral data to investigate its potential use for large-scale mapping of the extent and severity of soil salinity.

Variation of soil physical-chemical characteristics in salt-affected soil in the Qarhan Salt Lake,Qaidam Basin

Soil salinization has adverse effects on the soil physical-chemical characteristics.However,little is known about the changes in soil salt ion concentrations and other soil physical-chemical characteristics within the Qarhan Salt Lake and at different soil depths in the surrounding areas.Here,we selected five sampling sites(S1,S2,S3,S4,and S5)alongside the Qarhan Salt Lake and in the Xidatan segment of the Kunlun Mountains to investigate the relationship among soil salt ion concentrations,soil physical-chemical characteristics,and environmental variables in April 2019.The results indicated that most sites had strongly saline and very strongly saline conditions.The main salt ions present in the soil were Na^(+),K^(+),and Cl^(-).Soil nutrients and soil microbial biomass(SMB)were significantly affected by the salinity(P<0.05).Moreover,soil salt ions(Na^(+),K^(+),Ca2+,Mg^(2+),Cl^(-),CO_(3)^(2-),SO_(4)^(2-),and HCO_(3)^(-))were positively correlated with electrical conductivity(EC)and soil water content(SWC),but negatively related to altitude and soil depth.Unlike soil salt ions,soil nutrients and SMB were positively correlated with altitude,but negatively related to EC and SWC.Moreover,soil nutrients and SMB were negatively correlated with soil salt ions.In conclusion,soil nutrients and SMB were mainly influenced by salinity,and were related to altitude,soil depth,and SWC in the areas from the Qarhan Salt Lake to the Xidatan segment.These results imply that the soil quality(mainly evaluated by soil physical-chemical characteristics)is mainly influenced by soil salt ions in the areas surrounding the Qarhan Salt Lake.Our results provide an accurate prediction of how the soil salt ions,soil nutrients,and SMB respond to the changes along a salt gradient.The underlying mechanisms controlling the soil salt ion distribution,soil nutrients,and SMB in an extremely arid desert climate playa should be studied in greater detail in the future.

Three-dimensional fluorescence spectral characteristics of dissolved organic carbon in cave drip waters and their responses to environment changes: Four cave systems as an example in Guizhou Province, China

Understanding the responses of fluorescence spectral characteristics of cave drip waters to modern environment and climate changes is key to the reconstructions of environmental and climatic changes using fluorescence spectral characteristics of speleothems. The fluorescence spectral characteristics of dissolved organic carbon (DOC) in four active cave systems were analyzed with a three-dimensional (3D) fluorescence spectral analysis method. We found that the fluorescence types of DOC were mainly of fulvic-like and protein-like fluorescences, both in soil waters and cave drip waters. The intensity of fulvic-like fluorescence was positively correlated with the concentrations of DOC, suggesting that the DOC of cave drip waters was derived from the overlying soil layer of a cave system. Compared with the other cave systems, the variation range of the excitation and emission wavelengths for fulvic-like fluorescence of cave drip waters in Liangfeng cave system that had forest vegetation was smaller and the excitation wavelength was longer, while its fluorescence intensity varied significantly. By contrast, the excitation and emission wavelengths and fluorescence intensity for that in Jiangjun cave system that had a scrub and tussock vegetation showed the most significant variation, while its excitation wavelength was shorter. This implies that the variation of vegetation overlying a cave appears to be a factor affecting the fluorescence spectral characteristics of cave drip waters.

Quantitative retrieval of soil salt content based on measured spectral data

Choosing the Minqin Oasis, located downstream of the Shiyang River in Northwest China, as the study area, we used field-measured hyperspectral data and laboratory-measured soil salt content data to analyze the characteristics of saline soil spectral reflectance and its transformation in the area, and elucidated the relations between the soil spectral re-flectance, reflectance transformation, and soil salt content. In addition, we screened sensitive wavebands. Then, a multiple linear regression model was established to predict the soil salt content based on the measured spectral data, and the accuracy of the model was verified using field-measured salinity data. The results showed that the overall shapes of the spectral curves of soils with different degrees of salinity were consistent, and the reflectance in visible and near-infrared bands for salinized soil was higher than that for non-salinized soil. After differential transformation, the correlation coefficient between the spectral reflectance and soil salt content was obviously improved. The first-order differential transformation model based on the logarithm of the reciprocal of saline soil spectral reflectance produced the highest accuracy and stability in the bands at 462 and 636 nm; the determination coefficient was 0.603, and the root mean square error was 5.407. Thus, the proposed model provides a good reference for the quantitative extraction and monitoring of regional soil salinization.

Soil Salinity Detection in Semi-Arid Region Using Spectral Unmixing, Remote Sensing and Ground Truth Measurements

Soil salinity is one of the serious environmental problems ravaging the soils of arid and semi-arid region, thereby affecting crop productivity, livestock, increase level of poverty and land degradation. Hyperspectral remote sensing is one of the important techniques to monitor, analyze and estimate the extent and severity of soil salt at regional to local scale. In this study we develop a model for the detection of salt-affected soils in arid and semi-arid regions and in our case it’s Ghannouch, Gabes. We used fourteen spectral indices and six spectral bands extracted from the Hyperion data. Linear Spectral Unmixing technique (LSU) was used in this study to improve the correlation between electrical conductivity and spectral indices and then improve the prediction of soil salinity as well as the reliability of the model. To build the model a multiple linear regression analysis was applied using the best correlated indices. The standard error of the estimate is about 1.57 mS/cm. The results of this study show that hyperion data is accurate and suitable for differentiating between categories of salt affected soils. The generated model can be used for management strategies in the future.

Effects of confining pressure and temperature on strength and deformation behavior of frozen saline silty clay

Buildings are always affected by frost heave and thaw settlement in cold regions,even where saline soil is present.This paper describes the triaxial testing results of frozen silty clay with high salt content and examines the in-fluence of confining pressure and temperature on its mechanical characteristics.Conventional triaxial compression tests were conducted under different confining pressures(0.5–7.0 MPa)and temperatures(-6℃,-8℃,-10℃,and-12℃).The test results show that when the confining pressure is less than 1 MPa,the frozen saline silty clay is dominated by brittle behavior with the X-shaped dilatancy failure mode.As the confining pressure increases,the sample gradually transitions from brittle to plastic behavior.The strength of frozen saline silty clay rises first and then decreases with increasing confining pressure.The improved Duncan-Chang hyperbolic model can describe the stress-strain relationship of frozen saline silty clay.And the parabolic strength criterion can be used to describe the strength evolution of frozen saline silty clay.The function relation of strength parameters with temperature is obtained by fitting,and the results of the parabolic strength criterion are in good agreement with the experimental results,especially when confining pressure is less than 5 MPa.Therefore,the study has important guiding significance for design and construction when considering high salinity soil as an engineering material in cold regions.

Evolution of soil DOM during thermal remediation below 100℃:concentration,spectral characteristics and complexation ability

The impact of thermal remediation on soil function has drawn increasing attention.So far,as the most active fraction of soil organic matter,the evolution of dissolved organic matter(DOM)during the thermal remediation lacks in-depth investigation,especially for the temperatures value below 100℃.In this study,a series of soil thermal treatment experiments was conducted at 30,60,and 90℃ during a 90-d period,where soil DOM concentration increased with heating temperature and duration.The molecular weight,functional groups content and aromaticity of DOM all decreased during the thermal treatment.The excitation-emission matrices(EEM)results suggested that humic acid-like substances transformed into fulvic acid-like substances(FIII/FV increased from 0.27 to 0.44)during the heating process,and five DOM components were further identified by EEM-PARAFAC.The change of DOM structures and components indicated the decline of DOM stability and hydrophilicity,and can potentially change the bioavailability and mobility.Elevated temperature also resulted in the decline of DOM complexation ability,which may be caused by the loss of binding sites due to the decrease of polar function groups,aromatic structures and hydrophilic components.This study provides valuable information about the evolution of DOM during thermal remediation,which would potentially change the fate of metal ions and the effectiveness of the post-treatment technologies in the treated region.

Detecting soil salinity with arid fraction integrated index and salinity index in feature space using Landsat TM imagery

Modeling soil salinity in an arid salt-affected ecosystem is a difficult task when using remote sensing data because of the complicated soil context （vegetation cover, moisture, surface roughness, and organic matter） and the weak spectral features of salinized soil. Therefore, an index such as the salinity index （SI） that only uses soil spectra may not detect soil salinity effectively and quantitatively. The use of vegetation reflectance as an indirect indicator can avoid limitations associated with the direct use of soil reflectance. The normalized difference vegetation index （NDVI）, as the most common vegetation index, was found to be responsive to salinity but may not be available for retrieving sparse vegetation due to its sensitivity to background soil in arid areas. Therefore, the arid fraction integrated index （AFⅡ） was created as supported by the spectral mixture analysis （SMA）, which is more appropriate for analyzing variations in vegetation cover （particularly halophytes） than NDVI in the study area. Using soil and vegetation separately for detecting salinity perhaps is not feasible. Then, we developed a new and operational model, the soil salinity detecting model （SDM） that combines AFⅡ and SI to quantitatively estimate the salt content in the surface soil. SDMs, including SDM1 and SDM2, were constructed through analyzing the spatial characteristics of soils with different salinization degree by integrating AFⅡ and SI using a scatterplot. The SDMs were then compared to the combined spectral response index （COSRI） from field measurements with respect to the soil salt content. The results indicate that the SDM values are highly correlated with soil salinity, in contrast to the performance of COSRI. Strong exponential relationships were observed between soil salinity and SDMs （R2〉0.86, RMSE〈6.86） compared to COSRI （R2=0.71, RMSE=16.21）. These results suggest that the feature space related to biophysical properties combined with AFII and SI can effectively provide information on soil salinity.

Prediction of Soil Salinity Using Multivariate Statistical Techniques and Remote Sensing Tools

Soil salinity limits plant growth, reduces crop productivity and degrades soil. Multispectral data from Landsat TM are used to study saline soils in southern Tunisia. This study will explore the potential multivariate statistical analysis, such as principal component analysis (PCA) and cluster analysis to identify the most correlated spectral indices and rapidly predict salt affected soils. Sixty six soil samples were collected for ground truth data in the investigated region. A high correlation was found between electrical conductivity and the spectral indices from near infrared and short-wave infrared spectrum. Different spectral indices were used from spectral bands of Landsat data. Statistical correlation between ground measurements of Electrical Conductivity (EC), spectral indices and Landsat original bands showed that the near and short-wave infrared bands (band 4, band 5 and 7) and the salinity indices (SI 5 and SI 9) have the highest correlation with EC. The use of CA revealed a strong correlation between electrical conductivity EC and spectral indices such abs4, abs5, abs7 and si5. The principal components analysis is conducted by incorporating the reflectance bands and spectral salinity indices from the remote sensing data. The first principal component has large positive associations with bands from the visible domain and salinity indices derived from these bands, while second principal component is strongly correlated with spectral indices from NIR and SWIR. Overall, it was found that the electrical conductivity EC is highly correlated (R2 = -0.72) to the second principal component (PC2), but no correlation is observed between EC and the first principal component (PC1). This suggests that the second component can be used as an explanatory variable for predicting EC. Based on these results and combining the spectral indices (PC2 and abs B4) into a regression analysis, model yielded a relatively high coefficient of determination R2 = 0.62 and a low RMSE = 1.86 dS/m.

Comparative Study among Different Semi-Empirical Models for Soil Salinity Prediction in an Arid Environment Using OLI Landsat-8 Data

Salt-affected soils, caused by natural or human activities, are a common environmental hazard in semi-arid and arid landscapes. Excess salts in soils affect plant growth and production, soil and water quality and, therefore, increase soil erosion and land degradation. This research investigates the performance of five different semi-empirical predictive models for soil salinity spatial distribution mapping in arid environment using OLI sensor image data. This is the first attempt to test remote sensing based semi-empirical salinity predictive models in this area: the Kingdom of Bahrain. To achieve our objectives, OLI data were standardized from the atmosphere interferences, the sensor radiometric drift, and the topographic and geometric distortions. Then, the five semi-empirical predictive models based on the Normalized Difference Salinity Index (NDSI), the Salinity Index-ASTER (SI-ASTER), the Salinity Index-1 (SI-1), the Soil Salinity and Sodicity Index-1 and Index-2 (SSSI-1 and SSSI-2), developed for slight and moderate salinity in agricultural land, were implemented and applied to OLI image data. For validation purposes, a fieldwork was organized and different important spots-locations representing different salinity levels were visited, photographed, and localized using an accurate GPS (σ ≤ ±30 cm). Based on this a priori knowledge of the soil salinity, six validation sites were selected to reflect non-saline, low, moderate, high and extreme salinity classes, descriptive statistics extracted from polygons and/or transects over these sites were used. The obtained results showed that the models based on NDSI, SI-1 and SI-ASTER all failed to detect salinity bounds for both extreme salinity (Sabkhah) and non-saline conditions. In Fact, NDSI and SI-ASTER gave respectively only 35% dS/m and 25% dS/m in extreme salinity validation site, while SI-1 and SI-ASTER indicated 38% dS/m and 39% dS/m in non-saline validation site. Therefore, these three models were deemed inadequate for the study site. However, both SSSI-1 and SSSI-2 allowed a detection of the previous salinity bounds and furthermore described similarly and correctly the urban-vegetation areas and the open-land areas. Their predicted EC is around 10% dS/m for non-saline urban soil, about 25% dS/m for low salinity urban-vegetation soil, approximately 30% to 75% dS/m, respectively, for moderate to high salinity soils. SSSI-2 based semi-empirical salinity models was able to differentiate the high salinity versus extreme salinity in areas where both exist and was very accurate to highlight the pure salt where SSSI-1 has reach saturation for both salinity classes. In conclusion, reliable salinity map was produced using the model based on SSSI-2 and OLI sensor data that allows a better characterization of the soil salinity problem in an Arid Environment.

Prediction of Soil Salinity Using Remote Sensing Tools and Linear Regression Model

Soil salinity is one of the most damaging environmental problems worldwide, especially in arid and semi-arid regions. Multispectral data Sentinel_2 are used to study saline soils in southern Tunisia. 34 soil samples were collected for ground truth data in the investigated region. A moderate correlation was found between electrical conductivity and the spectral indices from SWIR. Different spectral indices were used from original bands of Sentinel_2 data. Statistical correlation between ground measurements of Electrical Conductivity (EC), spectral indices and Sentinel_2 original bands showed that SWIR bands (b11 and b12) and the salinity index SI have the highest correlation with EC. Based on these results and combining these remotely sensed variables into a regression analysis model yielded a coefficient of determination R2 = 0.48 and an RMSE = 4.8 dS/m.

Salt-Affected Soil Mapping in an Arid Environment Using Semi-Empirical Model and Landsat-OLI Data

The aim of this research is to map the salt-affected soil in an arid environment using an advanced semi-empirical predictive model, Operational Land Imager (OLI) data, a digital elevation model (DEM), field soil sampling, and laboratory and statistical analyses. To achieve our objectives, the OLI data were atmospherically corrected, radiometric sensor drift was calibrated, and distortions of topography and geometry were corrected using a DEM. Then, the soil salinity map was derived using a semi-empirical predictive model based on the Soil Salinity and Sodicity Index-2 (SSSI-2). The vegetation cover map was extracted from the Transformed Difference Vegetation Index (TDVI). In addition, accurate DEM of 5-m pixels was used to derive topographic attributes (elevation and slope). Visual comparisons and statistical validation of the semi-empirical model using ground truth were undertaken in order to test its capability in an arid environment for moderate and strong salinity mapping. To accomplish this step, fieldwork was organized and 120 soil samples were collected with various degrees of salinity, including non-saline soil samples. Each one was automatically labeled using a digital camera and an accurate global positioning system (GPS) survey (σ ≤ ± 30 cm) connected in real time to the geographic information system (GIS) database. Subsequently, in the laboratory, the major exchangeable cations (Ca2+, Mg2+, Na+, K+, Cl- and SO42-), pH and the electrical conductivity (EC-Lab) were extracted from a saturated soil paste, as well as the sodium adsorption ratio (SAR) being calculated. The EC-Lab, which is generally accepted as the most effective method for soil salinity quantification was used for statistical analysis and validation purposes. The obtained results demonstrated a very good conformity between the derived soil salinity map from OLI data and the ground truth, highlighting six major salinity classes: Extreme, very high, high, moderate, low and non-saline. The laboratory chemical analyses corroborate these results. Furthermore, the semi-empirical predictive model provides good global results in comparison to the ground truth and laboratory analysis (EC-Lab), with correlation coefficient (R2) of 0.97, an index of agreement (D) of 0.84 (p < 0.05), and low overall root mean square error (RMSE) of 11%. Moreover, we found that topographic attributes have a substantial impact on the spatial distribution of salinity. The areas at a relatively high altitude and with hard bedrock are less susceptible to salinity, while areas at a low altitude and slope (≤2%) composed of Quaternary soil are prone to it. In these low areas, the water table is very close to the surface (≤1 m), and the absence of an adequate drainage network contributes significantly to waterlogging. Consequently, the intrusion and emergence of seawater at the surface, coupled with high temperature and high evaporation rates, contribute extensively to the soil salinity in the study area.

不同改良剂对土壤盐渍化特性及燕麦生物排盐作用的影响

为探讨不同土壤改良剂在旱区盐渍土上的应用效果,通过单因素随机区组试验,以不施改良剂(T0)为对照,探究粉煤灰(T1)、生物炭(T2)、普通有机肥(T3)、糠醛渣(T4)、蚯蚓粪(T5)、腐殖酸(T6)六种改良剂单施以及分别使用单施用量的1/2进行不同改良剂粉煤灰+有机肥(T7)、粉煤灰+糠醛渣(T8)、生物炭+糠醛渣(T9)、蚯蚓粪+腐殖酸(T10)配施对土壤盐渍化特性和燕麦盐分积累的影响。结果表明,与T0处理相比,T1~T10处理下土壤Na^(+)、HCO_(3)^(-)、Cl^(-)和SO_(4)^(2-)含量分别降低37.18%~67.83%、35.25%~78.10%、45.16%~69.51%和6.60%~13.94%,K^(+)、Ca^(2+)和Mg^(2+)含量均增加;土壤全盐含量和钠吸附比(SAR)分别降低8.41%~15.97%和41.94%~69.70%;施用改良剂后土壤的pH值不同程度降低,T1、T2处理因改良剂自身的碱性导致土壤pH值降低效果不显著,T3~T10处理的土壤pH值较T0处理降低了5.09%~8.61%。改良剂配施对土壤Na^(+)、HCO_(3)^(-)、Cl^(-)、SO_(4)^(2-)含量、全盐含量及SAR、pH值的降低程度均优于单施,其中T7处理的降低效果最好。施用改良剂提高了燕麦的干草产量、地上部分的含盐量和盐分积累量。除T6处理外其他处理的燕麦盐含量较T0处理增加了1.43%~3.52%;T1~T10处理的燕麦盐分积累量增加了14.92%~34.64%,且盐分积累量增加幅度亦表现为配施大于单施。施用改良剂加速了耕层土壤的脱盐,T1~T10处理的脱盐率是T0处理的5~9倍,T7处理的脱盐率较T1~T5处理提高了4.99%~7.41%。综合来看,供试改良剂的单施及四种配施均具有降低试区盐渍土耕层土壤的盐度和碱化度的作用,且提升了燕麦的生物排盐作用;以粉煤灰与普通有机肥配施对耕层土壤盐渍化程度的降低效果最好。

基于GF-1数据的耕地土壤镉(Cd)含量遥感估算方法

本文采用多种光谱变换和回归分析方法探索了使用GF-1卫星影像监测耕地土壤镉(Cd)含量的可行性。首先针对获取的GF-1原始影像数据,在完成预处理及剔除植被信息后进行倒对数、平方根和反正弦平方根变换,生成4套光谱影像;然后分别用采样点5 m缓冲区内各套影像光谱统计值与Cd含量进行相关性分析和多种回归分析。选择模型决定系数最高(>95%)的反正弦平方根变换后的自适应重加权回归方法构建的线性回归模型作为遥感估算模型。遥感估算结果在稻田积水、边缘地带等出现了异常估算值;笔者分析原因后应用线性插值的方法得到最终估算结果。相关性分析和建模精度表明该方法是可行的,有望应用于实际土壤质量监测和土地管理中。

基于高光谱的景电灌区盐渍化土壤水、盐估算研究

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